Object Detection(目標檢測神文)
目標檢測神文，非常全而且持續在更新。轉發自：https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html，如有侵權聯繫刪除。
我會跟進原作者博客持續更新，加入自己對目標檢測領域的一些新研究及論文解讀。博客根據需求直接進行關鍵字搜索，例如2018，可找到最新論文。
- Papers
- ———————————————————————————————————-
- R-CNN
- Fast R-CNN
- Faster R-CNN
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- R-CNN minus R
- Faster R-CNN in MXNet with distributed implementation and data parallelization
- Contextual Priming and Feedback for Faster R-CNN
- An Implementation of Faster RCNN with Study for Region Sampling
- Interpretable R-CNN
- Light-Head R-CNN
- Cascade R-CNN
- MultiBox
- SPP-Net
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
- Object Detectors Emerge in Deep Scene CNNs
- segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
- Object Detection Networks on Convolutional Feature Maps
- Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
- DeepBox: Learning Objectness with Convolutional Networks
- MR-CNN
- YOLO
- You Only Look Once: Unified, Real-Time Object Detection
- darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
- Start Training YOLO with Our Own Data
- YOLO: Core ML versus MPSNNGraph
- TensorFlow YOLO object detection on Android
- Computer Vision in iOS – Object Detection
- YOLOv2
- YOLOv3
- DenseBox
- SSD
- DSSD
- FSSD
- ESSD
- Inside-Outside Net (ION)
- Factors in Finetuning Deep Model for object detection
- CRAFT
- OHEM
- R-FCN
- MS-CNN
- PVANET
- GBD-Net
- Gated Bi-directional CNN for Object Detection
- Crafting GBD-Net for Object Detection
- StuffNet: Using ‘Stuff’ to Improve Object Detection
- Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene
- Hierarchical Object Detection with Deep Reinforcement Learning
- Learning to detect and localize many objects from few examples
- Speed/accuracy trade-offs for modern convolutional object detectors
- SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
- Feature Pyramid Network (FPN)
- Feature Pyramid Networks for Object Detection
- Action-Driven Object Detection with Top-Down Visual Attentions
- Beyond Skip Connections: Top-Down Modulation for Object Detection
- Wide-Residual-Inception Networks for Real-time Object Detection
- Attentional Network for Visual Object Detection
- Learning Chained Deep Features and Classifiers for Cascade in Object Detection
- DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
- Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
- Spatial Memory for Context Reasoning in Object Detection
- Accurate Single Stage Detector Using Recurrent Rolling Convolution
- Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
- LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
- Point Linking Network for Object Detection
- Perceptual Generative Adversarial Networks for Small Object Detection
- Few-shot Object Detection
- Yes-Net: An effective Detector Based on Global Information
- SMC Faster R-CNN: Toward a scene-specialized multi-object detector
- Towards lightweight convolutional neural networks for object detection
- RON: Reverse Connection with Objectness Prior Networks for Object Detection
- Mimicking Very Efficient Network for Object Detection
- Residual Features and Unified Prediction Network for Single Stage Detection
- Deformable Part-based Fully Convolutional Network for Object Detection
- Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
- Recurrent Scale Approximation for Object Detection in CNN
- DSOD
- RetinaNet
- Focal Loss for Dense Object Detection
- Focal Loss Dense Detector for Vehicle Surveillance
- CoupleNet: Coupling Global Structure with Local Parts for Object Detection
- Incremental Learning of Object Detectors without Catastrophic Forgetting
- Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
- StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
- Dynamic Zoom-in Network for Fast Object Detection in Large Images
- Zero-Annotation Object Detection with Web Knowledge Transfer
- MegDet
- MegDet: A Large Mini-Batch Object Detector
- Single-Shot Refinement Neural Network for Object Detection
- Receptive Field Block Net for Accurate and Fast Object Detection
- An Analysis of Scale Invariance in Object Detection - SNIP
- Feature Selective Networks for Object Detection
- Learning a Rotation Invariant Detector with Rotatable Bounding Box
- Scalable Object Detection for Stylized Objects
- Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
- Deep Regionlets for Object Detection
- Training and Testing Object Detectors with Virtual Images
- Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
- Spot the Difference by Object Detection
- Localization-Aware Active Learning for Object Detection
- Object Detection with Mask-based Feature Encoding
- LSTD: A Low-Shot Transfer Detector for Object Detection
- Domain Adaptive Faster R-CNN for Object Detection in the Wild
- Pseudo Mask Augmented Object Detection
- Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
- Learning Region Features for Object Detection
- Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
- Object Detection for Comics using Manga109 Annotations
- Task-Driven Super Resolution: Object Detection in Low-resolution Images
- Transferring Common-Sense Knowledge for Object Detection
- Multi-scale Location-aware Kernel Representation for Object Detection
- Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
- DetNet: A Backbone network for Object Detection
- Robust Physical Adversarial Attack on Faster R-CNN Object Detector
- AdvDetPatch: Attacking Object Detectors with Adversarial Patches
- Physical Adversarial Examples for Object Detectors
- Quantization Mimic: Towards Very Tiny CNN for Object Detection
- Object detection at 200 Frames Per Second
- Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images
- SNIPER: Efficient Multi-Scale Training
- Soft Sampling for Robust Object Detection
- MetaAnchor: Learning to Detect Objects with Customized Anchors
- Localization Recall Precision (LRP): A New Performance Metric for Object Detection
- Auto-Context R-CNN
- Pooling Pyramid Network for Object Detection
- Modeling Visual Context is Key to Augmenting Object Detection Datasets
- Dual Refinement Network for Single-Shot Object Detection
- Acquisition of Localization Confidence for Accurate Object Detection
- CornerNet: Detecting Objects as Paired Keypoints
- Unsupervised Hard Example Mining from Videos for Improved Object Detection
- SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection
- A Survey of Modern Object Detection Literature using Deep Learning
- Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
- Deep Feature Pyramid Reconfiguration for Object Detection
- Non-Maximum Suppression (NMS)
- Adversarial Examples
- Weakly Supervised Object Detection
- Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
- Weakly supervised object detection using pseudo-strong labels
- Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
- Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
- Video Object Detection
- Learning Object Class Detectors from Weakly Annotated Video
- Analysing domain shift factors between videos and images for object detection
- Video Object Recognition
- Deep Learning for Saliency Prediction in Natural Video
- T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
- Object Detection from Video Tubelets with Convolutional Neural Networks
- Object Detection in Videos with Tubelets and Multi-context Cues
- Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
- CNN Based Object Detection in Large Video Images
- Object Detection in Videos with Tubelet Proposal Networks
- Flow-Guided Feature Aggregation for Video Object Detection
- Video Object Detection using Faster R-CNN
- Improving Context Modeling for Video Object Detection and Tracking
- Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
- Mobile Video Object Detection with Temporally-Aware Feature Maps
- Towards High Performance Video Object Detection
- Impression Network for Video Object Detection
- Spatial-Temporal Memory Networks for Video Object Detection
- 3D-DETNet: a Single Stage Video-Based Vehicle Detector
- Object Detection in Videos by Short and Long Range Object Linking
- Object Detection in Video with Spatiotemporal Sampling Networks
- Towards High Performance Video Object Detection for Mobiles
- Optimizing Video Object Detection via a Scale-Time Lattice
- Object Detection on Mobile Devices
- Object Detection in 3D
- Object Detection on RGB-D
- Zero-Shot Object Detection
- Salient Object Detection
- Best Deep Saliency Detection Models (CVPR 2016 & 2015)
- Large-scale optimization of hierarchical features for saliency prediction in natural images
- Predicting Eye Fixations using Convolutional Neural Networks
- Saliency Detection by Multi-Context Deep Learning
- DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
- SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection
- Shallow and Deep Convolutional Networks for Saliency Prediction
- Recurrent Attentional Networks for Saliency Detection
- Two-Stream Convolutional Networks for Dynamic Saliency Prediction
- Unconstrained Salient Object Detection
- Unconstrained Salient Object Detection via Proposal Subset Optimization
- DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
- Salient Object Subitizing
- Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection
- Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs
- Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
- A Deep Multi-Level Network for Saliency Prediction
- Visual Saliency Detection Based on Multiscale Deep CNN Features
- A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
- Deeply supervised salient object detection with short connections
- Weakly Supervised Top-down Salient Object Detection
- SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
- Visual Saliency Prediction Using a Mixture of Deep Neural Networks
- A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network
- Saliency Detection by Forward and Backward Cues in Deep-CNNs
- Supervised Adversarial Networks for Image Saliency Detection
- Group-wise Deep Co-saliency Detection
- Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
- Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
- Learning Uncertain Convolutional Features for Accurate Saliency Detection
- Deep Edge-Aware Saliency Detection
- Self-explanatory Deep Salient Object Detection
- PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection
- DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets
- Recurrently Aggregating Deep Features for Salient Object Detection
- Deep saliency: What is learnt by a deep network about saliency?
- Contrast-Oriented Deep Neural Networks for Salient Object Detection
- Salient Object Detection by Lossless Feature Reflection
- HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection
- Video Saliency Detection
- Visual Relationship Detection
- Visual Relationship Detection with Language Priors
- ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection
- Visual Translation Embedding Network for Visual Relation Detection
- Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection
- Detecting Visual Relationships with Deep Relational Networks
- Identifying Spatial Relations in Images using Convolutional Neural Networks
- PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
- Natural Language Guided Visual Relationship Detection
- Detecting Visual Relationships Using Box Attention
- Google AI Open Images - Visual Relationship Track
- Face Deteciton
- Multi-view Face Detection Using Deep Convolutional Neural Networks
- From Facial Parts Responses to Face Detection: A Deep Learning Approach
- Compact Convolutional Neural Network Cascade for Face Detection
- Face Detection with End-to-End Integration of a ConvNet and a 3D Model
- CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
- Towards a Deep Learning Framework for Unconstrained Face Detection
- Supervised Transformer Network for Efficient Face Detection
- UnitBox: An Advanced Object Detection Network
- Bootstrapping Face Detection with Hard Negative Examples
- Grid Loss: Detecting Occluded Faces
- A Multi-Scale Cascade Fully Convolutional Network Face Detector
- MTCNN
- Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks
- Face Detection using Deep Learning: An Improved Faster RCNN Approach
- Faceness-Net: Face Detection through Deep Facial Part Responses
- Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained 「Hard Faces」
- End-To-End Face Detection and Recognition
- Face R-CNN
- Face Detection through Scale-Friendly Deep Convolutional Networks
- Scale-Aware Face Detection
- Detecting Faces Using Inside Cascaded Contextual CNN
- Multi-Branch Fully Convolutional Network for Face Detection
- SSH: Single Stage Headless Face Detector
- Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container
- FaceBoxes: A CPU Real-time Face Detector with High Accuracy
- S3FD: Single Shot Scale-invariant Face Detector
- Detecting Faces Using Region-based Fully Convolutional Networks
- AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
- Face Attention Network: An effective Face Detector for the Occluded Faces
- Feature Agglomeration Networks for Single Stage Face Detection
- Face Detection Using Improved Faster RCNN
- PyramidBox: A Context-assisted Single Shot Face Detector
- A Fast Face Detection Method via Convolutional Neural Network
- Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy
- Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
- SFace: An Efficient Network for Face Detection in Large Scale Variations
- Survey of Face Detection on Low-quality Images
- Anchor Cascade for Efficient Face Detection
- Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization
- Detect Small Faces
- Person Head Detection
- Pedestrian Detection / People Detection
- Pedestrian Detection aided by Deep Learning Semantic Tasks
- Deep Learning Strong Parts for Pedestrian Detection
- Taking a Deeper Look at Pedestrians
- Convolutional Channel Features
- End-to-end people detection in crowded scenes
- Learning Complexity-Aware Cascades for Deep Pedestrian Detection
- Deep convolutional neural networks for pedestrian detection
- Scale-aware Fast R-CNN for Pedestrian Detection
- New algorithm improves speed and accuracy of pedestrian detection
- Pushing the Limits of Deep CNNs for Pedestrian Detection
- A Real-Time Deep Learning Pedestrian Detector for Robot Navigation
- A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation
- Is Faster R-CNN Doing Well for Pedestrian Detection?
- Unsupervised Deep Domain Adaptation for Pedestrian Detection
- Reduced Memory Region Based Deep Convolutional Neural Network Detection
- Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
- Detecting People in Artwork with CNNs
- Multispectral Deep Neural Networks for Pedestrian Detection
- Deep Multi-camera People Detection
- Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
- What Can Help Pedestrian Detection?
- Illuminating Pedestrians via Simultaneous Detection & Segmentation
- Rotational Rectification Network for Robust Pedestrian Detection
- STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos
- Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy
- Repulsion Loss: Detecting Pedestrians in a Crowd
- Aggregated Channels Network for Real-Time Pedestrian Detection
- Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
- Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection
- Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
- PCN: Part and Context Information for Pedestrian Detection with CNNs
- Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
- Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
- Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
- Vehicle Detection
- Traffic-Sign Detection
- Traffic-Sign Detection and Classification in the Wild
- Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data
- Detecting Small Signs from Large Images
- Localized Traffic Sign Detection with Multi-scale Deconvolution Networks
- Detecting Traffic Lights by Single Shot Detection
- A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
- Skeleton Detection
- Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
- DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
- SRN: Side-output Residual Network for Object Symmetry Detection in the Wild
- Hi-Fi: Hierarchical Feature Integration for Skeleton Detection
- Fruit Detection
- Shadow Detection
- Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
- A+D-Net: Shadow Detection with Adversarial Shadow Attenuation
- Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal
- Direction-aware Spatial Context Features for Shadow Detection
- Direction-aware Spatial Context Features for Shadow Detection and Removal
- Others Detection
- Deep Deformation Network for Object Landmark Localization
- Fashion Landmark Detection in the Wild
- Deep Learning for Fast and Accurate Fashion Item Detection
- OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as 「OSM-Crosswalk-Detection」)
- Selfie Detection by Synergy-Constraint Based Convolutional Neural Network
- Associative Embedding:End-to-End Learning for Joint Detection and Grouping
- Deep Cuboid Detection: Beyond 2D Bounding Boxes
- Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
- Deep Learning Logo Detection with Data Expansion by Synthesising Context
- Scalable Deep Learning Logo Detection
- Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks
- Automatic Handgun Detection Alarm in Videos Using Deep Learning
- Objects as context for part detection
- Using Deep Networks for Drone Detection
- Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
- Target Driven Instance Detection
- DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion
- VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
- Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants
- ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos
- Deep Learning Object Detection Methods for Ecological Camera Trap Data
- EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
- Towards End-to-End Lane Detection: an Instance Segmentation Approach
- iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
- Object Proposal
- DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
- Scale-aware Pixel-wise Object Proposal Networks
- Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
- Learning to Segment Object Proposals via Recursive Neural Networks
- Learning Detection with Diverse Proposals
- ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
- Improving Small Object Proposals for Company Logo Detection
- Open Logo Detection Challenge
- Localization
- Beyond Bounding Boxes: Precise Localization of Objects in Images
- Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
- Weakly Supervised Object Localization Using Size Estimates
- Active Object Localization with Deep Reinforcement Learning
- Localizing objects using referring expressions
- LocNet: Improving Localization Accuracy for Object Detection
- Learning Deep Features for Discriminative Localization
- ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
- Ensemble of Part Detectors for Simultaneous Classification and Localization
- STNet: Selective Tuning of Convolutional Networks for Object Localization
- Soft Proposal Networks for Weakly Supervised Object Localization
- Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
- Tutorials / Talks
- Projects
- Detectron
- TensorBox: a simple framework for training neural networks to detect objects in images
- Object detection in torch: Implementation of some object detection frameworks in torch
- Using DIGITS to train an Object Detection network
- FCN-MultiBox Detector
- KittiBox: A car detection model implemented in Tensorflow.
- Deformable Convolutional Networks + MST + Soft-NMS
- How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow
- Metrics for object detection
- Leaderboard
- Tools
- Blogs
- Convolutional Neural Networks for Object Detection
- Introducing automatic object detection to visual search (Pinterest)
- Deep Learning for Object Detection with DIGITS
- Analyzing The Papers Behind Facebook’s Computer Vision Approach
- Easily Create High Quality Object Detectors with Deep Learning
- How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit
- Object Detection in Satellite Imagery, a Low Overhead Approach
- You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks
- Faster R-CNN Pedestrian and Car Detection
- Small U-Net for vehicle detection
- Region of interest pooling explained
- Supercharge your Computer Vision models with the TensorFlow Object Detection API
- Understanding SSD MultiBox — Real-Time Object Detection In Deep Learning
- One-shot object detection
- An overview of object detection: one-stage methods
| Method | backbone | test size | VOC2007 | VOC2010 | VOC2012 | ILSVRC 2013 | MSCOCO 2015 | Speed |
|---|---|---|---|---|---|---|---|---|
| OverFeat | 24.3% | |||||||
| R-CNN | AlexNet | 58.5% | 53.7% | 53.3% | 31.4% | |||
| R-CNN | VGG17 | 66.0% | ||||||
| SPP_net | ZF-5 | 54.2% | 31.84% | |||||
| DeepID-Net | 64.1% | 50.3% | ||||||
| NoC | 73.3% | 68.8% | ||||||
| Fast-RCNN | VGG16 | 70.0% | 68.8% | 68.4% | 19.7%(@[0.5-0.95]), 35.9%(@0.5) | |||
| MR-CNN | 78.2% | 73.9% | ||||||
| Faster-RCNN | VGG16 | 78.8% | 75.9% | 21.9%(@[0.5-0.95]), 42.7%(@0.5) | 198ms | |||
| Faster-RCNN | ResNet101 | 85.6% | 83.8% | 37.4%(@[0.5-0.95]), 59.0%(@0.5) | ||||
| YOLO | 63.4% | 57.9% | 45 fps | |||||
| YOLO | VGG-16 | 66.4% | 21 fps | |||||
| YOLOv2 | 448x448 | 78.6% | 73.4% | 21.6%(@[0.5-0.95]), 44.0%(@0.5) | 40 fps | |||
| SSD | VGG16 | 300x300 | 77.2% | 75.8% | 25.1%(@[0.5-0.95]), 43.1%(@0.5) | 46 fps | ||
| SSD | VGG16 | 512x512 | 79.8% | 78.5% | 28.8%(@[0.5-0.95]), 48.5%(@0.5) | 19 fps | ||
| SSD | ResNet101 | 300x300 | 28.0%(@[0.5-0.95]) | 16 fps | ||||
| SSD | ResNet101 | 512x512 | 31.2%(@[0.5-0.95]) | 8 fps | ||||
| DSSD | ResNet101 | 300x300 | 28.0%(@[0.5-0.95]) | 8 fps | ||||
| DSSD | ResNet101 | 500x500 | 33.2%(@[0.5-0.95]) | 6 fps | ||||
| ION | 79.2% | 76.4% | ||||||
| CRAFT | 75.7% | 71.3% | 48.5% | |||||
| OHEM | 78.9% | 76.3% | 25.5%(@[0.5-0.95]), 45.9%(@0.5) | |||||
| R-FCN | ResNet50 | 77.4% | 0.12sec(K40), 0.09sec(TitianX) | |||||
| R-FCN | ResNet101 | 79.5% | 0.17sec(K40), 0.12sec(TitianX) | |||||
| R-FCN(ms train) | ResNet101 | 83.6% | 82.0% | 31.5%(@[0.5-0.95]), 53.2%(@0.5) | ||||
| PVANet 9.0 | 84.9% | 84.2% | 750ms(CPU), 46ms(TitianX) | |||||
| RetinaNet | ResNet101-FPN | |||||||
| Light-Head R-CNN | Xception* | 800/1200 | 31.5%@[0.5:0.95] | 95 fps | ||||
| Light-Head R-CNN | Xception* | 700/1100 | 30.7%@[0.5:0.95] | 102 fps |
Papers
———————————————————————————————————-
Deep Neural Networks for Object Detection
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
- arxiv: http://arxiv.org/abs/1312.6229
- github: https://github.com/sermanet/OverFeat
- code: http://cilvr.nyu.edu/doku.php?id=software:overfeat:start
R-CNN
Rich feature hierarchies for accurate object detection and semantic segmentation
- intro: R-CNN
- arxiv: http://arxiv.org/abs/1311.2524
- supp: http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf
- slides: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
- slides: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
- github: https://github.com/rbgirshick/rcnn
- notes: http://zhangliliang.com/2014/07/23/paper-note-rcnn/
- caffe-pr(「Make R-CNN the Caffe detection example」): https://github.com/BVLC/caffe/pull/482
Fast R-CNN
Fast R-CNN
- arxiv: http://arxiv.org/abs/1504.08083
- slides: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
- github: https://github.com/rbgirshick/fast-rcnn
- github(COCO-branch): https://github.com/rbgirshick/fast-rcnn/tree/coco
- webcam demo: https://github.com/rbgirshick/fast-rcnn/pull/29
- notes: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
- notes: http://blog.csdn.net/linj_m/article/details/48930179
- github(「Fast R-CNN in MXNet」): https://github.com/precedenceguo/mx-rcnn
- github: https://github.com/mahyarnajibi/fast-rcnn-torch
- github: https://github.com/apple2373/chainer-simple-fast-rnn
- github: https://github.com/zplizzi/tensorflow-fast-rcnn
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1704.03414
- paper: http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf
- github(Caffe): https://github.com/xiaolonw/adversarial-frcnn
Faster R-CNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- intro: NIPS 2015
- arxiv: http://arxiv.org/abs/1506.01497
- gitxiv: http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
- slides: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
- github(official, Matlab): https://github.com/ShaoqingRen/faster_rcnn
- github: https://github.com/rbgirshick/py-faster-rcnn
- github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn
- github: https://github.com//jwyang/faster-rcnn.pytorch
- github: https://github.com/mitmul/chainer-faster-rcnn
- github: https://github.com/andreaskoepf/faster-rcnn.torch
- github: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
- github: https://github.com/smallcorgi/Faster-RCNN_TF
- github: https://github.com/CharlesShang/TFFRCNN
- github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
- github: https://github.com/yhenon/keras-frcnn
- github: https://github.com/Eniac-Xie/faster-rcnn-resnet
- github(C++): https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev
R-CNN minus R
- intro: BMVC 2015
- arxiv: http://arxiv.org/abs/1506.06981
Faster R-CNN in MXNet with distributed implementation and data parallelization
Contextual Priming and Feedback for Faster R-CNN
- intro: ECCV 2016. Carnegie Mellon University
- paper: http://abhinavsh.info/context_priming_feedback.pdf
- poster: http://www.eccv2016.org/files/posters/P-1A-20.pdf
An Implementation of Faster RCNN with Study for Region Sampling
- intro: Technical Report, 3 pages. CMU
- arxiv: https://arxiv.org/abs/1702.02138
- github: https://github.com/endernewton/tf-faster-rcnn
Interpretable R-CNN
- intro: North Carolina State University & Alibaba
- keywords: AND-OR Graph (AOG)
- arxiv: https://arxiv.org/abs/1711.05226
Light-Head R-CNN
Light-Head R-CNN: In Defense of Two-Stage Object Detector
- intro: Tsinghua University & Megvii Inc
- arxiv: https://arxiv.org/abs/1711.07264
- github(official, Tensorflow): https://github.com/zengarden/light_head_rcnn
- github: https://github.com/terrychenism/Deformable-ConvNets/blob/master/rfcn/symbols/resnet_v1_101_rfcn_light.py#L784
Cascade R-CNN
Cascade R-CNN: Delving into High Quality Object Detection
- intro: CVPR 2018. UC San Diego
- arxiv: https://arxiv.org/abs/1712.00726
- github(Caffe, official): https://github.com/zhaoweicai/cascade-rcnn
MultiBox
Scalable Object Detection using Deep Neural Networks
- intro: first MultiBox. Train a CNN to predict Region of Interest.
- arxiv: http://arxiv.org/abs/1312.2249
- github: https://github.com/google/multibox
- blog: https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html
Scalable, High-Quality Object Detection
- intro: second MultiBox
- arxiv: http://arxiv.org/abs/1412.1441
- github: https://github.com/google/multibox
SPP-Net
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- intro: ECCV 2014 / TPAMI 2015
- arxiv: http://arxiv.org/abs/1406.4729
- github: https://github.com/ShaoqingRen/SPP_net
- notes: http://zhangliliang.com/2014/09/13/paper-note-sppnet/
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
- intro: PAMI 2016
- intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations
- project page: http://www.ee.cuhk.edu.hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html
- arxiv: http://arxiv.org/abs/1412.5661
Object Detectors Emerge in Deep Scene CNNs
- intro: ICLR 2015
- arxiv: http://arxiv.org/abs/1412.6856
- paper: https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou_iclr15.pdf
- paper: https://people.csail.mit.edu/khosla/papers/iclr2015_zhou.pdf
- slides: http://places.csail.mit.edu/slide_iclr2015.pdf
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
- intro: CVPR 2015
- project(code+data): https://www.cs.toronto.edu/~yukun/segdeepm.html
- arxiv: https://arxiv.org/abs/1502.04275
- github: https://github.com/YknZhu/segDeepM
Object Detection Networks on Convolutional Feature Maps
- intro: TPAMI 2015
- keywords: NoC
- arxiv: http://arxiv.org/abs/1504.06066
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
- arxiv: http://arxiv.org/abs/1504.03293
- slides: http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf
- github: https://github.com/YutingZhang/fgs-obj
DeepBox: Learning Objectness with Convolutional Networks
- keywords: DeepBox
- arxiv: http://arxiv.org/abs/1505.02146
- github: https://github.com/weichengkuo/DeepBox
MR-CNN
Object detection via a multi-region & semantic segmentation-aware CNN model
- intro: ICCV 2015. MR-CNN
- arxiv: http://arxiv.org/abs/1505.01749
- github: https://github.com/gidariss/mrcnn-object-detection
- notes: http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/
- notes: http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/
YOLO
You Only Look Once: Unified, Real-Time Object Detection
- arxiv: http://arxiv.org/abs/1506.02640
- code: http://pjreddie.com/darknet/yolo/
- github: https://github.com/pjreddie/darknet
- blog: https://pjreddie.com/publications/yolo/
- slides: https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p
- reddit: https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/
- github: https://github.com/gliese581gg/YOLO_tensorflow
- github: https://github.com/xingwangsfu/caffe-yolo
- github: https://github.com/frankzhangrui/Darknet-Yolo
- github: https://github.com/BriSkyHekun/py-darknet-yolo
- github: https://github.com/tommy-qichang/yolo.torch
- github: https://github.com/frischzenger/yolo-windows
- github: https://github.com/AlexeyAB/yolo-windows
- github: https://github.com/nilboy/tensorflow-yolo
darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
- blog: https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp
- github: https://github.com/thtrieu/darkflow
Start Training YOLO with Our Own Data
- intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
- blog: http://guanghan.info/blog/en/my-works/train-yolo/
- github: https://github.com/Guanghan/darknet
YOLO: Core ML versus MPSNNGraph
- intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
- blog: http://machinethink.net/blog/yolo-coreml-versus-mps-graph/
- github: https://github.com/hollance/YOLO-CoreML-MPSNNGraph
TensorFlow YOLO object detection on Android
- intro: Real-time object detection on Android using the YOLO network with TensorFlow
- github: https://github.com/natanielruiz/android-yolo
Computer Vision in iOS – Object Detection
- blog: https://sriraghu.com/2017/07/12/computer-vision-in-ios-object-detection/
- github:https://github.com/r4ghu/iOS-CoreML-Yolo
YOLOv2
YOLO9000: Better, Faster, Stronger
- arxiv: https://arxiv.org/abs/1612.08242
- code: http://pjreddie.com/yolo9000/
- github(Chainer): https://github.com/leetenki/YOLOv2
- github(Keras): https://github.com/allanzelener/YAD2K
- github(PyTorch): https://github.com/longcw/yolo2-pytorch
- github(Tensorflow): https://github.com/hizhangp/yolo_tensorflow
- github(Windows): https://github.com/AlexeyAB/darknet
- github: https://github.com/choasUp/caffe-yolo9000
- github: https://github.com/philipperemy/yolo-9000
darknet_scripts
- intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
- github: https://github.com/Jumabek/darknet_scripts
Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2
LightNet: Bringing pjreddie’s DarkNet out of the shadows
YOLO v2 Bounding Box Tool
- intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
- github: https://github.com/Cartucho/yolo-boundingbox-labeler-GUI
YOLOv3
YOLOv3: An Incremental Improvement
- project page: https://pjreddie.com/darknet/yolo/
- arxiv: https://arxiv.org/abs/1804.02767
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
- intro: ICCV 2015
- intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection task
- arxiv: http://arxiv.org/abs/1506.07704
- slides: https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf
- slides: http://image-net.org/challenges/talks/lunit-kaist-slide.pdf
DenseBox
DenseBox: Unifying Landmark Localization with End to End Object Detection
- arxiv: http://arxiv.org/abs/1509.04874
- demo: http://pan.baidu.com/s/1mgoWWsS
- KITTI result: http://www.cvlibs.net/datasets/kitti/eval_object.php
SSD
SSD: Single Shot MultiBox Detector
- intro: ECCV 2016 Oral
- arxiv: http://arxiv.org/abs/1512.02325
- paper: http://www.cs.unc.edu/~wliu/papers/ssd.pdf
- slides: http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.pdf
- github(Official): https://github.com/weiliu89/caffe/tree/ssd
- video: http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973
- github: https://github.com/zhreshold/mxnet-ssd
- github: https://github.com/zhreshold/mxnet-ssd.cpp
- github: https://github.com/rykov8/ssd_keras
- github: https://github.com/balancap/SSD-Tensorflow
- github: https://github.com/amdegroot/ssd.pytorch
- github(Caffe): https://github.com/chuanqi305/MobileNet-SSD
What’s the diffience in performance between this new code you pushed and the previous code? #327
https://github.com/weiliu89/caffe/issues/327
DSSD
DSSD : Deconvolutional Single Shot Detector
- intro: UNC Chapel Hill & Amazon Inc
- arxiv: https://arxiv.org/abs/1701.06659
- github: https://github.com/chengyangfu/caffe/tree/dssd
- github: https://github.com/MTCloudVision/mxnet-dssd
- demo: http://120.52.72.53/www.cs.unc.edu/c3pr90ntc0td/~cyfu/dssd_lalaland.mp4
Enhancement of SSD by concatenating feature maps for object detection
- intro: rainbow SSD (R-SSD)
- arxiv: https://arxiv.org/abs/1705.09587
Context-aware Single-Shot Detector
- keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
- arxiv: https://arxiv.org/abs/1707.08682
Feature-Fused SSD: Fast Detection for Small Objects
https://arxiv.org/abs/1709.05054
FSSD
FSSD: Feature Fusion Single Shot Multibox Detector
https://arxiv.org/abs/1712.00960
Weaving Multi-scale Context for Single Shot Detector
- intro: WeaveNet
- keywords: fuse multi-scale information
- arxiv: https://arxiv.org/abs/1712.03149
ESSD
Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects
- intro: Zhengzhou University
- arxiv: https://arxiv.org/abs/1805.07009
Inside-Outside Net (ION)
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
- intro: 「0.8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1.15s per image with it」.
- arxiv: http://arxiv.org/abs/1512.04143
- slides: http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf
- coco-leaderboard: http://mscoco.org/dataset/#detections-leaderboard
Adaptive Object Detection Using Adjacency and Zoom Prediction
- intro: CVPR 2016. AZ-Net
- arxiv: http://arxiv.org/abs/1512.07711
- github: https://github.com/luyongxi/az-net
- youtube: https://www.youtube.com/watch?v=YmFtuNwxaNM
G-CNN: an Iterative Grid Based Object Detector
Factors in Finetuning Deep Model for object detection
Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution
- intro: CVPR 2016.rank 3rd for provided data and 2nd for external data on ILSVRC 2015 object detection
- project page: http://www.ee.cuhk.edu.hk/~wlouyang/projects/ImageNetFactors/CVPR16.html
- arxiv: http://arxiv.org/abs/1601.05150
We don’t need no bounding-boxes: Training object class detectors using only human verification
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
A MultiPath Network for Object Detection
- intro: BMVC 2016. Facebook AI Research (FAIR)
- arxiv: http://arxiv.org/abs/1604.02135
- github: https://github.com/facebookresearch/multipathnet
CRAFT
CRAFT Objects from Images
- intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNN
- project page: http://byangderek.github.io/projects/craft.html
- arxiv: https://arxiv.org/abs/1604.03239
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_CRAFT_Objects_From_CVPR_2016_paper.pdf
- github: https://github.com/byangderek/CRAFT
OHEM
Training Region-based Object Detectors with Online Hard Example Mining
- intro: CVPR 2016 Oral. Online hard example mining (OHEM)
- arxiv: http://arxiv.org/abs/1604.03540
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.pdf
- github(Official): https://github.com/abhi2610/ohem
- author page: http://abhinav-shrivastava.info/
S-OHEM: Stratified Online Hard Example Mining for Object Detection
Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
- intro: CVPR 2016
- keywords: scale-dependent pooling (SDP), cascaded rejection classifiers (CRC)
- paper: http://www-personal.umich.edu/~wgchoi/SDP-CRC_camready.pdf
R-FCN
R-FCN: Object Detection via Region-based Fully Convolutional Networks
arxiv: http://arxiv.org/abs/1605.06409
github: https://github.com/daijifeng001/R-FCN
github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/rfcn
github: https://github.com/Orpine/py-R-FCN
github: https://github.com/PureDiors/pytorch_RFCN
github: https://github.com/bharatsingh430/py-R-FCN-multiGPU
github: https://github.com/xdever/RFCN-tensorflow
R-FCN-3000 at 30fps: Decoupling Detection and Classification
Recycle deep features for better object detection
MS-CNN
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
- intro: ECCV 2016
- intro: 640×480: 15 fps, 960×720: 8 fps
- arxiv: http://arxiv.org/abs/1607.07155
- github: https://github.com/zhaoweicai/mscnn
- poster: http://www.eccv2016.org/files/posters/P-2B-38.pdf
Multi-stage Object Detection with Group Recursive Learning
- intro: VOC2007: 78.6%, VOC2012: 74.9%
- arxiv: http://arxiv.org/abs/1608.05159
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
- intro: WACV 2017. SubCNN
- arxiv: http://arxiv.org/abs/1604.04693
- github: https://github.com/tanshen/SubCNN
PVANET
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
- intro: Presented at NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks (EMDNN). Continuation of arXiv:1608.08021
- arxiv: https://arxiv.org/abs/1611.08588
- github: https://github.com/sanghoon/pva-faster-rcnn
- leaderboard(PVANet 9.0): http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4
GBD-Net
Gated Bi-directional CNN for Object Detection
- intro: The Chinese University of Hong Kong & Sensetime Group Limited
- paper: http://link.springer.com/chapter/10.1007/978-3-319-46478-7_22
- mirror: https://pan.baidu.com/s/1dFohO7v
Crafting GBD-Net for Object Detection
- intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideo
- intro: gated bi-directional CNN (GBD-Net)
- arxiv: https://arxiv.org/abs/1610.02579
- github: https://github.com/craftGBD/craftGBD
StuffNet: Using ‘Stuff’ to Improve Object Detection
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene
Hierarchical Object Detection with Deep Reinforcement Learning
- intro: Deep Reinforcement Learning Workshop (NIPS 2016)
- project page: https://imatge-upc.github.io/detection-2016-nipsws/
- arxiv: https://arxiv.org/abs/1611.03718
- slides: http://www.slideshare.net/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning
- github: https://github.com/imatge-upc/detection-2016-nipsws
- blog: http://jorditorres.org/nips/
Learning to detect and localize many objects from few examples
Speed/accuracy trade-offs for modern convolutional object detectors
- intro: CVPR 2017. Google Research
- arxiv: https://arxiv.org/abs/1611.10012
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
- arxiv: https://arxiv.org/abs/1612.01051
- github: https://github.com/BichenWuUCB/squeezeDet
- github: https://github.com/fregu856/2D_detection
Feature Pyramid Network (FPN)
Feature Pyramid Networks for Object Detection
- intro: Facebook AI Research
- arxiv: https://arxiv.org/abs/1612.03144
Action-Driven Object Detection with Top-Down Visual Attentions
Beyond Skip Connections: Top-Down Modulation for Object Detection
- intro: CMU & UC Berkeley & Google Research
- arxiv: https://arxiv.org/abs/1612.06851
Wide-Residual-Inception Networks for Real-time Object Detection
- intro: Inha University
- arxiv: https://arxiv.org/abs/1702.01243
Attentional Network for Visual Object Detection
- intro: University of Maryland & Mitsubishi Electric Research Laboratories
- arxiv: https://arxiv.org/abs/1702.01478
Learning Chained Deep Features and Classifiers for Cascade in Object Detection
- keykwords: CC-Net
- intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007
- arxiv: https://arxiv.org/abs/1702.07054
DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
- intro: ICCV 2017 (poster)
- arxiv: https://arxiv.org/abs/1703.10295
Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1704.03944
Spatial Memory for Context Reasoning in Object Detection
Accurate Single Stage Detector Using Recurrent Rolling Convolution
- intro: CVPR 2017. SenseTime
- keywords: Recurrent Rolling Convolution (RRC)
- arxiv: https://arxiv.org/abs/1704.05776
- github: https://github.com/xiaohaoChen/rrc_detection
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
- intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc
- arxiv: https://arxiv.org/abs/1705.05922
Point Linking Network for Object Detection
- intro: Point Linking Network (PLN)
- arxiv: https://arxiv.org/abs/1706.03646
Perceptual Generative Adversarial Networks for Small Object Detection
Few-shot Object Detection
Yes-Net: An effective Detector Based on Global Information
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
Towards lightweight convolutional neural networks for object detection
RON: Reverse Connection with Objectness Prior Networks for Object Detection
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1707.01691
- github: https://github.com/taokong/RON
Mimicking Very Efficient Network for Object Detection
- intro: CVPR 2017. SenseTime & Beihang University
- paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Mimicking_Very_Efficient_CVPR_2017_paper.pdf
Residual Features and Unified Prediction Network for Single Stage Detection
https://arxiv.org/abs/1707.05031
Deformable Part-based Fully Convolutional Network for Object Detection
- intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
- arxiv: https://arxiv.org/abs/1707.06175
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1707.06399
Recurrent Scale Approximation for Object Detection in CNN
- intro: ICCV 2017
- keywords: Recurrent Scale Approximation (RSA)
- arxiv: https://arxiv.org/abs/1707.09531
- github: https://github.com/sciencefans/RSA-for-object-detection
DSOD
DSOD: Learning Deeply Supervised Object Detectors from Scratch
- intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China
- arxiv: https://arxiv.org/abs/1708.01241
- github: https://github.com/szq0214/DSOD
RetinaNet
Focal Loss for Dense Object Detection
- intro: ICCV 2017 Best student paper award. Facebook AI Research
- keywords: RetinaNet
- arxiv: https://arxiv.org/abs/1708.02002
Focal Loss Dense Detector for Vehicle Surveillance
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1708.02863
Incremental Learning of Object Detectors without Catastrophic Forgetting
- intro: ICCV 2017. Inria
- arxiv: https://arxiv.org/abs/1708.06977
Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
Dynamic Zoom-in Network for Fast Object Detection in Large Images
https://arxiv.org/abs/1711.05187
Zero-Annotation Object Detection with Web Knowledge Transfer
- intro: NTU, Singapore & Amazon
- keywords: multi-instance multi-label domain adaption learning framework
- arxiv: https://arxiv.org/abs/1711.05954
MegDet
MegDet: A Large Mini-Batch Object Detector
- intro: Peking University & Tsinghua University & Megvii Inc
- arxiv: https://arxiv.org/abs/1711.07240
Single-Shot Refinement Neural Network for Object Detection
- arxiv: https://arxiv.org/abs/1711.06897
- github: https://github.com/sfzhang15/RefineDet
- github: https://github.com/MTCloudVision/RefineDet-Mxnet
Receptive Field Block Net for Accurate and Fast Object Detection
- intro: RFBNet
- arxiv: https://arxiv.org/abs/1711.07767
- github: https://github.com//ruinmessi/RFBNet
An Analysis of Scale Invariance in Object Detection - SNIP
- intro: CVPR 2018
- arxiv: https://arxiv.org/abs/1711.08189
- github: https://github.com/bharatsingh430/snip
Feature Selective Networks for Object Detection
Learning a Rotation Invariant Detector with Rotatable Bounding Box
- arxiv: https://arxiv.org/abs/1711.09405
- github(official, Caffe): https://github.com/liulei01/DRBox
Scalable Object Detection for Stylized Objects
- intro: Microsoft AI & Research Munich
- arxiv: https://arxiv.org/abs/1711.09822
Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
Deep Regionlets for Object Detection
- keywords: region selection network, gating network
- arxiv: https://arxiv.org/abs/1712.02408
Training and Testing Object Detectors with Virtual Images
- intro: IEEE/CAA Journal of Automatica Sinica
- arxiv: https://arxiv.org/abs/1712.08470
Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
- keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation
- arxiv: https://arxiv.org/abs/1712.08832
Spot the Difference by Object Detection
- intro: Tsinghua University & JD Group
- arxiv: https://arxiv.org/abs/1801.01051
Localization-Aware Active Learning for Object Detection
Object Detection with Mask-based Feature Encoding
LSTD: A Low-Shot Transfer Detector for Object Detection
- intro: AAAI 2018
- arxiv: https://arxiv.org/abs/1803.01529
Domain Adaptive Faster R-CNN for Object Detection in the Wild
- intro: CVPR 2018. ETH Zurich & ESAT/PSI
- arxiv: https://arxiv.org/abs/1803.03243
- github(official. Caffe): https://github.com/yuhuayc/da-faster-rcnn
Pseudo Mask Augmented Object Detection
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
Learning Region Features for Object Detection
- intro: Peking University & MSRA
- arxiv: https://arxiv.org/abs/1803.07066
Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
- intro: Singapore Management University & Zhejiang University
- arxiv: https://arxiv.org/abs/1803.08208
Object Detection for Comics using Manga109 Annotations
- intro: University of Tokyo & National Institute of Informatics, Japan
- arxiv: https://arxiv.org/abs/1803.08670
Task-Driven Super Resolution: Object Detection in Low-resolution Images
Transferring Common-Sense Knowledge for Object Detection
Multi-scale Location-aware Kernel Representation for Object Detection
- intro: CVPR 2018
- arxiv: https://arxiv.org/abs/1804.00428
- github: https://github.com/Hwang64/MLKP
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
- intro: National University of Defense Technology
- arxiv: https://arxiv.org/abs/1804.04606
DetNet: A Backbone network for Object Detection
- intro: Tsinghua University & Megvii Inc
- arxiv: https://arxiv.org/abs/1804.06215
Robust Physical Adversarial Attack on Faster R-CNN Object Detector
AdvDetPatch: Attacking Object Detectors with Adversarial Patches
Physical Adversarial Examples for Object Detectors
- intro: WOOT 2018
- arxiv: https://arxiv.org/abs/1807.07769
Quantization Mimic: Towards Very Tiny CNN for Object Detection
Object detection at 200 Frames Per Second
- intro: United Technologies Research Center-Ireland
- arxiv: https://arxiv.org/abs/1805.06361
Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images
- intro: CVPR 2018 Deep Vision Workshop
- arxiv: https://arxiv.org/abs/1805.11778
SNIPER: Efficient Multi-Scale Training
Soft Sampling for Robust Object Detection
MetaAnchor: Learning to Detect Objects with Customized Anchors
- intro: Megvii Inc (Face++) & Fudan University
- arxiv: https://arxiv.org/abs/1807.00980
Localization Recall Precision (LRP): A New Performance Metric for Object Detection
- intro: ECCV 2018. Middle East Technical University
- arxiv: https://arxiv.org/abs/1807.01696
- github: https://github.com/cancam/LRP
Auto-Context R-CNN
- intro: Rejected by ECCV18
- arxiv: https://arxiv.org/abs/1807.02842
Pooling Pyramid Network for Object Detection
- intro: Google AI Perception
- arxiv: https://arxiv.org/abs/1807.03284
Modeling Visual Context is Key to Augmenting Object Detection Datasets
- intro: ECCV 2018
- arxiv: https://arxiv.org/abs/1807.07428
Dual Refinement Network for Single-Shot Object Detection
Acquisition of Localization Confidence for Accurate Object Detection
- intro: ECCV 2018
- arxiv: https://arxiv.org/abs/1807.11590
- gihtub: https://github.com/vacancy/PreciseRoIPooling
CornerNet: Detecting Objects as Paired Keypoints
- intro: ECCV 2018
- keywords: IoU-Net, PreciseRoIPooling
- arxiv: https://arxiv.org/abs/1808.01244
- github: https://github.com/umich-vl/CornerNet
Unsupervised Hard Example Mining from Videos for Improved Object Detection
- intro: ECCV 2018
- arxiv: https://arxiv.org/abs/1808.04285
SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection
A Survey of Modern Object Detection Literature using Deep Learning
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
- intro: BMVC 2018
- arxiv: https://arxiv.org/abs/1807.11013
- github: https://github.com/lyxok1/Tiny-DSOD
Deep Feature Pyramid Reconfiguration for Object Detection
- intro: ECCV 2018
- arxiv: https://arxiv.org/abs/1808.07993
Non-Maximum Suppression (NMS)
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
- intro: CVPR 2015
- arxiv: http://arxiv.org/abs/1411.5309
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wan_End-to-End_Integration_of_2015_CVPR_paper.pdf
A convnet for non-maximum suppression
- arxiv: http://arxiv.org/abs/1511.06437
Improving Object Detection With One Line of Code
Soft-NMS – Improving Object Detection With One Line of Code
- intro: ICCV 2017. University of Maryland
- keywords: Soft-NMS
- arxiv: https://arxiv.org/abs/1704.04503
- github: https://github.com/bharatsingh430/soft-nms
Learning non-maximum suppression
- intro: CVPR 2017
- project page: https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/learning-nms/
- arxiv: https://arxiv.org/abs/1705.02950
- github: https://github.com/hosang/gossipnet
Relation Networks for Object Detection
- intro: CVPR 2018 oral
- arxiv: https://arxiv.org/abs/1711.11575
- github(official, MXNet): https://github.com/msracver/Relation-Networks-for-Object-Detection
Adversarial Examples
Adversarial Examples that Fool Detectors
- intro: University of Illinois
- arxiv: https://arxiv.org/abs/1712.02494
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
- project page: http://nicholas.carlini.com/code/nn_breaking_detection/
- arxiv: https://arxiv.org/abs/1705.07263
- github: https://github.com/carlini/nn_breaking_detection
Weakly Supervised Object Detection
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
- intro: CVPR 2016
- arxiv: http://arxiv.org/abs/1604.05766
Weakly supervised object detection using pseudo-strong labels
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
- intro: IJCAI 2017
- arxiv: https://arxiv.org/abs/1706.06768
Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
- intro: TPAMI 2017. National Institutes of Health (NIH) Clinical Center
- arxiv: https://arxiv.org/abs/1801.03145
Video Object Detection
Learning Object Class Detectors from Weakly Annotated Video
- intro: CVPR 2012
- paper: https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth_biwi_00905.pdf
Analysing domain shift factors between videos and images for object detection
Video Object Recognition
Deep Learning for Saliency Prediction in Natural Video
- intro: Submitted on 12 Jan 2016
- keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
- paper: https://hal.archives-ouvertes.fr/hal-01251614/document
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
- intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
- arxiv: http://arxiv.org/abs/1604.02532
- github: https://github.com/myfavouritekk/T-CNN
Object Detection from Video Tubelets with Convolutional Neural Networks
- intro: CVPR 2016 Spotlight paper
- arxiv: https://arxiv.org/abs/1604.04053
- paper: http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet_CVPR16.pdf
- gihtub: https://github.com/myfavouritekk/vdetlib
Object Detection in Videos with Tubelets and Multi-context Cues
- intro: SenseTime Group
- slides: http://www.ee.cuhk.edu.hk/~xgwang/CUvideo.pdf
- slides: http://image-net.org/challenges/talks/Object%20Detection%20in%20Videos%20with%20Tubelets%20and%20Multi-context%20Cues%20-%20Final.pdf
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
- intro: BMVC 2016
- keywords: pseudo-labeler
- arxiv: http://arxiv.org/abs/1607.04648
- paper: http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video_object_detection_BMVC.pdf
CNN Based Object Detection in Large Video Images
- intro: WangTao @ 愛奇藝
- keywords: object retrieval, object detection, scene classification
- slides: http://on-demand.gputechconf.com/gtc/2016/presentation/s6362-wang-tao-cnn-based-object-detection-large-video-images.pdf
Object Detection in Videos with Tubelet Proposal Networks
Flow-Guided Feature Aggregation for Video Object Detection
- intro: MSRA
- arxiv: https://arxiv.org/abs/1703.10025
Video Object Detection using Faster R-CNN
- blog: http://andrewliao11.github.io/object_detection/faster_rcnn/
- github: https://github.com/andrewliao11/py-faster-rcnn-imagenet
Improving Context Modeling for Video Object Detection and Tracking
http://image-net.org/challenges/talks_2017/ilsvrc2017_short(poster).pdf
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1708.00666
Mobile Video Object Detection with Temporally-Aware Feature Maps
Towards High Performance Video Object Detection
Impression Network for Video Object Detection
Spatial-Temporal Memory Networks for Video Object Detection
3D-DETNet: a Single Stage Video-Based Vehicle Detector
Object Detection in Videos by Short and Long Range Object Linking
Object Detection in Video with Spatiotemporal Sampling Networks
- intro: University of Pennsylvania, 2Dartmouth College
- arxiv: https://arxiv.org/abs/1803.05549
Towards High Performance Video Object Detection for Mobiles
- intro: Microsoft Research Asia
- arxiv: https://arxiv.org/abs/1804.05830
Optimizing Video Object Detection via a Scale-Time Lattice
- intro: CVPR 2018
- project page: http://mmlab.ie.cuhk.edu.hk/projects/ST-Lattice/
- arxiv: https://arxiv.org/abs/1804.05472
- github: https://github.com/hellock/scale-time-lattice
Object Detection on Mobile Devices
Pelee: A Real-Time Object Detection System on Mobile Devices
- intro: ICLR 2018 workshop track
- intro: based on the SSD
- arxiv: https://arxiv.org/abs/1804.06882
- github: https://github.com/Robert-JunWang/Pelee
Object Detection in 3D
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
- intro: Valeo Schalter und Sensoren GmbH & Ilmenau University of Technology
- arxiv: https://arxiv.org/abs/1803.06199
Object Detection on RGB-D
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Differential Geometry Boosts Convolutional Neural Networks for Object Detection
- intro: CVPR 2016
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w23/html/Wang_Differential_Geometry_Boosts_CVPR_2016_paper.html
A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation
Zero-Shot Object Detection
Zero-Shot Detection
- intro: Australian National University
- keywords: YOLO
- arxiv: https://arxiv.org/abs/1803.07113
Zero-Shot Object Detection
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
- intro: Australian National University
- arxiv: https://arxiv.org/abs/1803.06049
Zero-Shot Object Detection by Hybrid Region Embedding
- intro: Middle East Technical University & Hacettepe University
- arxiv: https://arxiv.org/abs/1805.06157
Salient Object Detection
This task involves predicting the salient regions of an image given by human eye fixations.
Best Deep Saliency Detection Models (CVPR 2016 & 2015)
Large-scale optimization of hierarchical features for saliency prediction in natural images
Predicting Eye Fixations using Convolutional Neural Networks
Saliency Detection by Multi-Context Deep Learning
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection
- paper: www.shengfenghe.com/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html
Shallow and Deep Convolutional Networks for Saliency Prediction
- intro: CVPR 2016
- arxiv: http://arxiv.org/abs/1603.00845
- github: https://github.com/imatge-upc/saliency-2016-cvpr
Recurrent Attentional Networks for Saliency Detection
- intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN)
- arxiv: http://arxiv.org/abs/1604.03227
Two-Stream Convolutional Networks for Dynamic Saliency Prediction
Unconstrained Salient Object Detection
Unconstrained Salient Object Detection via Proposal Subset Optimization
- intro: CVPR 2016
- project page: http://cs-people.bu.edu/jmzhang/sod.html
- paper: http://cs-people.bu.edu/jmzhang/SOD/CVPR16SOD_camera_ready.pdf
- github: https://github.com/jimmie33/SOD
- caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-object-proposal-models-for-salient-object-detection
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
Salient Object Subitizing
- intro: CVPR 2015
- intro: predicting the existence and the number of salient objects in an image using holistic cues
- project page: http://cs-people.bu.edu/jmzhang/sos.html
- arxiv: http://arxiv.org/abs/1607.07525
- paper: http://cs-people.bu.edu/jmzhang/SOS/SOS_preprint.pdf
- caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-models-for-salient-object-subitizing
Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection
- intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)
- arxiv: http://arxiv.org/abs/1608.05177
Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs
- intro: ECCV 2016
- arxiv: http://arxiv.org/abs/1608.05186
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
A Deep Multi-Level Network for Saliency Prediction
Visual Saliency Detection Based on Multiscale Deep CNN Features
- intro: IEEE Transactions on Image Processing
- arxiv: http://arxiv.org/abs/1609.02077
A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
- intro: DSCLRCN
- arxiv: https://arxiv.org/abs/1610.01708
Deeply supervised salient object detection with short connections
- intro: IEEE TPAMI 2018 (IEEE CVPR 2017)
- arxiv: https://arxiv.org/abs/1611.04849
- github(official, Caffe): https://github.com/Andrew-Qibin/DSS
- github(Tensorflow): https://github.com/Joker316701882/Salient-Object-Detection
Weakly Supervised Top-down Salient Object Detection
- intro: Nanyang Technological University
- arxiv: https://arxiv.org/abs/1611.05345
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
- project page: https://imatge-upc.github.io/saliency-salgan-2017/
- arxiv: https://arxiv.org/abs/1701.01081
Visual Saliency Prediction Using a Mixture of Deep Neural Networks
A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network
Saliency Detection by Forward and Backward Cues in Deep-CNNs
Supervised Adversarial Networks for Image Saliency Detection
Group-wise Deep Co-saliency Detection
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
- intro: University of Maryland College Park & eBay Inc
- arxx" target="_blank">http://cs-people.bu.edu/jmzhang/SOD/CVPR16SOD_camera_ready.pdf
- github: https://github.com/jimmie33/SOD
- caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-object-proposal-models-for-salient-object-detectionDHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
Salient Object Subitizing
- intro: CVPR 2015
- intro: predicting the existence and the number of salient objects in an image using holistic cues
- project page: http://cs-people.bu.edu/jmzhang/sos.html
- arxiv: http://arxiv.org/abs/1607.07525
- paper: http://cs-people.bu.edu/jmzhang/SOS/SOS_preprint.pdf
- caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-models-for-salient-object-subitizingDeeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection
- intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)
- arxiv: http://arxiv.org/abs/1608.05177
Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs
- intro: ECCV 2016
- arxiv: http://arxiv.org/abs/1608.05186
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
A Deep Multi-Level Network for Saliency Prediction
Visual Saliency Detection Based on Multiscale Deep CNN Features
- intro: IEEE Transactions on Image Processing
- arxiv: http://arxiv.org/abs/1609.02077
A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
- intro: DSCLRCN
- arxiv: https://arxiv.org/abs/1610.01708
Deeply supervised salient object detection with short connections
- intro: IEEE TPAMI 2018 (IEEE CVPR 2017)
- arxiv: https://arxiv.org/abs/1611.04849
- github(official, Caffe): https://github.com/Andrew-Qibin/DSS
- github(Tensorflow): https://github.com/Joker316701882/Salient-Object-Detection
Weakly Supervised Top-down Salient Object Detection
- intro: Nanyang Technological University
- arxiv: https://arxiv.org/abs/1611.05345
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
- project page: https://imatge-upc.github.io/saliency-salgan-2017/
- arxiv: https://arxiv.org/abs/1701.01081
Visual Saliency Prediction Using a Mixture of Deep Neural Networks
A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network
Saliency Detection by Forward and Backward Cues in Deep-CNNs
Supervised Adversarial Networks for Image Saliency Detection
Group-wise Deep Co-saliency Detection
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
- intro: University of Maryland College Park & eBay Inc
- arxiv: https://arxiv.org/abs/1708.00079
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
- intro: ICCV 2017
- arixv: https://arxiv.org/abs/1708.02001
Learning Uncertain Convolutional Features for Accurate Saliency Detection
- intro: Accepted as a poster in ICCV 2017
- arxiv: https://arxiv.org/abs/1708.02031
Deep Edge-Aware Saliency Detection
Self-explanatory Deep Salient Object Detection
- intro: National University of Defense Technology, China & National University of Singapore
- arxiv: https://arxiv.org/abs/1708.05595
PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection
- arxiv: Saliency Detection by Forward and Backward Cues in Deep-CNNs
Supervised Adversarial Networks for Image Saliency Detection
Group-wise Deep Co-saliency Detection
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
- intro: University of Maryland College Park & eBay Inc
- arxiv: https://arxiv.org/abs/1708.00079
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
- intro: ICCV 2017
- arixv: https://arxiv.org/abs/1708.02001
Learning Uncertain Convolutional Features for Accurate Saliency Detection
- intro: Accepted as a poster in ICCV 2017
- arxiv: https://arxiv.org/abs/1708.02031
Deep Edge-Aware Saliency Detection
Self-explanatory Deep Salient Object Detection
- intro: National University of Defense Technology, China & National University of Singapore
- arxiv: https://arxiv.org/abs/1708.05595
PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection
DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets
Recurrently Aggregating Deep Features for Salient Object Detection
- intro: AAAI 2018
- paper: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16775/16281
Deep saliency: What is learnt by a deep network about saliency?
- intro: 2nd Workshop on Visualisation for Deep Learning in the 34th International Conference On Machine Learning
- arxiv: https://arxiv.org/abs/1801.04261
Contrast-Oriented Deep Neural Networks for Salient Object Detection
- intro: TNNLS
- arxiv: https://arxiv.org/abs/1803.11395
Salient Object Detection by Lossless Feature Reflection
- intro: IJCAI 2018
- arxiv: https://arxiv.org/abs/1802.06527
HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection
Video Saliency Detection
Deep Learning For Video Saliency Detection
Video Salient Object Detection Using Spatiotemporal Deep Features
Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM
Visual Relationship Detection
Visual Relationship Detection with Language Priors
- intro: ECCV 2016 oral
- paper: https://cs.stanford.edu/people/ranjaykrishna/vrd/vrd.pdf
- github: https://github.com/Prof-Lu-Cewu/Visual-Relationship-Detection
ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection
- intro: Visual Phrase reasoning Convolutional Neural Network (ViP-CNN), Visual Phrase Reasoning Structure (VPRS)
- arxiv: https://arxiv.org/abs/1702.07191
Visual Translation Embedding Network for Visual Relation Detection
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection
- intro: CVPR 2017 spotlight paper
- arxiv: https://arxiv.org/abs/1703.03054
Detecting Visual Relationships with Deep Relational Networks
- intro: CVPR 2017 oral. The Chinese University of Hong Kong
- arxiv: https://arxiv.org/abs/1704.03114
https://arxiv.org/abs/1707.07381
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
- intro: University of Maryland College Park & eBay Inc
- arxiv: https://arxiv.org/abs/1708.00079
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
- intro: ICCV 2017
- arixv:
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