2019 ICCV之多光譜行人檢測：Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection
Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection 當前的問題及概述： 真實的多光譜數據存在位置偏移問題，即彩色熱像對沒有嚴格對齊，使得一個物體在不同的模式中有不同的位置。 在本文中，提出了一種新的對齊區域CNN (AR-CNN)來處理端到端的弱對齊數據。 a是目前數據集弱對齊圖示，b爲本文的框架思路
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相關文章
- 1. 2019 ICIP之多光譜行人檢測：UNPAIRED CROSS-SPECTRAL PEDESTRIAN DETECTION VIA ADVERSARIAL FEATURE LEARNING
- 2. 自用（跨模態、多光譜行人檢測論文）
- 3. Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks （深度學習多光譜行人檢測綜述）
- 4. Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
- 5. 2019PR之RGBT行人檢測：Illumination-aware faster R-CNN for robust multispectral pedestrian detection
- 6. Box-level Segmentation Supervised Deep Neural Networks for Multispectral Pedestrian Detection(19)綜述
- 7. Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
- 8. 【行人檢測】High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection/CSP
- 9. 行人檢測--What Can Help Pedestrian Detection?
- 10. Object Detection(目標檢測神文)(二)