Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model
SAM顯著性檢測模型 本文特點： 1、相比之前做顯著性檢測的模型，本文最大的特點是採用了neural attentive mechanism 這種機制是什麼意思，給出原文的解釋（自己不太明白）： Machine attention [16] is a computational paradigm which aims to compute an output as a weighted sum o
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相關文章
- 1. Predicting Human Microbe-Drug Associations via Graph Convolutional Network with Conditional Random F
- 2. Structure and Function of Human Eye
- 3. Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
- 4. 圖像顯著性檢測論文及代碼彙總
- 5. 轉---圖像顯著性檢測論文及代碼彙總
- 6. 圖像顯著性檢測總結
- 7. Saliency Integration An Arbitrator Model閱讀總結(正在更新)
- 8. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
- 9. Assembly and diploid architecture of an individual human genome via single-molecule technologies
- 10. (3D-HPE)Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop