Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model
SAM显著性检测模型 本文特点： 1、相比之前做显著性检测的模型，本文最大的特点是采用了neural attentive mechanism 这种机制是什么意思，给出原文的解释（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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相关文章
- 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. 图像显著性检测论文及代码汇总
- 5. 转---图像显著性检测论文及代码汇总
- 6. 图像显著性检测总结
- 7. Saliency Integration An Arbitrator Model阅读总结(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