Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model

时间 2021-01-11

标签显著性检测深度学习繁體版

原文原文链接

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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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

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