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A Self Validation Network for Object-Level Human Attention Estimation
Zehua Zhang, Chen Yu, David Crandall
Some recent work [22, 66, 68] has discussed estimating probability maps of ego-attention or predicting gaze points in egocentric videos. However, people think not in terms of points in their field of view, but in terms of theobjects that they are attending to. Of course, the object of interest could be obtained by first estimating the gaze with the gaze estimator and generating object candidates from an off-theshelf object detector, and then picking the object that the estimated gaze falls in. Because this bottom-up approach estimateswhere and what separately, it could be doomed to fail if the eye gaze prediction is slightly inaccurate, such as falling between two objects or in the intersection ofmultiple object bounding boxes (Figure1).
Robust and Communication-Efficient Collaborative Learning
Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani
Neural Information Processing Systems http://nips.cc/
Defending Against Neural Fake News
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, Yejin Choi
Neural Information Processing Systems http://nips.cc/
Auxiliary Losses for Learning Generalizable Concept-based Models
Bottleneck Models (CBMs) have gained popularity since their introduction.
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback
Arun Verma, Manjesh Hanawal, Arun Rajkumar, Raman Sankaran
The problem is challenging because the loss distribution and threshold value of each arm are unknown. We study this novel setting by establishing its'equivalence' to Multiple-Play Multi-Armed Bandits (MP-MAB) andCombinatorial Semi-Bandits.
