activity
20182022
most citedSelf-Challenging Improves Cross-Domain Generalization

43 citations · 163 across the 11 of their papers we have counts for

collaborators

12 papers

cs.CV20214 cited

Elaborative Rehearsal for Zero-shot Action Recognition

Shizhe Chen, Dong Huang

The growing number of action classes has posed a new challenge for video understanding, making Zero-Shot Action Recognition (ZSAR) a thriving direction. The ZSAR task aims to recog…

cs.LG202126 cited

How Do Adam and Training Strategies Help BNNs Optimization?

Zechun Liu, Zhiqiang Shen, Shichao Li +3

The best performing Binary Neural Networks (BNNs) are usually attained using Adam optimization and its multi-step training variants. However, to the best of our knowledge, few stud…

cs.CV202043 cited

Self-Challenging Improves Cross-Domain Generalization

Zeyi Huang, Haohan Wang, Eric P. Xing +1

Convolutional Neural Networks (CNN) conduct image classification by activating dominant features that correlated with labels. When the training and testing data are under similar d…

cs.CV20193 cited

Multiple Anchor Learning for Visual Object Detection

Wei Ke, Tianliang Zhang, Zeyi Huang +3

Classification and localization are two pillars of visual object detectors. However, in CNN-based detectors, these two modules are usually optimized under a fixed set of candidate…

cs.CV201911 cited

Frame-wise Motion and Appearance for Real-time Multiple Object Tracking

Jimuyang Zhang, Sanping Zhou, Jinjun Wang +1

The main challenge of Multiple Object Tracking (MOT) is the efficiency in associating indefinite number of objects between video frames. Standard motion estimators used in tracking…

eess.SP201918 cited

Temporal Unet: Sample Level Human Action Recognition using WiFi

Fei Wang, Yunpeng Song, Jimuyang Zhang +2

Human doing actions will result in WiFi distortion, which is widely explored for action recognition, such as the elderly fallen detection, hand sign language recognition, and keyst…