4 citations · 4 across the 5 of their papers we have counts for
5 papers
Interpret the Predictions of Deep Networks via Re-Label Distillation
Yingying Hua, Shiming Ge, Daichi Zhang
Interpreting the predictions of a black-box deep network can facilitate the reliability of its deployment. In this work, we propose a re-label distillation approach to learn a dire…
Distilling Channels for Efficient Deep Tracking
Shiming Ge, Zhao Luo, Chunhui Zhang +2
Deep trackers have proven success in visual tracking. Typically, these trackers employ optimally pre-trained deep networks to represent all diverse objects with multi-channel featu…
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition
Shiming Ge, Kangkai Zhang, Haolin Liu +4
In spite of great success in many image recognition tasks achieved by recent deep models, directly applying them to recognize low-resolution images may suffer from low accuracy due…
Deepfake Video Detection with Spatiotemporal Dropout Transformer
Daichi Zhang, Fanzhao Lin, Yingying Hua +3
While the abuse of deepfake technology has caused serious concerns recently, how to detect deepfake videos is still a challenge due to the high photo-realistic synthesis of each fr…
Interpretable Face Manipulation Detection via Feature Whitening
Yingying Hua, Daichi Zhang, Pengju Wang +1
Why should we trust the detections of deep neural networks for manipulated faces? Understanding the reasons is important for users in improving the fairness, reliability, privacy a…