3 papers
cs.CV2024
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…
cs.CV2024
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…
cs.CV2024
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…