30 citations · 84 across the 16 of their papers we have counts for
19 papers · 1 filter
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…
Look Through Masks: Towards Masked Face Recognition with De-Occlusion Distillation
Chenyu Li, Shiming Ge, Daichi Zhang +1
Many real-world applications today like video surveillance and urban governance need to address the recognition of masked faces, where content replacement by diverse masks often br…
Efficient Low-Resolution Face Recognition via Bridge Distillation
Shiming Ge, Shengwei Zhao, Chenyu Li +2
Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation a…
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…
Selective-Supervised Contrastive Learning with Noisy Labels
Shikun Li, Xiaobo Xia, Shiming Ge +1
Deep networks have strong capacities of embedding data into latent representations and finishing following tasks. However, the capacities largely come from high-quality annotated l…