66 citations · 102 across the 7 of their papers we have counts for
5 papers · 2 filters
ISTR: End-to-End Instance Segmentation with Transformers
Jie Hu, Liujuan Cao, Yao Lu +6
End-to-end paradigms significantly improve the accuracy of various deep-learning-based computer vision models. To this end, tasks like object detection have been upgraded by replac…
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack
Yixu Wang, Jie Li, Hong Liu +4
Previous studies have verified that the functionality of black-box models can be stolen with full probability outputs. However, under the more practical hard-label setting, we obse…
Lottery Jackpots Exist in Pre-trained Models
Yuxin Zhang, Mingbao Lin, Yunshan Zhong +2
Network pruning is an effective approach to reduce network complexity with acceptable performance compromise. Existing studies achieve the sparsity of neural networks via time-cons…
Distilling a Powerful Student Model via Online Knowledge Distillation
Shaojie Li, Mingbao Lin, Yan Wang +4
Existing online knowledge distillation approaches either adopt the student with the best performance or construct an ensemble model for better holistic performance. However, the fo…
Network Pruning using Adaptive Exemplar Filters
Mingbao Lin, Rongrong Ji, Shaojie Li +4
Popular network pruning algorithms reduce redundant information by optimizing hand-crafted models, and may cause suboptimal performance and long time in selecting filters. We innov…