13 citations · 16 across the 5 of their papers we have counts for
5 papers
Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention
Xiangcheng Liu, Tianyi Wu, Guodong Guo
Vision transformer has emerged as a new paradigm in computer vision, showing excellent performance while accompanied by expensive computational cost. Image token pruning is one of…
RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization
Hongyi Yao, Pu Li, Jian Cao +3
We introduce a Power-of-Two low-bit post-training quantization(PTQ) method for deep neural network that meets hardware requirements and does not call for long-time retraining. Powe…
A free lunch from ViT:Adaptive Attention Multi-scale Fusion Transformer for Fine-grained Visual Recognition
Yuan Zhang, Jian Cao, Ling Zhang +4
Learning subtle representation about object parts plays a vital role in fine-grained visual recognition (FGVR) field. The vision transformer (ViT) achieves promising results on com…
AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance
Xiangcheng Liu, Jian Cao, Hongyi Yao +2
Channel pruning is one of the major compression approaches for deep neural networks. While previous pruning methods have mostly focused on identifying unimportant channels, channel…
An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution
Wenyu Sun, Jian Cao, Pengtao Xu +2
We propose an efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network structures close to winner tickets in the early training stage considerin…