62 citations · 64 across the 4 of their papers we have counts for
4 papers
Token Compensator: Altering Inference Cost of Vision Transformer without Re-Tuning
Shibo Jie, Yehui Tang, Jianyuan Guo +3
Token compression expedites the training and inference of Vision Transformers (ViTs) by reducing the number of the redundant tokens, e.g., pruning inattentive tokens or merging sim…
Revisiting the Parameter Efficiency of Adapters from the Perspective of Precision Redundancy
Shibo Jie, Haoqing Wang, Zhi-Hong Deng
Current state-of-the-art results in computer vision depend in part on fine-tuning large pre-trained vision models. However, with the exponential growth of model sizes, the conventi…
Detachedly Learn a Classifier for Class-Incremental Learning
Ziheng Li, Shibo Jie, Zhi-Hong Deng
In continual learning, model needs to continually learn a feature extractor and classifier on a sequence of tasks. This paper focuses on how to learn a classifier based on a pretra…
Convolutional Bypasses Are Better Vision Transformer Adapters
Shibo Jie, Zhi-Hong Deng
The pretrain-then-finetune paradigm has been widely adopted in computer vision. But as the size of Vision Transformer (ViT) grows exponentially, the full finetuning becomes prohibi…