6 citations · 12 across the 12 of their papers we have counts for
12 papers
On Improving the Algorithm-, Model-, and Data- Efficiency of Self-Supervised Learning
Yun-Hao Cao, Jianxin Wu
Self-supervised learning (SSL) has developed rapidly in recent years. However, most of the mainstream methods are computationally expensive and rely on two (or more) augmentations…
Dense Vision Transformer Compression with Few Samples
Hanxiao Zhang, Yifan Zhou, Guo-Hua Wang +1
Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a…
DiffuLT: How to Make Diffusion Model Useful for Long-tail Recognition
Jie Shao, Ke Zhu, Hanxiao Zhang +1
This paper proposes a new pipeline for long-tail (LT) recognition. Instead of re-weighting or re-sampling, we utilize the long-tailed dataset itself to generate a balanced proxy th…
Rectify the Regression Bias in Long-Tailed Object Detection
Ke Zhu, Minghao Fu, Jie Shao +2
Long-tailed object detection faces great challenges because of its extremely imbalanced class distribution. Recent methods mainly focus on the classification bias and its loss func…
Reviving Undersampling for Long-Tailed Learning
Hao Yu, Yingxiao Du, Jianxin Wu
The training datasets used in long-tailed recognition are extremely unbalanced, resulting in significant variation in per-class accuracy across categories. Prior works mostly used…
Multi-Label Self-Supervised Learning with Scene Images
Ke Zhu, Minghao Fu, Jianxin Wu
Self-supervised learning (SSL) methods targeting scene images have seen a rapid growth recently, and they mostly rely on either a dedicated dense matching mechanism or a costly uns…