7 citations · 15 across the 7 of their papers we have counts for
7 papers
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
Jiang-Xin Shi, Tong Wei, Zhi Zhou +3
The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts perf…
Scene-Aware Feature Matching
Xiaoyong Lu, Yaping Yan, Tong Wei +1
Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene. This…
Generalized Differentiable RANSAC
Tong Wei, Yash Patel, Alexander Shekhovtsov +2
We propose -RANSAC, a generalized differentiable RANSAC that allows learning the entire randomized robust estimation pipeline. The proposed approach enables the use of rela…
A Survey on Extreme Multi-label Learning
Tong Wei, Zhen Mao, Jiang-Xin Shi +2
Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good pe…
Transfer and Share: Semi-Supervised Learning from Long-Tailed Data
Tong Wei, Qian-Yu Liu, Jiang-Xin Shi +2
Long-Tailed Semi-Supervised Learning (LTSSL) aims to learn from class-imbalanced data where only a few samples are annotated. Existing solutions typically require substantial cost…
Adaptive Reordering Sampler with Neurally Guided MAGSAC
Tong Wei, Jiri Matas, Daniel Barath
We propose a new sampler for robust estimators that always selects the sample with the highest probability of consisting only of inliers. After every unsuccessful iteration, the in…
Prototypical Classifier for Robust Class-Imbalanced Learning
Tong Wei, Jiang-Xin Shi, Yu-Feng Li +1
Deep neural networks have been shown to be very powerful methods for many supervised learning tasks. However, they can also easily overfit to training set biases, i.e., label noise…