3 citations · 7 across the 6 of their papers we have counts for
6 papers
Vision-Language Models are Strong Noisy Label Detectors
Tong Wei, Hao-Tian Li, Chun-Shu Li +3
Recent research on fine-tuning vision-language models has demonstrated impressive performance in various downstream tasks. However, the challenge of obtaining accurately labeled da…
Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning
Wei Tang, Weijia Zhang, Min-Ling Zhang
Multi-instance partial-label learning (MIPL) addresses scenarios where each training sample is represented as a multi-instance bag associated with a candidate label set containing…
Binary Classification with Confidence Difference
Wei Wang, Lei Feng, Yuchen Jiang +3
Recently, learning with soft labels has been shown to achieve better performance than learning with hard labels in terms of model generalization, calibration, and robustness. Howev…
Robust Representation Learning for Unreliable Partial Label Learning
Yu Shi, Dong-Dong Wu, Xin Geng +1
Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. How…
Rethinking the Value of Labels for Instance-Dependent Label Noise Learning
Hanwen Deng, Weijia Zhang, Min-Ling Zhang
Label noise widely exists in large-scale datasets and significantly degenerates the performances of deep learning algorithms. Due to the non-identifiability of the instance-depende…
Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection
Zhaofei Wang, Weijia Zhang, Min-Ling Zhang
Weakly Supervised Object Detection (WSOD) enables the training of object detection models using only image-level annotations. State-of-the-art WSOD detectors commonly rely on multi…