7 papers
Joint Class-Time Learning for Video Classification with Multi-Instance Partial-Label Learning
Lingyu Shen, Wei Tang, Fakhri Karray +1
Multi-instance partial-label learning (MIPL) addresses inexact supervision in both the instance and label spaces, which can be applied to video classification. However, bag-level l…
Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning
Wei Tang, Yin-Fang Yang, Weijia Zhang +1
Multi-instance partial-label learning (MIPL) is a weakly supervised framework that extends the principles of multi-instance learning (MIL) and partial-label learning (PLL) to addre…
Unleashing the Power of Vision-Language Models for Long-Tailed Multi-Label Visual Recognition
Wei Tang, Zuo-Zheng Wang, Kun Zhang +2
Long-tailed multi-label visual recognition poses a significant challenge, as images typically contain multiple labels with highly imbalanced class distributions, leading to biased…
Tuning the Right Foundation Models is What you Need for Partial Label Learning
Kuang He, Wei Tang, Tong Wei +1
Partial label learning (PLL) seeks to train generalizable classifiers from datasets with inexact supervision, a common challenge in real-world applications. Existing studies have d…
Multi-Instance Partial-Label Learning with Margin Adjustment
Wei Tang, Yin-Fang Yang, Zhaofei Wang +2
Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label s…
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…
From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification
Xin Liu, Weijia Zhang, Wei Tang +4
In whole slide images (WSIs) analysis, attention-based multi-instance learning (MIL) models are susceptible to spurious correlations and degrade under domain shift. These methods m…