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20082026
most citedMulti-Instance Multi-Label Learning

435 citations · 503 across the 62 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.LG2024

Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition

Zi-Hao Zhou, Siyuan Fang, Zi-Jing Zhou +3

Long-tailed semi-supervised learning poses a significant challenge in training models with limited labeled data exhibiting a long-tailed label distribution. Current state-of-the-ar…

stat.ML2024

HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks

Xin Liu, Weijia Zhang, Min-Ling Zhang

In survival analysis, subjects often face competing risks; for example, individuals with cancer may also suffer from heart disease or other illnesses, which can jointly influence t…

cs.LG2024★ 3 cited

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…

cs.LG2024

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…

cs.LG2024★ 1 cited

Towards Few-Shot Learning in the Open World: A Review and Beyond

Hui Xue, Yuexuan An, Yongchun Qin +5

Human intelligence is characterized by our ability to absorb and apply knowledge from the world around us, especially in rapidly acquiring new concepts from minimal examples, under…

cs.CV2024

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…

cs.LG2024

Decouple then Converge: Handling Unknown Unlabeled Distributions in Long-Tailed Semi-Supervised Learning

Kai Gan, Tong Wei, Min-Ling Zhang

While long-tailed semi-supervised learning (LTSSL) has attracted growing attention in many real-world classification tasks, existing LTSSL algorithms typically assume that labeled…