Weakly Supervised Learning

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13 papers in the last four weeks, up 225% on the four weeks before. 0.1% of all new papers.

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Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels
Apr 8, 2026Yaqi Zhao, Haoliang Sun, Yating Wang +2Prompt Learning VLM Adaptation
School of Software, Shandong University, Jinan, China
Exploring the Temporal Consistency for Point-Level Weakly-Supervised Temporal Action Localization
Feb 5, 2026Yunchuan Ma, Laiyun Qing, Guorong Li +3Weakly Supervised Learning Temporal Consistency
University of Chinese Academy of Sciences, Beijing, 100190, China · Macquarie University
Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning
Dec 19, 2025Wei Tang, Yin-Fang Yang, Weijia Zhang +1Model Calibration Weakly Supervised Learning
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China, and the Key Laboratory of Computer Network and Information Integration (Southeast University), MoE, China · School of Computer and Information Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia
A Unified and Stable Risk Minimization Framework for Weakly Supervised Learning with Theoretical Guarantees
Nov 28, 2025Miao Zhang, Junpeng Li, Changchun Hua +1Weakly Supervised Learning Statistical Learning Theory
Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China
Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation
Aug 22, 2025Jiaqi Ma, Guo-Sen Xie, Fang Zhao +1Weakly Supervised Image Segmentation Weakly Supervised Semantic Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · School of Intelligence Science and Technology, Nanjing University, Suzhou 215163, China
SynSeg: Feature Synergy for Multi-Category Contrastive Learning in End-to-End Open-Vocabulary Semantic Segmentation
Aug 8, 2025Weichen Zhang, Kebin Liu, Fan Dang +3Contrastive Learning Weakly Supervised Image Segmentation
Tsinghua University, 100084, Beijing, China · Beijing Jiaotong University, 100044, Beijing, China
Post-Disaster Affected Area Segmentation with a Vision Transformer (ViT)-based EVAP Model using Sentinel-2 and Formosat-5 Imagery
Jul 21, 2025Yi-Shan Chu, Hsuan-Cheng WeiRemote Sensing Image Understanding Weakly Supervised Image Segmentation
Department of Mathematical Science, National Chengchi University, Taipei, Taiwan · Division of Satellite Data Application, Taiwan Space Agency, Hsinchu, Taiwan · Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
Neuro-symbolic Weak Supervision: Theory and Semantics
Mar 24, 2025Nijesh Upreti, Vaishak BelleWeakly Supervised Learning Neuro-Symbolic Reasoning
School of Informatics, University of Edinburgh, Edinburgh, UK
TeD-Loc: Text Distillation for Weakly Supervised Object Localization
Jan 22, 2025Shakeeb Murtaza, Soufiane Belharbi, Alexis Guichemerre +2Cross-Modal Knowledge Distillation Weakly Supervised Learning
LIVIA, ILLS, Dept. of Systems Engineering, ETS Montreal, Canada
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
May 29, 2024Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai +1Weakly Supervised Learning Positive-Unlabeled Learning
NTT
A Realistic Protocol for Evaluation of Weakly Supervised Object Localization
Apr 15, 2024Shakeeb Murtaza, Soufiane Belharbi, Marco Pedersoli +1Weakly Supervised Learning Object Detection
LIVIA, ILLS, Dept. of Systems Engineering, ETS Montreal, Canada
When Minute-Resolution Monitoring Meets Session-Level Injury Labels: Landmark-Based Discrimination in Elite Women's Football
Date pendingEvangelos Chatzidimitriou, Konstantinos TserpesWeakly Supervised Learning
National Technical University of Athens, Athens, Greece

Apr 8, 2026, cs.CVJ/K move · Enter open · S save

Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels

Yaqi Zhao, Haoliang Sun, Yating Wang, Yongshun Gong+1

School of Software, Shandong University, Jinan, China

Prompt Learning VLM Adaptation Weakly Supervised Learning

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Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream tasks. However, when only partial labels are available, its performance is often limited by label ambiguity and insufficient supervisory information. To address this issue, we propose Holistic Optimal Label Selection (HopS), leveraging the generalization ability of pre-trained feature encoders through two complementary strategies. First, we design a local density-based filter that selects the top frequent labels from the nearest neighbors' candidate sets and uses the softmax scores to identify the most plausible label, capturing structural regularities in the feature space. Second, we introduce a global selection objective based on optimal transport that maps the uniform sampling distribution to the candidate label distributions across a batch. By minimizing the expected transport cost, it can determine the most likely label assignments. These two strategies work together to provide robust label selection from both local and global perspectives. Extensive experiments on eight benchmark datasets show that HopS consistently improves performance under partial supervision and outperforms all baselines. Those results highlight the merit of holistic label selection and offer a practical solution for prompt learning in weakly supervised settings.

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