Weakly Supervised Learning

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 102

All topics

Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance
May 25, 2026Xia Li, Xinran Liu, Lin Qi +1Weakly Supervised Image Segmentation Weakly Supervised Learning
School of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
ImPartial: Multi-channel Whole-Cell Segmentation using Partial Annotations
May 22, 2026Gunjan Shrivastava, Saad NadeemImage Segmentation Weakly Supervised Image Segmentation
Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
Weakly Supervised Cross-Modal Learning for 4D Radar Scene Flow Estimation
May 18, 2026Jingyun Fu, Zhiyu Xiang, Na ZhaoCross-Modal Learning Autonomous Driving Perception
Zhejiang University, Hangzhou, Zhejiang, China · Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing, Zhejiang, China · Singapore University of Technology and Design, Singapore.
Robust Audio Tagging under Class-wise Supervision Unreliability
May 17, 2026Yuanbo Hou, Zhaoyi Liu, Tong Ye +4Noisy-Label Learning Weakly Supervised Learning
Machine Learning Research Group, Engineering Science, University of Oxford, UK · KU Leuven, Belgium · GISP, Harbin Engineering University, China +2
Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes
May 15, 2026Tan-Ha Mai, Chao-Kai Chiang, Han-Hwa Shih +3Multiclass Classification Weakly Supervised Learning
1National Taiwan University · 3RIKEN Center for Advanced Intelligence Project · 2The University of Tokyo
Multi-proposal Collaboration and Multi-task Training for Weakly-supervised Video Moment Retrieval
May 14, 2026Bolin Zhang, Chao Yang, Bin Jiang +2Temporal Video Grounding Weakly Supervised Learning
College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China. · Mathematical and Data Science Center, Nagoya University, Nagoya,May 464-8601, Aichi, Japan. · Graduate School of Informatics, Nagoya University, Nagoya, 464-8601,14 Aichi, Japan.
Focused PU learning from imbalanced data
May 14, 2026Elias Zavitsanos, Georgios PaliourasClass-Imbalanced Learning Binary Classification
Institute of Informatics and Telecommunications, NCSR “Demokritos”, Patriarhou Gregoriou E and 27 Neapoleos St., Agia Paraskevi, 15310, Attica, Greece.
Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data
May 12, 2026Paul Hoareau, Kuan Yi Wang, Brandon Bujak +6Weakly Supervised Image Segmentation Weakly Supervised Semantic Segmentation
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada · ´Ecole Centrale de Lyon, Lyon, France · Mila - Quebec AI Institute, Montreal, QC, Canada +9
Unified Approach for Weakly Supervised Multicalibration
May 11, 2026Futoshi Futami, Takashi IshidaPost-Hoc Calibration Model Calibration
The University of Osaka / RIKEN AIP / The University of Tokyo · The University of Tokyo / RIKEN AIP
Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale
May 10, 2026Yong FuWeakly Supervised Learning Physics-Informed ML
Substratum Labs, Inc.
WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records
May 10, 2026Ruan Dong, Yuanyun Zhang, Shi LiHealthcare Multi-View Learning
University of Science and Technology of China · Columbia University
Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation
May 9, 2026Yutszyuk Wong, Wentai Wu, Yuen-Ying Yeung +1Online Anomaly Detection Anomaly Localization
Jinan University · Guangzhou, China · South China University of Technology
EAR: Enhancing Uni-Modal Representations for Weakly Supervised Audio-Visual Video Parsing
May 9, 2026Huilai Li, Xiaomeng Di, Ying Xing +3Audio-Visual Understanding Weakly Supervised Learning
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
Risk-Consistent Multiclass Learning from Random Label-Subset Membership Queries
May 8, 2026Jiaxu Su, Junpeng Li, Changchun Hua +1Multiclass Classification Weakly Supervised Learning
Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China
Better Protein Function Prediction by Modeling Survivorship Bias
May 7, 2026Zhongmou Chao, Poompol Buathong, Ekaterina Selivanovitch +2Weakly Supervised Learning Molecular Property Prediction
Smith School of Chemical and Biomolecular Engineering, Cornell University, USA · Center for Applied Mathematics, Cornell University, USA · School of Operations Research and Information Engineering, Cornell University, USA
On the Blessing of Pre-training in Weak-to-Strong Generalization
May 7, 2026Wei Yao, Wang Zhaoyang, Gengze Xu +5Language Model Pretraining Neural Network Generalization
Renmin University of China · Shanghai Jiao Tong University · Tongji University +1
In-Context Positive-Unlabeled Learning
May 7, 2026Siyan Liu, Yi Chang, Manli Cheng +2Binary Classification Weakly Supervised Learning
Department of Statistics and Actuarial Science, University of Waterloo
Weakly Supervised Concept Learning for Object-centric Visual Reasoning
May 5, 2026Sparsh Tiwari, Bettina Finzel, Gesina SchwalbeVisual Reasoning Object-Centric Representation Learning
University of Lübeck, Germany · University of Bamberg, Germany · University of Ulm, Germany
Label-Efficient School Detection from Aerial Imagery via Weakly Supervised Pretraining and Fine-Tuning
May 5, 2026Zakarya Elmimouni, Fares Fourati, Mohamed-Slim AlouiniWeakly Supervised Learning Object Detection
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Learning to Segment using Summary Statistics and Weak Supervision
May 4, 2026Omkar Kulkarni, Edward Raff, Tim OatesUltrasound Image Segmentation Image Segmentation
University of Maryland, Baltimore County, Baltimore MD, USA · Crowdstrike, Austin TX, USA
ViewSAM: Learning View-aware Cross-modal Semantics for Weakly Supervised Cross-view Referring Multi-Object Tracking
May 4, 2026Jiawei Ge, Xintian Zhang, Jiuxin Cao +9Cross-Modal Alignment Weakly Supervised Learning
Southeast University · Universität der Bundeswehr München · Queen Mary, University of London +2
Uncertainty-Aware Trip Purpose Inference from GPS Trajectories via POI Semantic Zones and Pareto Calibration
May 2, 2026Bo Yang, Haoxuan Ma, Yifan Liu +4Uncertainty Quantification Weakly Supervised Learning
1UCLA Mobility Lab, Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, USA. · 2Novateur Research Solutions, Ashburn, VA, USA.
MemeScouts@LT-EDI 2026: Asking the Right Questions -- Prompted Weak Supervision for Meme Hate Speech Detection
Apr 27, 2026Ivo Bueno, Lea Hirlimann, Enkelejda KasneciHate Speech Detection Weakly Supervised Learning
Technical University of Munich · Munich Center for Machine Learning (MCML) · LMU Munich
From Noisy Historical Maps to Time-Series Oil Palm Mapping Without Annotation in Malaysia and Indonesia (2020-2024)
Apr 26, 2026Nuttaset Kuapanich, Juepeng Zheng, Bohan Shi +6Remote Sensing Image Segmentation Agricultural Remote Sensing
School of Artificial Intelligence, Sun Yat-Sen University, Zhuhai, China. · National Supercomputing Center in Shenzhen, Shenzhen, China. · Khoury College of Computer Sciences, Northeastern University, Boston, United States of America. +1
Weakly Supervised Multicenter Nancy Index Scoring in Ulcerative Colitis Using Foundation Models
Apr 26, 2026Adam Kukučka, Ondřej Fabián, Vít Musil +1Computational Pathology Pathology Foundation Models
Masaryk University, Faculty of Informatics, Brno, Czech Republic · Institute of Clinical and Experimental Medicine, Prague, Czech Republic
LAF-Based Evaluation and UTTL-Based Learning Strategies with MIATTs
Apr 22, 2026Yongquan YangWeakly Supervised Learning Automated Evaluation
Institute of Sciences for AI, Chengdu, Sichuan, China
When Can LLMs Learn to Reason with Weak Supervision?
Apr 20, 2026Salman Rahman, Jingyan Shen, Anna Mordvina +3Supervised Fine-Tuning RL for Language Model Reasoning
University of California, Los Angeles · New York University · Google
Weakly-Supervised Referring Video Object Segmentation through Text Supervision
Apr 20, 2026Miaojing Shi, Jun Huang, Zijie Yue +1Video Object Segmentation Referring Video Object Segmentation
College of Electronic and Information Engineering, Tongji University
A proposal for PU classification under Non-SCAR using clustering and logistic model
Apr 18, 2026Konrad Furmanczyk, Kacper PaczutkowskiLogistic Regression Weakly Supervised Learning
Institute of Information Technology, Warsaw University of Life Sciences, Warsaw, Poland
MapSR: Prompt-Driven Land Cover Map Super-Resolution via Vision Foundation Models
Apr 16, 2026Ruiqi Wang, Qi Yu, Jie Ma +1Land Cover Classification Remote Sensing
School of Information Science and Technology, Beijing Foreign Studies University, Beijing 100089, China

May 25, 2026, cs.CVJ/K move · Enter open · S save

Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance

Xia Li, Xinran Liu, Lin Qi, Junyu Dong

School of Computer Science and Technology, Ocean University of China, Qingdao 266100, China

Weakly Supervised Image Segmentation Weakly Supervised Learning Camouflaged Object Detection

Open paper AskPDF arXiv

Camouflaged object detection (COD) from a single image is a challenging task due to the high similarity between objects and their surroundings. Existing fully supervised methods require labor-intensive pixel-level annotations, making weakly supervised methods a viable compromise that balances accuracy and annotation efficiency. However, weakly supervised methods often experience performance degradation due to the use of coarse annotations. In this paper, we introduce a new weakly supervised approach for camouflaged object detection to overcome these limitations. Specifically, we propose a novel network, MGNet, which tackles edge ambiguity and missed detections by utilizing initial masks generated by our custom-designed Cascaded Mask Decoder (CMD) to guide the segmentation process and enhance edge predictions. We introduce a Context Enhancement Module(CEM) to reduce the missing detection, and a Mask-guided Feature Aggregation Module (MFAM) for effective feature aggregation. For the weak supervision challenge, we propose BoxSAM, which leverages the Segment Anything Model (SAM) with bounding-box prompts to generate pseudo-labels. By employing a redundant processing strategy, high quality pixel-level pseudo-labels are provided for training MGNet. Extensive experiments demonstrate that our method delivers competitive performance against current state-of-the-art methods.

Momentum

Related topics

Latest papers 102