:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Xu, Bruce Changlong, James, Jose, Ryu, Alexander
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning
Online Access:	https://arxiv.org/abs/2605.24771
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Evaluating Decision Rules Across Many Weak Experiments
by: Chou, Winston, et al.
Published: (2025)

Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations
by: Li, Ming, et al.
Published: (2024)

From Gaze to Insight: Bridging Human Visual Attention and Vision Language Model Explanation for Weakly-Supervised Medical Image Segmentation
by: Chen, Jingkun, et al.
Published: (2025)

Activation Sensitivity as a Unifying Principle for Post-Training Quantization
by: Xu, Bruce Changlong
Published: (2026)

Vision-Language Models are Strong Noisy Label Detectors
by: Wei, Tong, et al.
Published: (2024)

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook
by: Ma, Yuan, et al.
Published: (2025)

FedNoisy: Federated Noisy Label Learning Benchmark
by: Liang, Siqi, et al.
Published: (2023)

Adaptive Label Correction for Robust Medical Image Segmentation with Noisy Labels
by: Qian, Chengxuan, et al.
Published: (2025)

ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation
by: Liu, Y., et al.
Published: (2024)

Evaluating Posterior Probabilities: Decision Theory, Proper Scoring Rules, and Calibration
by: Ferrer, Luciana, et al.
Published: (2024)

Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels
by: Dong, Jiahua, et al.
Published: (2024)

Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels
by: Guo, Erjian, et al.
Published: (2025)

From Fuzzy Speech to Medical Insight: Benchmarking LLMs on Noisy Patient Narratives
by: Mama, Eden, et al.
Published: (2025)

Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation
by: Wang, Qian-Wei, et al.
Published: (2025)

EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models
by: Gao, Jun, et al.
Published: (2024)

Dirichlet-Based Prediction Calibration for Learning with Noisy Labels
by: Zong, Chen-Chen, et al.
Published: (2024)

RuleSafe-VL: Evaluating Rule-Conditioned Decision Reasoning in Vision-Language Content Moderation
by: Lu, Zhifeng, et al.
Published: (2026)

Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images
by: Adams, Jadie, et al.
Published: (2024)

INSIGHT: Explainable Weakly-Supervised Medical Image Analysis
by: Zhang, Wenbo, et al.
Published: (2024)

Contrastive Learning with Diffusion Features for Weakly Supervised Medical Image Segmentation
by: Zeng, Dewen, et al.
Published: (2025)

Benchmarking and Mitigating Sycophancy in Medical Vision Language Models
by: Xu, Juangui, et al.
Published: (2025)

From Calibration to Refinement: Seeking Certainty via Probabilistic Evidence Propagation for Noisy-Label Person Re-Identification
by: Yuan, Xin, et al.
Published: (2026)

Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation
by: Deshpande, Tanvi, et al.
Published: (2024)

Towards Weakly-Supervised Hate Speech Classification Across Datasets
by: Jin, Yiping, et al.
Published: (2023)

Weakly Supervised Ephemeral Gully Detection In Remote Sensing Images Using Vision Language Models
by: Tousi, Seyed Mohamad Ali, et al.
Published: (2025)

Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation
by: Wu, Linshan, et al.
Published: (2024)

A Bayesian Approach to Segmentation with Noisy Labels via Spatially Correlated Distributions
by: Tadokoro, Ryu, et al.
Published: (2025)

Dynamical Label Augmentation and Calibration for Noisy Electronic Health Records
by: Li, Yuhao, et al.
Published: (2025)

Weakly Supervised Label Learning Flows
by: Lu, You, et al.
Published: (2023)

Calibration-Aware Prompt Learning for Medical Vision-Language Models
by: Basu, Abhishek, et al.
Published: (2025)

Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models
by: Chen, Zhaozheng, et al.
Published: (2023)

Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift
by: Khan, Behraj, et al.
Published: (2025)

What Do Vision-Language Models Encode for Personalized Image Aesthetics Assessment?
by: Ryu, Koki, et al.
Published: (2026)

DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation
by: Pan, Qingtao, et al.
Published: (2024)

Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement
by: Ye, Liqin, et al.
Published: (2025)

Emerging Trends in Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation with Image-Level Supervision
by: Zhang, Zheyuan, et al.
Published: (2025)

Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation
by: Zhu, Lei, et al.
Published: (2025)

FNBench: Benchmarking Robust Federated Learning against Noisy Labels
by: Jiang, Xuefeng, et al.
Published: (2025)

Generalization of Medical Large Language Models through Cross-Domain Weak Supervision
by: Long, Robert, et al.
Published: (2025)

DIER ‐Net: Debiased Learning With Medical Image Noisy Label by Intrinsic and Extrinsic Regularization
by: Cheng Xue
Published: (2025)