Metric Learning

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6 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Latest papers 47

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Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation
May 14, 2026Zhiquan Chen, Haitao Wang, Guowei Zou +1Image Segmentation Metric Learning
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China
Beyond Binary: Reframing GUI Critique as Continuous Semantic Alignment
May 14, 2026Yuchen Sun, Pei Fu, Shaojie Zhang +6GUI Agents AI Agent Evaluation
1Shanghai Jiao Tong University · 2Xiaomi Inc.
MARGIN: Margin-Aware Regularized Geometry for Imbalanced Vulnerability Detection
May 11, 2026Yuteng Zhang, Huifang Ma, Jiahui Wei +2Class-Imbalanced Learning Software Vulnerability Detection
College of Artificial Intelligence and Computer Science, Northwest Normal University, Lanzhou 730070, China
Dynamic Meta-Metrics: Source-Sentence Conditioned Weighting for MT Evaluation
May 9, 2026Luke Zhang, Justin Vasselli, Aditya Khan +2Metric Learning MT Evaluation
University of Toronto, Canada · Nara Institute of Science and Technology, Japan · Ontario Tech University, Canada
Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis
May 8, 2026Ozgu Goksu, Nicolas PugeaultRepresentation Collapse Non-IID Federated Learning
School of Computing Science University of Glasgow
DPM++: Dynamic Masked Metric Learning for Occluded Person Re-identification
May 7, 2026Lei Tan, Yingshi Luan, Pincong Zou +2Metric Learning Person Re-Identification
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China
Geometric Kolmogorov--Arnold Network (GeoKAN)
May 7, 2026Abhijit Sen, Bikram Keshari Parida, Giridas Maiti +2Neural Surrogate Modeling Metric Learning
Department of Physics and Engineering Physics, Tulane University, New Orleans, Louisiana 70118, USA. · Institute of Applied Geosciences, Karlsruhe Institute of Technology, Karlsruhe 76131, Germany.
EGA: Adapting Frozen Encoders for Vector Search with Bounded Out-of-Distribution Degradation
May 7, 2026Dongfang ZhaoOOD Generalization Vision Foundation Model Adaptation
Tacoma School of Engineering and Technology University of Washington
Optimal Transport Audio Distance with Learned Riemannian Ground Metrics
May 7, 2026Wonwoo JeongSpeech Generation Evaluation Metric Learning
Sogang University
Beyond Rigid Geometries: The Spline-Pullback Metric for Universal Diffeomorphic SPD Representation Learning
May 6, 2026Tushar Das, Subrata Dutta, Sarmistha Neogy +1Geometric Representation Learning Metric Learning
National Institute of Technology Jamshedpur · Jadavpur University · Machine Vision and Intelligence Lab National Institute of Technology Jamshedpur
Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning
May 2, 2026Richeng Zhou, Xuelin Zhang, Liyuan LiuMetric Learning Statistical Learning Theory
Distance metric learning for conditional anomaly detection
May 1, 2026Michal Valko, Milos HauskrechtMetric Learning Anomaly Detection
University of Pittsburgh, PA
FunFace: Feature Utility and Norm Estimation for Face Recognition
Apr 29, 2026Žiga Babnik, Fadi Boutros, Naser Damer +3Metric Learning Face Recognition
University of Ljubljana, Ljubljana, Slovenia · Fraunhofer Institute for Computer Graphics Research IGD, Germany · Department of Computer Science, TU Darmstadt, Germany +1
StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition
Apr 23, 2026Kwan Yun, Changmin Lee, Ayeong Jeong +3Metric Learning Face Recognition
KAIST, South Korea
Layout-Aware Representation Learning for Open-Set ID Fraud Discovery
Apr 17, 2026Jinxing Li, Nicholas Ren, Cathy Chang +2Image Forgery Detection Visual Representation Learning
Persona Identities
Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification
Apr 16, 2026Meijia Wang, Guochao Wang, Haozhen Chu +4Frequency-Domain Feature Learning Fine-Grained Image Classification
School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China
Open-Set Vein Biometric Recognition with Deep Metric Learning
Apr 16, 2026Paweł Pilarek, Marcel Musiałek, Anna GórskaBiometric Identification Domain Generalization
Wroclaw University of Science and Technology, Wroclaw, Poland

May 14, 2026, cs.CVJ/K move · Enter open · S save

Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation

Zhiquan Chen, Haitao Wang, Guowei Zou, Hejun Wu

School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China

Image Segmentation Metric Learning Medical Image Segmentation Medical Image Representation Learning

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Accurate medical image segmentation is fundamental to precision medicine, yet robust delineation remains challenging under heterogeneous appearances, ambiguous boundaries, and large anatomical variability. Similar intensity and texture patterns between targets and surrounding tissues often lead to blurred activations and unreliable separation. We attribute these failures to representation collapse during encoding and insufficient fine grained multi scale decoding. To address these issues, we propose Med DisSeg, a dispersion driven medical image segmentation framework that jointly improves representation learning and anatomical delineation. Med DisSeg combines a lightweight Dispersive Loss with adaptive attention for fine grained structure segmentation. The Dispersive Loss enlarges inter sample margins by treating in batch hidden representations as negative pairs, producing well dispersed and boundary aware embeddings with negligible overhead. Based on these enhanced representations, the encoder strengthens structure sensitive responses, while the decoder performs adaptive multi scale calibration to preserve complementary local texture and global shape information. Extensive experiments on five datasets spanning three imaging modalities demonstrate consistent state of the art performance. Moreover, Med DisSeg achieves competitive results on multi organ CT segmentation, supporting its robustness and cross task applicability.

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