Multi-View Contrastive Learning

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12 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Cross-polarization pairing for label-free few-shot thin-section classification
Oct 17, 2026Bingchen YanFew-Shot Image Classification Microscopy Image Analysis
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
Shared Gaussianization: What Gaussian Regularizers Certify About Contrastive Learning, and What They Miss
Oct 7, 2026Ruoyu Zhao, Yuting Chen, Jinheng Zhang +2Contrastive Learning Multi-View Contrastive Learning
City University of Hong Kong · Georgia Institute of Technology · University of Pennsylvania +2
Positive Pair Geometry Matters: Optimal Transport for Contrastive Learning of Visual Representations
Sep 21, 2026Akshit Nanda, Shahzad Ahmad, Ram Prasad PadhyContrastive Learning Data Augmentation
Department of Computer Science and Engineering, Indian Institute of Technology Bhubaneswar · Østfold University of Applied Sciences, NTNU
AnyViewDex: View-Invariant Dexterous Manipulation from RGB Observations
Sep 17, 2026Soham Patil, Om Sanjay Gunjal, Sourabh Bhosale +3Robotic Grasping Zero-Shot Robotic Manipulation
Robotics Research Center, IIIT-H, India · Department of Electronics Engineering, Veermata Jijabai Technological Institute Mumbai, India · Department of Electrical Engineering and Computer Sciences, Indian Institute of Science Education and Research Bhopal, India
Multi-View Mixture-of-Experts with Vision-Language Reranking for Cross-View Object Geo-Localization
Sep 16, 2026Xuyu Fan, Qi Ming, Zhu Han +6Multi-View Contrastive Learning Cross-View Geo-Localization
College of Computer Science, Beijing University of Technology · Zhengzhou University · Zhejiang University +3
Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning
Jun 11, 2026Paolo Muratore, Mackenzie Weygandt MathisContrastive Learning Parameter Identifiability
EPFL
Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse
May 14, 2026Yunsong Fang, Tingyu Wang, Zhedong ZhengCross-View Geo-Localization Multi-View Contrastive Learning
University of Macau, Macau SAR, China · Hangzhou Dianzi University, Hangzhou, China
ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation
Apr 15, 2026Xiaofan Zhou, Kyumin LeeMulti-View Contrastive Learning Sequential Recommendation
Worcester Polytechnic Institute, Worcester, USA
FAST3DIS: Feed-forward Anchored Scene Transformer for 3D Instance Segmentation
Mar 27, 2026Changyang Li, Xueqing Huang, Shin-Fang Chng +3Multi-View Contrastive Learning 3D Instance Segmentation
Goertek Alpha Labs
Pathology-Aware Multi-View Contrastive Learning for Patient-Independent ECG Reconstruction
Mar 18, 2026Youssef Youssef, Jitin SinglaMulti-View Contrastive Learning Electrocardiography
Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India · Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
Patent Representation Learning via Self-supervision
Nov 3, 2025You Zuo, Kim Gerdes, Éric de la Clergerie +1Contrastive Learning Representation Learning
ALMAnaCH · Inria Paris, France · Questel Paris, France +2
Divergence-Based Similarity Function for Multi-View Contrastive Learning
Jul 9, 2025Jaehyoung Jeon, Cheolsu Lim, Myungjoo KangContrastive Learning Multi-View Learning
Department of Mathematics, Seoul National University, Seoul, Korea · Research Institute of Mathematics, Seoul National University, Seoul, Korea

Oct 17, 2026J/K move · Enter open · S save

Cross-polarization pairing for label-free few-shot thin-section classification

Bingchen Yan

School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China

Few-Shot Image Classification Microscopy Image Analysis Pseudo-Labeling Multi-View Contrastive Learning

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Few-shot classifiers of rock thin sections are meta-trained on many rock types that a petrographer has identified slide by slide, and this identification is the costly step that new collections lack. We study few-shot recognition of unseen rock types when no training slide is identified, and the only information available is what the microscope records anyway: which slide an image comes from, and whether it was taken in plane-polarized (PPL) or cross-polarized (XPL) light. We first show why generic unsupervised pseudo-tasks fit this setting poorly: the positives they find without labels are either wrong or near-duplicates of the anchor, so they teach the model little. Polarized-light microscopy provides positives that avoid both problems. The PPL and XPL views of one field show the same grains, so they are correct by construction, yet they look very different. We propose cross-polarization pairing (XPP), which treats each slide as a pseudo-class and pairs its PPL and XPL views of the same field location, so that matching must rely on grain shape and texture rather than on colour. On the Nanjing University collection of 108 rock types, with support and query images taken from different slides, XPP is the best label-free method on average over three random splits, and matches a supervised model trained on 78 identified rock types. Controls show that colour augmentation and other acquisition positives do not reproduce the gain. We also show that the image-level protocol of earlier work is saturated, and propose a cross-slide protocol in its place.