Multi-View Contrastive Learning

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13 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

ProAlign: Progressive Positional and Prototype-Guided Alignment for Aerial-Ground Person Re-Identification
Nov 3, 2026Qiao Li, Jing Chen, Cong Wu +3Person Re-Identification Cross-Modal Alignment
School of Cyber Science and Engineering, Wuhan University · National University of Singapore
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

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ProAlign: Progressive Positional and Prototype-Guided Alignment for Aerial-Ground Person Re-Identification

Qiao Li, Jing Chen, Cong Wu, Kun He+2

School of Cyber Science and Engineering, Wuhan University · National University of Singapore

Person Re-Identification Cross-Modal Alignment Multi-View Contrastive Learning Positional Encoding

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Aerial-Ground Person Re-Identification (AG-ReID) aims to match pedestrian images captured by unmanned aerial vehicles (UAVs) and ground-based surveillance cameras. The task remains highly challenging due to severe viewpoint discrepancies, frequent occlusions, and substantial domain gaps between aerial and ground imagery. Beyond these observable factors, existing methods often overlook two fundamental structural issues: cumulative positional drift during hierarchical feature extraction and semantic inconsistency across cross-view feature distributions. To address these challenges, we propose ProAlign, a Progressive Positional and Prototype-Guided Alignment Network for AG-ReID. ProAlign comprises two key components: a Layer-wise Progressive Positional Embedding (LPPE) module and a View-Aware Prototype Contrastive Learning (PCL) module. LPPE performs adaptive positional calibration throughout transformer layers by employing conditional positional encoding in shallow layers to mitigate local spatial distortions, while introducing learnable static positional embeddings in deeper layers to reinforce global semantic priors. Meanwhile, PCL maintains view-specific aerial and ground prototypes for each identity and enforces cross-view semantic consistency via a dual-prototype contrastive objective. Extensive experiments on CARGO and two real-world AG-ReID benchmarks demonstrate the effectiveness of ProAlign, achieving state-of-the-art overall retrieval performance on CARGO with improvements of 1.60% in Rank-1 accuracy and 5.01% in mAP over the strongest competing methods.