Looking Alike From Far to Near: Enhancing Cross-Resolution Re-Identification via Feature Vector Panning

2025-10-01

AI Summary

In surveillance scenarios, large variations in camera distances cause substantial resolution disparities among pedestrian images, severely degrading cross-resolution person re-identification (ReID) performance. This work is the first to empirically identify and validate a strong semantic directional bias in the ReID feature space that correlates with image resolution. To address this, we propose Vector-based Progressive Feature Alignment (VPFA), a lightweight framework that explicitly aligns low-resolution (LR) and high-resolution (HR) features by modeling this directional bias—eliminating reliance on computationally expensive super-resolution preprocessing or joint LR/HR training. Leveraging Canonical Correlation Analysis (CCA) and Pearson correlation statistics, we rigorously verify the directional separability of resolution-induced bias. VPFA embeds a learnable vector translation module into standard backbone networks. On multiple cross-resolution ReID benchmarks, VPFA achieves state-of-the-art performance with significantly lower computational overhead, demonstrating both superior accuracy and efficiency.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Feature Construction/Reformulation
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In surveillance scenarios, varying camera distances cause significant differences among pedestrian image resolutions, making it hard to match low-resolution (LR) images with high-resolution (HR) counterparts, limiting the performance of Re-Identification (ReID) tasks. Most existing Cross-Resolution ReID (CR-ReID) methods rely on super-resolution (SR) or joint learning for feature compensation, which increases training and inference complexity and has reached a performance bottleneck in recent studies. Inspired by semantic directions in the word embedding space, we empirically discover that semantic directions implying resolution differences also emerge in the feature space of ReID, and we substantiate this finding from a statistical perspective using Canonical Correlation Analysis and Pearson Correlation Analysis. Based on this interesting finding, we propose a lightweight and effective Vector Panning Feature Alignment (VPFA) framework, which conducts CR-ReID from a novel perspective of modeling the resolution-specific feature discrepancy. Extensive experimental results on multiple CR-ReID benchmarks show that our method significantly outperforms previous state-of-the-art baseline models while obtaining higher efficiency, demonstrating the effectiveness and superiority of our model based on the new finding in this paper.

Problem

• Matching low-resolution with high-resolution pedestrian images
• Overcoming performance bottlenecks in cross-resolution re-identification
• Reducing complexity while improving feature alignment efficiency

Innovation

• Vector Panning Feature Alignment for resolution discrepancy
• Lightweight framework modeling resolution-specific feature differences
• Semantic direction analysis in feature space for ReID