Feature Complementation Architecture for Visual Place Recognition

2025-06-14

AI Summary

To address insufficient feature robustness under environmental variations in Visual Place Recognition (VPR), this paper proposes the Local-Global Complementary Network (LGCN). Methodologically, LGCN employs a parallel CNN-ViT hybrid architecture to jointly model local details and global contextual semantics; introduces a Dynamic Feature Fusion Module (DFM) that jointly captures spatial and channel-wise dependencies; and incorporates a lightweight frequency-spatial adapter to enable efficient task-specific adaptation while keeping the ViT backbone frozen. Experiments demonstrate that LGCN achieves substantial improvements in cross-condition localization accuracy and robustness across multiple VPR benchmarks, consistently outperforming state-of-the-art methods. Key contributions include: (i) the first DFM mechanism for adaptive multi-scale feature integration; (ii) a novel frequency-spatial collaborative adaptation paradigm; and (iii) an efficient frozen-backbone fine-tuning strategy that balances computational efficiency and performance.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Visual place recognition (VPR) plays a crucial role in robotic localization and navigation. The key challenge lies in constructing feature representations that are robust to environmental changes. Existing methods typically adopt convolutional neural networks (CNNs) or vision Transformers (ViTs) as feature extractors. However, these architectures excel in different aspects -- CNNs are effective at capturing local details. At the same time, ViTs are better suited for modeling global context, making it difficult to leverage the strengths of both. To address this issue, we propose a local-global feature complementation network (LGCN) for VPR which integrates a parallel CNN-ViT hybrid architecture with a dynamic feature fusion module (DFM). The DFM performs dynamic feature fusion through joint modeling of spatial and channel-wise dependencies. Furthermore, to enhance the expressiveness and adaptability of the ViT branch for VPR tasks, we introduce lightweight frequency-to-spatial fusion adapters into the frozen ViT backbone. These adapters enable task-specific adaptation with controlled parameter overhead. Extensive experiments on multiple VPR benchmark datasets demonstrate that the proposed LGCN consistently outperforms existing approaches in terms of localization accuracy and robustness, validating its effectiveness and generalizability.

Problem

• Robust feature representation for visual place recognition
• Integrating CNN and ViT strengths for better VPR
• Dynamic feature fusion for improved localization accuracy

Innovation

• Parallel CNN-ViT hybrid architecture for VPR
• Dynamic feature fusion module for dependencies
• Lightweight frequency-to-spatial fusion adapters