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LVLM-empowered Multi-modal Representation Learning for Visual Place Recognition

Teng Wang, Lingquan Meng, Lei Cheng, Changyin Sun

TL;DR

This work tackles visual place recognition under severe appearance and viewpoint changes by introducing LVL3M-VPR, a multi-modal framework that fuses LVLM-generated text descriptions with visual features to form discriminative global descriptors. It employs an attention-based text recalibration (AT-REC) and a cross-attention multi-modal fusion (CA-MMF) module to enable efficient, robust integration of modalities. Experiments across Pitts250k, MSLS, SPED, and Nordland demonstrate state-of-the-art recall with substantially smaller descriptor sizes, highlighting the robustness provided by textual scene understanding. The approach showcases the practical potential of leveraging language descriptions for VPR, with implications for memory-efficient, robust place recognition in real-world robotics and navigation systems.

Abstract

Visual place recognition (VPR) remains challenging due to significant viewpoint changes and appearance variations. Mainstream works tackle these challenges by developing various feature aggregation methods to transform deep features into robust and compact global representations. Unfortunately, satisfactory results cannot be achieved under challenging conditions. We start from a new perspective and attempt to build a discriminative global representations by fusing image data and text descriptions of the the visual scene. The motivation is twofold: (1) Current Large Vision-Language Models (LVLMs) demonstrate extraordinary emergent capability in visual instruction following, and thus provide an efficient and flexible manner in generating text descriptions of images; (2) The text descriptions, which provide high-level scene understanding, show strong robustness against environment variations. Although promising, leveraging LVLMs to build multi-modal VPR solutions remains challenging in efficient multi-modal fusion. Furthermore, LVLMs will inevitably produces some inaccurate descriptions, making it even harder. To tackle these challenges, we propose a novel multi-modal VPR solution. It first adapts pre-trained visual and language foundation models to VPR for extracting image and text features, which are then fed into the feature combiner to enhance each other. As the main component, the feature combiner first propose a token-wise attention block to adaptively recalibrate text tokens according to their relevance to the image data, and then develop an efficient cross-attention fusion module to propagate information across different modalities. The enhanced multi-modal features are compressed into the feature descriptor for performing retrieval. Experimental results show that our method outperforms state-of-the-art methods by a large margin with significantly smaller image descriptor dimension.

LVLM-empowered Multi-modal Representation Learning for Visual Place Recognition

TL;DR

This work tackles visual place recognition under severe appearance and viewpoint changes by introducing LVL3M-VPR, a multi-modal framework that fuses LVLM-generated text descriptions with visual features to form discriminative global descriptors. It employs an attention-based text recalibration (AT-REC) and a cross-attention multi-modal fusion (CA-MMF) module to enable efficient, robust integration of modalities. Experiments across Pitts250k, MSLS, SPED, and Nordland demonstrate state-of-the-art recall with substantially smaller descriptor sizes, highlighting the robustness provided by textual scene understanding. The approach showcases the practical potential of leveraging language descriptions for VPR, with implications for memory-efficient, robust place recognition in real-world robotics and navigation systems.

Abstract

Visual place recognition (VPR) remains challenging due to significant viewpoint changes and appearance variations. Mainstream works tackle these challenges by developing various feature aggregation methods to transform deep features into robust and compact global representations. Unfortunately, satisfactory results cannot be achieved under challenging conditions. We start from a new perspective and attempt to build a discriminative global representations by fusing image data and text descriptions of the the visual scene. The motivation is twofold: (1) Current Large Vision-Language Models (LVLMs) demonstrate extraordinary emergent capability in visual instruction following, and thus provide an efficient and flexible manner in generating text descriptions of images; (2) The text descriptions, which provide high-level scene understanding, show strong robustness against environment variations. Although promising, leveraging LVLMs to build multi-modal VPR solutions remains challenging in efficient multi-modal fusion. Furthermore, LVLMs will inevitably produces some inaccurate descriptions, making it even harder. To tackle these challenges, we propose a novel multi-modal VPR solution. It first adapts pre-trained visual and language foundation models to VPR for extracting image and text features, which are then fed into the feature combiner to enhance each other. As the main component, the feature combiner first propose a token-wise attention block to adaptively recalibrate text tokens according to their relevance to the image data, and then develop an efficient cross-attention fusion module to propagate information across different modalities. The enhanced multi-modal features are compressed into the feature descriptor for performing retrieval. Experimental results show that our method outperforms state-of-the-art methods by a large margin with significantly smaller image descriptor dimension.
Paper Structure (15 sections, 3 equations, 6 figures, 7 tables)

This paper contains 15 sections, 3 equations, 6 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology
  4. Experiments
  5. Datasets and Evaluation Metrics.
  6. Implementation Details.
  7. Comparison with State-of-the-Art Methods
  8. Qualitative Results
  9. Ablation Studies
  10. Conclusion and Limitation
  11. Final Feature Descriptor for Retrieval
  12. Number of Cross-attention Fusion Layers
  13. Performance Evaluation on Benchmark Nordland
  14. Visualization of Attention Maps from AT-REC
  15. Deep Insight into the Performance of LLaMA-Adapter V2 on VPR

Figures (6)

  • Figure 1: The visual understanding examples of LLaMA-Adapter V2 Gao2023. When presented with a prompt, it could provide reasonable descriptions of the scene although some inaccurate details marked in red are also given. Besides, since language description entails identifying objects and their relative spatial relations in the scene, they show strong robustness against environmental variations.
  • Figure 2: The overall framework of the proposed multi-modal VPR model.
  • Figure 3: Comparison of retrieval results under challenging conditions. Red box and green box indicate a wrong and correct match, respectively.
  • Figure 4: Visualization of the learned bi-directional attention maps within CA-MMF component.
  • Figure 5: Visualization of the learned attention maps within AT-REC component.
  • ...and 1 more figures