EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition

2024-05-28

arXiv.org

AI Summary

To address challenges in visual place recognition (VPR)—including occlusion, day–night, and seasonal variations—this paper proposes a zero-shot method that leverages pre-trained Vision Transformers (e.g., DINOv2) without fine-tuning. The method introduces three key innovations: (1) a novel zero-shot re-ranking mechanism based on self-attention layer features to enhance matching discriminability; (2) single-stage cross-layer pooling to generate ultra-compact 128-dimensional global descriptors; and (3) fusion of local base features with global descriptors to improve robustness and cross-domain generalization. Evaluated on standard VPR benchmarks, the approach significantly outperforms existing zero-shot methods and achieves performance on par with state-of-the-art supervised approaches. This demonstrates an effective pathway to unlock the inherent structural potential of frozen ViT models for VPR, without requiring task-specific adaptation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Representation Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The task of Visual Place Recognition (VPR) is to predict the location of a query image from a database of geo-tagged images. Recent studies in VPR have highlighted the significant advantage of employing pre-trained foundation models like DINOv2 for the VPR task. However, these models are often deemed inadequate for VPR without further fine-tuning on VPR-specific data. In this paper, we present an effective approach to harness the potential of a foundation model for VPR. We show that features extracted from self-attention layers can act as a powerful re-ranker for VPR, even in a zero-shot setting. Our method not only outperforms previous zero-shot approaches but also introduces results competitive with several supervised methods. We then show that a single-stage approach utilizing internal ViT layers for pooling can produce global features that achieve state-of-the-art performance, with impressive feature compactness down to 128D. Moreover, integrating our local foundation features for re-ranking further widens this performance gap. Our method also demonstrates exceptional robustness and generalization, setting new state-of-the-art performance, while handling challenging conditions such as occlusion, day-night transitions, and seasonal variations.

Problem

• Pre-trained Models
• Visual Place Recognition (VPR)
• Robustness to Environmental Changes

Innovation

• Self-Attention Layer Utilization
• Zero-Shot Learning Enhancement
• Efficient Global Feature Extraction