Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition (2402.14505v3)

Abstract: Recent studies show that vision models pre-trained in generic visual learning tasks with large-scale data can provide useful feature representations for a wide range of visual perception problems. However, few attempts have been made to exploit pre-trained foundation models in visual place recognition (VPR). Due to the inherent difference in training objectives and data between the tasks of model pre-training and VPR, how to bridge the gap and fully unleash the capability of pre-trained models for VPR is still a key issue to address. To this end, we propose a novel method to realize seamless adaptation of pre-trained models for VPR. Specifically, to obtain both global and local features that focus on salient landmarks for discriminating places, we design a hybrid adaptation method to achieve both global and local adaptation efficiently, in which only lightweight adapters are tuned without adjusting the pre-trained model. Besides, to guide effective adaptation, we propose a mutual nearest neighbor local feature loss, which ensures proper dense local features are produced for local matching and avoids time-consuming spatial verification in re-ranking. Experimental results show that our method outperforms the state-of-the-art methods with less training data and training time, and uses about only 3% retrieval runtime of the two-stage VPR methods with RANSAC-based spatial verification. It ranks 1st on the MSLS challenge leaderboard (at the time of submission). The code is released at https://github.com/Lu-Feng/SelaVPR.

• The paper introduces SelaVPR, a hybrid adaptation framework that integrates global and local adaptation modules to bridge pre-trained models with VPR tasks.
• It proposes a novel mutual nearest neighbor local feature loss that refines dense local feature extraction and improves matching efficiency.
• The method outperforms state-of-the-art approaches on benchmarks like Tokyo24/7 and Pitts30k while needing significantly less training data and computational resources.

Seamless Adaptation of Pre-trained Foundation Models for Visual Place Recognition

The paper "Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition" addresses a significant challenge in leveraging pre-trained vision models for Visual Place Recognition (VPR). The primary focus is on bridging the gap between the pre-training tasks and VPR to harness the full potential of pre-trained models. This paper introduces a novel method, named SelaVPR, aimed at efficiently adapting foundation models like DINOv2 for the VPR task.

Key Contributions

1. Hybrid Adaptation Framework:
   • The authors propose a hybrid adaptation mechanism that consists of global and local adaptation modules. The method employs lightweight adapters that facilitate global and local feature extraction without modifying the parameters of the pre-trained model. This hybrid adaptation efficiently utilizes the robust features of foundation models to enhance place recognition capabilities, focusing on salient landmarks crucial for identifying places.
2. Mutual Nearest Neighbor Local Feature Loss:
   • A new loss function, the mutual nearest neighbor local feature loss, is introduced to guide the local adaptation module. It ensures that the adaptation process yields effective dense local features utilized for local matching. By avoiding time-intensive spatial verification techniques such as RANSAC, the proposed method significantly reduces retrieval runtime.
3. Performance and Efficiency:
   • The proposed SelaVPR method demonstrates superior performance on various VPR benchmarks, including Tokyo24/7, MSLS, and Pitts30k, outperforming several state-of-the-art methods. Noteworthy is the method's ability to achieve these results with substantially less training data and computational requirements. For example, it utilizes only about 3% of the retrieval runtime compared to traditional two-stage VPR methods that rely on geometric verifications.

Methodology

The hybrid adaptation method leverages the Vision Transformer (ViT)-based pre-trained foundation model, DINOv2.

• Global Adaptation:
  • This involves introducing adapters within transformer blocks to adjust the global feature extraction process, ensuring that the output representation is finely tuned to the VPR task.
• Local Adaptation:
  • The local adaptation employs up-convolutional layers to upsample feature maps, enabling the model to produce dense local features essential for re-ranking in the two-stage VPR pipeline.

Implications and Future Directions

The research presents a well-structured solution to fully exploit pre-trained foundation models for VPR, efficiently bridging the pre-training fine-tuning gap. This holds substantial implications for improving VPR systems, particularly in dynamic environments with changing conditions and viewpoints. The proposed approach's efficiency and effectiveness pave the way for real-world large-scale VPR deployment and could be extended to other domain-specific recognition tasks.

Future work can explore enhancing the robustness of local feature adaptations, as well as integrating more advanced fine-tuning strategies that minimize the impact of domain shifts between pre-training and target tasks. Additionally, further exploration of the foundational models' capabilities in different environmental conditions can provide deeper insights and broader applications in diverse VPR scenarios.