Supervised Fine-tuning Evaluation for Long-term Visual Place Recognition

• URL: http://arxiv.org/abs/2211.07696v1
• Date: Mon, 14 Nov 2022 19:16:21 GMT
• Title: Supervised Fine-tuning Evaluation for Long-term Visual Place Recognition
• Authors: Farid Alijani and Esa Rahtu
• Abstract summary: We present a comprehensive study on the utility of deep convolutional neural networks with two state-of-the-art pooling layers. We compare deep learned global features with three different loss functions, e.g. triplet, contrastive and ArcFace, for learning the parameters of the architectures. Our investigation demonstrates that fine tuning architectures with ArcFace loss in an end-to-end manner outperforms other two losses by approximately 14% in outdoor and 12% in indoor datasets.
• Score: 14.632777952261716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present a comprehensive study on the utility of deep convolutional neural networks with two state-of-the-art pooling layers which are placed after convolutional layers and fine-tuned in an end-to-end manner for visual place recognition task in challenging conditions, including seasonal and illumination variations. We compared extensively the performance of deep learned global features with three different loss functions, e.g. triplet, contrastive and ArcFace, for learning the parameters of the architectures in terms of fraction of the correct matches during deployment. To verify effectiveness of our results, we utilized two real world datasets in place recognition, both indoor and outdoor. Our investigation demonstrates that fine tuning architectures with ArcFace loss in an end-to-end manner outperforms other two losses by approximately 1~4% in outdoor and 1~2% in indoor datasets, given certain thresholds, for the visual place recognition tasks.

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