Evidential Transformers for Improved Image Retrieval

• URL: http://arxiv.org/abs/2409.01082v1
• Date: Mon, 2 Sep 2024 09:10:47 GMT
• Title: Evidential Transformers for Improved Image Retrieval
• Authors: Danilo Dordevic, Suryansh Kumar,
• Abstract summary: We introduce the Evidential Transformer, an uncertainty-driven transformer model for improved and robust image retrieval. We incorporate probabilistic methods into image retrieval, achieving robust and reliable results.
• Score: 7.397099215417549
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce the Evidential Transformer, an uncertainty-driven transformer model for improved and robust image retrieval. In this paper, we make several contributions to content-based image retrieval (CBIR). We incorporate probabilistic methods into image retrieval, achieving robust and reliable results, with evidential classification surpassing traditional training based on multiclass classification as a baseline for deep metric learning. Furthermore, we improve the state-of-the-art retrieval results on several datasets by leveraging the Global Context Vision Transformer (GC ViT) architecture. Our experimental results consistently demonstrate the reliability of our approach, setting a new benchmark in CBIR in all test settings on the Stanford Online Products (SOP) and CUB-200-2011 datasets.

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