Evidential Transformers for Improved Image Retrieval
<|reference_start|>Evidential Transformers for Improved Image Retrieval: 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.<|reference_end|>
@article{dordevic2024evidential,
    title={Evidential Transformers for Improved Image Retrieval},
    author={Danilo Dordevic, Suryansh Kumar},
    journal={arXiv preprint arXiv:2409.01082},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01082},
    primaryClass={cs.CV cs.IR cs.LG}
}