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Efficient large-scale image retrieval with deep feature orthogonality and Hybrid-Swin-Transformers

2021-10-07 20:41:13
Christof Henkel

Abstract

We present an efficient end-to-end pipeline for largescale landmark recognition and retrieval. We show how to combine and enhance concepts from recent research in image retrieval and introduce two architectures especially suited for large-scale landmark identification. A model with deep orthogonal fusion of local and global features (DOLG) using an EfficientNet backbone as well as a novel Hybrid-Swin-Transformer is discussed and details how to train both architectures efficiently using a step-wise approach and a sub-center arcface loss with dynamic margins are provided. Furthermore, we elaborate a novel discriminative re-ranking methodology for image retrieval. The superiority of our approach was demonstrated by winning the recognition and retrieval track of the Google Landmark Competition 2021.

Abstract (translated)

URL

https://arxiv.org/abs/2110.03786

PDF

https://arxiv.org/pdf/2110.03786.pdf


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