Efficient large-scale image retrieval with deep feature orthogonality and Hybrid-Swin-Transformers
This work addresses efficient image retrieval for landmark identification, representing an incremental improvement with specific competition success.
The authors tackled large-scale landmark recognition and retrieval by introducing two architectures and a re-ranking method, achieving first place in the Google Landmark Competition 2021.
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.