2020/04/02 by Eu Wern Teh, Teh, Eu Wern, Terrance DeVries +3 · 4 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Multimodal Machine Learning Applications
paper · pdf · doi:10.48550/arxiv.2004.01113
openalex publication_date 2020/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of distance metric learning (DML), where the task is\nto learn an effective similarity measure between images. We revisit ProxyNCA\nand incorporate several enhancements. We find that low temperature scaling is a\nperformance-critical component and explain why it works. Besides, we also\ndiscover that Global Max Pooling works better in general when compared to\nGlobal Average Pooling. Additionally, our proposed fast moving proxies also\naddresses small gradient issue of proxies, and this component synergizes well\nwith low temperature scaling and Global Max Pooling. Our enhanced model, called\nProxyNCA++, achieves a 22.9 percentage point average improvement of Recall@1\nacross four different zero-shot retrieval datasets compared to the original\nProxyNCA algorithm. Furthermore, we achieve state-of-the-art results on the\nCUB200, Cars196, Sop, and InShop datasets, achieving Recall@1 scores of 72.2,\n90.1, 81.4, and 90.9, respectively.\n