Image-text Pre-training For Logo Recognition
Mark Hubenthal, Suren Kumar . 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 – 5 citations
[Paper]
Open-set logo recognition is commonly solved by first detecting possible logo regions and then matching the detected parts against an ever-evolving dataset of cropped logo images. The matching model, a metric learning problem, is especially challenging for logo recognition due to the mixture of text and symbols in logos. We propose two novel contributions to improve the matching model’s performance: (a) using image-text paired samples for pre-training, and (b) an improved metric learning loss function. A standard paradigm of fine-tuning ImageNet pre-trained models fails to discover the text sensitivity necessary to solve the matching problem effectively. This work demonstrates the importance of pre-training on image-text pairs, which significantly improves the performance of a visual embedder trained for the logo retrieval task, especially for more text-dominant classes. We construct a composite public logo dataset combining LogoDet3K, OpenLogo, and FlickrLogos-47 deemed OpenLogoDet3K47. We show that the same vision backbone pre-trained on image-text data, when fine-tuned on OpenLogoDet3K47, achieves (98.6%) recall@1, significantly improving performance over pre-training on Imagenet1K ((97.6%)). We generalize the ProxyNCA++ loss function to propose ProxyNCAHN++ which incorporates class-specific hard negative images. The proposed method sets new state-of-the-art on five public logo datasets considered, with a (3.5%) zero-shot recall@1 improvement on LogoDet3K test, (4%) on OpenLogo, (6.5%) on FlickrLogos-47, (6.2%) on Logos In The Wild, and (0.6%) on BelgaLogo.