Image-Text Pre-Training for Logo Recognition
Mark Hubenthal Suren Kumar
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.