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Component-based Attention for Large-scale Trademark Retrieval
lib:efaf478aa82c77b2 (v1.0.0)
Authors: Osman Tursun,Simon Denman,Sabesan Sivapalan,Sridha Sridharan,Clinton Fookes,Sandra Mau
ArXiv: 1811.02746
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1811.02746v2
The demand for large-scale trademark retrieval (TR) systems has significantly increased to combat the rise in international trademark infringement. Unfortunately, the ranking accuracy of current approaches using either hand-crafted or pre-trained deep convolution neural network (DCNN) features is inadequate for large-scale deployments. We show in this paper that the ranking accuracy of TR systems can be significantly improved by incorporating hard and soft attention mechanisms, which direct attention to critical information such as figurative elements and reduce attention given to distracting and uninformative elements such as text and background. Our proposed approach achieves state-of-the-art results on a challenging large-scale trademark dataset.
ArXiv: 1811.02746
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1811.02746v2
The demand for large-scale trademark retrieval (TR) systems has significantly increased to combat the rise in international trademark infringement. Unfortunately, the ranking accuracy of current approaches using either hand-crafted or pre-trained deep convolution neural network (DCNN) features is inadequate for large-scale deployments. We show in this paper that the ranking accuracy of TR systems can be significantly improved by incorporating hard and soft attention mechanisms, which direct attention to critical information such as figurative elements and reduce attention given to distracting and uninformative elements such as text and background. Our proposed approach achieves state-of-the-art results on a challenging large-scale trademark dataset.
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