Knowledge Mining with Scene Text for Fine-Grained Recognition

• URL: http://arxiv.org/abs/2203.14215v1
• Date: Sun, 27 Mar 2022 05:54:00 GMT
• Title: Knowledge Mining with Scene Text for Fine-Grained Recognition
• Authors: Hao Wang, Junchao Liao, Tianheng Cheng, Zewen Gao, Hao Liu, Bo Ren, Xiang Bai, Wenyu Liu
• Abstract summary: We propose an end-to-end trainable network that mines implicit contextual knowledge behind scene text image. We employ KnowBert to retrieve relevant knowledge for semantic representation and combine it with image features for fine-grained classification. Our method outperforms the state-of-the-art by 3.72% mAP and 5.39% mAP, respectively.
• Score: 53.74297368412834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, the semantics of scene text has been proven to be essential in fine-grained image classification. However, the existing methods mainly exploit the literal meaning of scene text for fine-grained recognition, which might be irrelevant when it is not significantly related to objects/scenes. We propose an end-to-end trainable network that mines implicit contextual knowledge behind scene text image and enhance the semantics and correlation to fine-tune the image representation. Unlike the existing methods, our model integrates three modalities: visual feature extraction, text semantics extraction, and correlating background knowledge to fine-grained image classification. Specifically, we employ KnowBert to retrieve relevant knowledge for semantic representation and combine it with image features for fine-grained classification. Experiments on two benchmark datasets, Con-Text, and Drink Bottle, show that our method outperforms the state-of-the-art by 3.72\% mAP and 5.39\% mAP, respectively. To further validate the effectiveness of the proposed method, we create a new dataset on crowd activity recognition for the evaluation. The source code and new dataset of this work are available at https://github.com/lanfeng4659/KnowledgeMiningWithSceneText.

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