Multi-View Active Fine-Grained Recognition

• URL: http://arxiv.org/abs/2206.01153v1
• Date: Thu, 2 Jun 2022 17:12:14 GMT
• Title: Multi-View Active Fine-Grained Recognition
• Authors: Ruoyi Du, Wenqing Yu, Heqing Wang, Dongliang Chang, Ting-En Lin, Yongbin Li, Zhanyu Ma
• Abstract summary: Fine-grained visual classification (FGVC) is being developed for decades. Discriminative information is not only present within seen local regions but also hides in other unseen perspectives. We propose a policy-gradient-based framework to achieve efficient recognition with active view selection.
• Score: 29.980409725777292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As fine-grained visual classification (FGVC) being developed for decades, great works related have exposed a key direction -- finding discriminative local regions and revealing subtle differences. However, unlike identifying visual contents within static images, for recognizing objects in the real physical world, discriminative information is not only present within seen local regions but also hides in other unseen perspectives. In other words, in addition to focusing on the distinguishable part from the whole, for efficient and accurate recognition, it is required to infer the key perspective with a few glances, e.g., people may recognize a "Benz AMG GT" with a glance of its front and then know that taking a look at its exhaust pipe can help to tell which year's model it is. In this paper, back to reality, we put forward the problem of active fine-grained recognition (AFGR) and complete this study in three steps: (i) a hierarchical, multi-view, fine-grained vehicle dataset is collected as the testbed, (ii) a simple experiment is designed to verify that different perspectives contribute differently for FGVC and different categories own different discriminative perspective, (iii) a policy-gradient-based framework is adopted to achieve efficient recognition with active view selection. Comprehensive experiments demonstrate that the proposed method delivers a better performance-efficient trade-off than previous FGVC methods and advanced neural networks.

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