Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval

• URL: http://arxiv.org/abs/2002.10310v4
• Date: Mon, 11 May 2020 18:32:08 GMT
• Title: Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval
• Authors: Ayan Kumar Bhunia, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song
• Abstract summary: Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user's query sketch. We reformulate the conventional FG-SBIR framework to tackle these challenges. We propose an on-the-fly design that starts retrieving as soon as the user starts drawing.
• Score: 203.2520862597357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user's query sketch. Its widespread applicability is however hindered by the fact that drawing a sketch takes time, and most people struggle to draw a complete and faithful sketch. In this paper, we reformulate the conventional FG-SBIR framework to tackle these challenges, with the ultimate goal of retrieving the target photo with the least number of strokes possible. We further propose an on-the-fly design that starts retrieving as soon as the user starts drawing. To accomplish this, we devise a reinforcement learning-based cross-modal retrieval framework that directly optimizes rank of the ground-truth photo over a complete sketch drawing episode. Additionally, we introduce a novel reward scheme that circumvents the problems related to irrelevant sketch strokes, and thus provides us with a more consistent rank list during the retrieval. We achieve superior early-retrieval efficiency over state-of-the-art methods and alternative baselines on two publicly available fine-grained sketch retrieval datasets.

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