Semantically Tied Paired Cycle Consistency for Any-Shot Sketch-based Image Retrieval

• URL: http://arxiv.org/abs/2006.11397v1
• Date: Sat, 20 Jun 2020 22:43:53 GMT
• Title: Semantically Tied Paired Cycle Consistency for Any-Shot Sketch-based Image Retrieval
• Authors: Anjan Dutta and Zeynep Akata
• Abstract summary: Low-shot sketch-based image retrieval is an emerging task in computer vision. In this paper, we address any-shot, i.e. zero-shot and few-shot, sketch-based image retrieval (SBIR) tasks. For solving these tasks, we propose a semantically aligned cycle-consistent generative adversarial network (SEM-PCYC) Our results demonstrate a significant boost in any-shot performance over the state-of-the-art on the extended version of the Sketchy, TU-Berlin and QuickDraw datasets.
• Score: 55.29233996427243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-shot sketch-based image retrieval is an emerging task in computer vision, allowing to retrieve natural images relevant to hand-drawn sketch queries that are rarely seen during the training phase. Related prior works either require aligned sketch-image pairs that are costly to obtain or inefficient memory fusion layer for mapping the visual information to a semantic space. In this paper, we address any-shot, i.e. zero-shot and few-shot, sketch-based image retrieval (SBIR) tasks, where we introduce the few-shot setting for SBIR. For solving these tasks, we propose a semantically aligned paired cycle-consistent generative adversarial network (SEM-PCYC) for any-shot SBIR, where each branch of the generative adversarial network maps the visual information from sketch and image to a common semantic space via adversarial training. Each of these branches maintains cycle consistency that only requires supervision at the category level, and avoids the need of aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be class-specific. Furthermore, we propose to combine textual and hierarchical side information via an auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in any-shot SBIR performance over the state-of-the-art on the extended version of the challenging Sketchy, TU-Berlin and QuickDraw datasets.

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