Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings

• URL: http://arxiv.org/abs/2303.11502v3
• Date: Thu, 30 Mar 2023 15:08:36 GMT
• Title: Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings
• Authors: Ayan Kumar Bhunia, Subhadeep Koley, Amandeep Kumar, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song
• Abstract summary: We study how sketches can be used as a weak label to detect salient objects present in an image. To accomplish this, we introduce a photo-to-sketch generation model that aims to generate sequential sketch coordinates corresponding to a given visual photo. Tests prove our hypothesis and delineate how our sketch-based saliency detection model gives a competitive performance compared to the state-of-the-art.
• Score: 99.9788496281408
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Human sketch has already proved its worth in various visual understanding tasks (e.g., retrieval, segmentation, image-captioning, etc). In this paper, we reveal a new trait of sketches - that they are also salient. This is intuitive as sketching is a natural attentive process at its core. More specifically, we aim to study how sketches can be used as a weak label to detect salient objects present in an image. To this end, we propose a novel method that emphasises on how "salient object" could be explained by hand-drawn sketches. To accomplish this, we introduce a photo-to-sketch generation model that aims to generate sequential sketch coordinates corresponding to a given visual photo through a 2D attention mechanism. Attention maps accumulated across the time steps give rise to salient regions in the process. Extensive quantitative and qualitative experiments prove our hypothesis and delineate how our sketch-based saliency detection model gives a competitive performance compared to the state-of-the-art.

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