Sketches in Computer Vision

Sketches have emerged as a powerful tool in computer vision, offering a unique modality that differs from traditional photos by focusing on simple, abstract representations. Their utility spans various applications such as sketch-based image retrieval (SBIR), sketch recognition, and sketch generation, where they effectively capture fine-grained details and enable cross-modal representation learning. Notable developments include Sketch-a-Net for sketch recognition, Sketch-RNN for sketch generation, and SketchyGAN for sketch-to-photo synthesis. The field has also seen advancements like self-supervised learning to reduce annotation needs, and sketch-photo joint learning for tasks like image manipulation and 3D shape modeling. Despite challenges such as style diversity and noisy strokes, sketches continue to gain traction in domains like e-commerce and AR/VR. Research is pushing boundaries with innovations like sketch-based 3D vision and the integration of sketches for few-shot model adaptation, highlighting their potential for broader commercial and artistic applications.

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