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Learning Dense Correspondences between Photos and Sketches
Xuanchen Lu · Xiaolong Wang · Judith E. Fan

Thu Jul 27 04:30 PM -- 06:00 PM (PDT) @ Exhibit Hall 1 #137
Event URL: https://photo-sketch-correspondence.github.io »

Humans effortlessly grasp the connection between sketches and real-world objects, even when these sketches are far from realistic. Moreover, human sketch understanding goes beyond categorization -- critically, it also entails understanding how individual elements within a sketch correspond to parts of the physical world it represents. What are the computational ingredients needed to support this ability? Towards answering this question, we make two contributions: first, we introduce a new sketch-photo correspondence benchmark, PSC6k, containing 150K annotations of 6250 sketch-photo pairs across 125 object categories, augmenting the existing Sketchy dataset with fine-grained correspondence metadata. Second, we propose a self-supervised method for learning dense correspondences between sketch-photo pairs, building upon recent advances in correspondence learning for pairs of photos. Our model uses a spatial transformer network to estimate the warp flow between latent representations of a sketch and photo extracted by a contrastive learning-based ConvNet backbone. We found that this approach outperformed several strong baselines and produced predictions that were quantitatively consistent with other warp-based methods. However, our benchmark also revealed systematic differences between predictions of the suite of models we tested and those of humans. Taken together, our work suggests a promising path towards developing artificial systems that achieve more human-like understanding of visual images at different levels of abstraction. Project page: https://photo-sketch-correspondence.github.io

Author Information

Xuanchen Lu (University of California, San Diego)
Xiaolong Wang (UC San Diego)
Xiaolong Wang

Our group has a broad interest around the directions of Computer Vision, Machine Learning and Robotics. Our focus is on learning 3D and dynamics representations through videos and physical robotic interaction data. We explore various means of supervision signals from the data itself, language, and common sense knowledge. We leverage these comprehensive representations to facilitate the learning of robot skills, with the goal of generalizing the robot to interact effectively with a wide range of objects and environments in the real physical world. Please check out our individual research topic of Self-Supervised Learning, Video Understanding, Common Sense Reasoning, RL and Robotics, 3D Interaction, Dexterous Hand.

Judith E. Fan (Stanford University)

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