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Hallucinating Visual Instances in Total Absentia

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12350))

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Abstract

In this paper, we investigate a new visual restoration task, termed as hallucinating visual instances in total absentia (HVITA). Unlike conventional image inpainting task that works on images with only part of a visual instance missing, HVITA concerns scenarios where an object is completely absent from the scene. This seemingly minor difference in fact makes the HVITA a much challenging task, as the restoration algorithm would have to not only infer the category of the object in total absentia, but also hallucinate an object of which the appearance is consistent with the background. Towards solving HVITA, we propose an end-to-end deep approach that explicitly looks into the global semantics within the image. Specifically, we transform the input image to a semantic graph, wherein each node corresponds to a detected object in the scene. We then adopt a Graph Convolutional Network on top of the scene graph to estimate the category of the missing object in the masked region, and finally introduce a Generative Adversarial Module to carry out the hallucination. Experiments on COCO, Visual Genome and NYU Depth v2 datasets demonstrate that the proposed approach yields truly encouraging and visually plausible results.

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Acknowledgement

This research was supported by Australian Research Council Projects FL-170100117, DP-180103424, LE-200100049 and the startup funding of Stevens Institute of Technology.

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Authors and Affiliations

  1. Faculty of Engineering, School of Computer Science, UBTECH Sydney AI Centre, The University of Sydney, Darlington, NSW, 2008, Australia

    Jiayan Qiu & Dacheng Tao

  2. Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Yiding Yang & Xinchao Wang

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  1. Jiayan Qiu
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  2. Yiding Yang
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  3. Xinchao Wang
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  4. Dacheng Tao
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Correspondence to Dacheng Tao .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Qiu, J., Yang, Y., Wang, X., Tao, D. (2020). Hallucinating Visual Instances in Total Absentia. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12350. Springer, Cham. https://doi.org/10.1007/978-3-030-58558-7_16

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