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Panoptic Scene Graph Generation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13687))

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Abstract

Existing research addresses scene graph generation (SGG)—a critical technology for scene understanding in images—from a detection perspective, i.e., objects are detected using bounding boxes followed by prediction of their pairwise relationships. We argue that such a paradigm causes several problems that impede the progress of the field. For instance, bounding box-based labels in current datasets usually contain redundant classes like hairs, and leave out background information that is crucial to the understanding of context. In this work, we introduce panoptic scene graph generation (PSG), a new problem task that requires the model to generate a more comprehensive scene graph representation based on panoptic segmentations rather than rigid bounding boxes. A high-quality PSG dataset, which contains 49k well-annotated overlapping images from COCO and Visual Genome, is created for the community to keep track of its progress. For benchmarking, we build four two-stage baselines, which are modified from classic methods in SGG, and two one-stage baselines called PSGTR and PSGFormer, which are based on the efficient Transformer-based detector, i.e., DETR. While PSGTR uses a set of queries to directly learn triplets, PSGFormer separately models the objects and relations in the form of queries from two Transformer decoders, followed by a prompting-like relation-object matching mechanism. In the end, we share insights on open challenges and future directions. We invite users to explore the PSG dataset on our project page https://psgdataset.org/, and try our codebase https://github.com/Jingkang50/OpenPSG.

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Acknowledgements

This work is supported by NTU NAP, MOE AcRF Tier 2 (T2EP20221-0033), and under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).

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Authors and Affiliations

  1. S-Lab, Nanyang Technological University, Singapore, Singapore

    Jingkang Yang, Yi Zhe Ang, Zujin Guo, Kaiyang Zhou & Ziwei Liu

  2. SenseTime Research, Shenzhen, China

    Wayne Zhang

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  1. Jingkang Yang
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  2. Yi Zhe Ang
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  3. Zujin Guo
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  4. Kaiyang Zhou
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  5. Wayne Zhang
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  6. Ziwei Liu
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Correspondence to Ziwei Liu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Yang, J., Ang, Y.Z., Guo, Z., Zhou, K., Zhang, W., Liu, Z. (2022). Panoptic Scene Graph Generation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13687. Springer, Cham. https://doi.org/10.1007/978-3-031-19812-0_11

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