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One-Shot Object Affordance Detection in the Wild

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Abstract

Affordance detection refers to identifying the potential action possibilities of objects in an image, which is a crucial ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we first study the challenging one-shot affordance detection problem in this paper, i.e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected. To this end, we devise a One-Shot Affordance Detection Network (OSAD-Net) that firstly estimates the human action purpose and then transfers it to help detect the common affordance from all candidate images. Through collaboration learning, OSAD-Net can capture the common characteristics between objects having the same underlying affordance and learn a good adaptation capability for perceiving unseen affordances. Besides, we build a large-scale purpose-driven affordance dataset v2 (PADv2) by collecting and labeling 30k images from 39 affordance and 103 object categories. With complex scenes and rich annotations, our PADv2 dataset can be used as a test bed to benchmark affordance detection methods and may also facilitate downstream vision tasks, such as scene understanding, action recognition, and robot manipulation. Specifically, we conducted comprehensive experiments on PADv2 dataset by including 11 advanced models from several related research fields. Experimental results demonstrate the superiority of our model over previous representative ones in terms of both objective metrics and visual quality. The benchmark suite is available at https://github.com/lhc1224/OSAD_Net.

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Notes

  1. “Detection” refers to the pixel-wise detection task, which has also been used in the area of salient object detection.

  2. It indicates our conference version model in Luo et al. (2021b)

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Acknowledgements

This work was supported by National Key R &D Program of China under Grant 2020AAA0105701, National Natural Science Foundation of China (NSFC) under Grants 61872327. Dr. Jing Zhang is supported by the ARC Project FL-170100117.

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Author notes

  1. Wei Zhai and Hongchen Luo contributed equally to this work. This work was done during Wei Zhai’s internship at JD Explore Academy.

Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Wei Zhai, Hongchen Luo & Yang Cao

  2. The University of Sydney, Sydney, Australia

    Jing Zhang & Dacheng Tao

  3. JD Explore Academy, Beijing, China

    Dacheng Tao

  4. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

    Yang Cao

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  1. Wei Zhai
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Zhai, W., Luo, H., Zhang, J. et al. One-Shot Object Affordance Detection in the Wild. Int J Comput Vis 130, 2472–2500 (2022). https://doi.org/10.1007/s11263-022-01642-4

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