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Generative Meta-Adversarial Network for Unseen Object Navigation

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Object navigation is a task to let the agent navigate to a target object. Prevailing works attempt to expand navigation ability in new environments and achieve reasonable performance on the seen object categories that have been observed in training environments. However, this setting is somewhat limited in real world scenario, where navigating to unseen object categories is generally unavoidable. In this paper, we focus on the problem of navigating to unseen objects in new environments only based on limited training knowledge. Same as the common ObjectNav tasks, our agent still gets the egocentric observation and target object category as the input and does not require any extra inputs. Our solution is to let the agent “imagine" the unseen object by synthesizing features of the target object. We propose a generative meta-adversarial network (GMAN), which is mainly composed of a feature generator and an environmental meta discriminator, aiming to generate features for unseen objects and new environments in two steps. The former generates the initial features of the unseen objects based on the semantic embedding of the object category. The latter enables the generator to further learn the background characteristics of the new environment, progressively adapting the generated features to approximate the real features of the target object. The adapted features serve as a more specific representation of the target to guide the agent. Moreover, to fast update the generator with a few observations, the entire adversarial framework is learned in the gradient-based meta-learning manner. The experimental results on AI2THOR and RoboTHOR simulators demonstrate the effectiveness of the proposed method in navigating to unseen object categories. The code is available at https://github.com/sx-zhang/GMAN.git.

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Acknowledgement

This work was supported by National Key Research and Development Project of New Generation Artificial Intelligence of China, under Grant 2018AAA0102500, in part by the National Natural Science Foundation of China under Grant 62125207, 62032022, 61902378 and U1936203, in part by Beijing Natural Science Foundation under Grant Z190020, in part by the National Postdoctoral Program for Innovative Talents under Grant BX201700255.

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Authors and Affiliations

  1. Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS), Institute of Computing Technology (ICT), Beijing, China

    Sixian Zhang, Weijie Li, Xinhang Song, Yubing Bai & Shuqiang Jiang

  2. University of Chinese Academy of Sciences (UCAS), Beijing, China

    Sixian Zhang, Weijie Li, Xinhang Song, Yubing Bai & Shuqiang Jiang

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  1. Sixian Zhang
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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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Zhang, S., Li, W., Song, X., Bai, Y., Jiang, S. (2022). Generative Meta-Adversarial Network for Unseen Object Navigation. 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 13699. Springer, Cham. https://doi.org/10.1007/978-3-031-19842-7_18

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