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Domain Adaptive Hand Keypoint and Pixel Localization in the Wild

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13669))

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Abstract

We aim to improve the performance of regressing hand keypoints and segmenting pixel-level hand masks under new imaging conditions (e.g., outdoors) when we only have labeled images taken under very different conditions (e.g., indoors). In the real world, it is important that the model trained for both tasks works under various imaging conditions. However, their variation covered by existing labeled hand datasets is limited. Thus, it is necessary to adapt the model trained on the labeled images (source) to unlabeled images (target) with unseen imaging conditions. While self-training domain adaptation methods (i.e., learning from the unlabeled target images in a self-supervised manner) have been developed for both tasks, their training may degrade performance when the predictions on the target images are noisy. To avoid this, it is crucial to assign a low importance (confidence) weight to the noisy predictions during self-training. In this paper, we propose to utilize the divergence of two predictions to estimate the confidence of the target image for both tasks. These predictions are given from two separate networks, and their divergence helps identify the noisy predictions. To integrate our proposed confidence estimation into self-training, we propose a teacher-student framework where the two networks (teachers) provide supervision to a network (student) for self-training, and the teachers are learned from the student by knowledge distillation. Our experiments show its superiority over state-of-the-art methods in adaptation settings with different lighting, grasping objects, backgrounds, and camera viewpoints. Our method improves by \(4\%\) the multi-task score on HO3D compared to the latest adversarial adaptation method. We also validate our method on Ego4D, egocentric videos with rapid changes in imaging conditions outdoors.

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Acknowledgments

This work was supported by JST ACT-X Grant Number JPMJAX2007, JSPS Research Fellowships for Young Scientists, JST AIP Acceleration Research Grant Number JPMJCR20U1, and JSPS KAKENHI Grant Number JP20H04205, Japan. This work was also supported in part by a hardware donation from Yu Darvish.

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Authors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Takehiko Ohkawa, Ryosuke Furuta & Yoichi Sato

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Takehiko Ohkawa, Yu-Jhe Li, Qichen Fu & Kris M. Kitani

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  1. Takehiko Ohkawa
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  2. Yu-Jhe Li
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  4. Ryosuke Furuta
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Correspondence to Takehiko Ohkawa .

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  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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Ohkawa, T., Li, YJ., Fu, Q., Furuta, R., Kitani, K.M., Sato, Y. (2022). Domain Adaptive Hand Keypoint and Pixel Localization in the Wild. 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 13669. Springer, Cham. https://doi.org/10.1007/978-3-031-20077-9_5

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