Semi-Supervised Learning of Optical Flow by Flow Supervisor

• URL: http://arxiv.org/abs/2207.10314v1
• Date: Thu, 21 Jul 2022 06:11:52 GMT
• Title: Semi-Supervised Learning of Optical Flow by Flow Supervisor
• Authors: Woobin Im, Sebin Lee, Sung-Eui Yoon
• Abstract summary: We propose a practical fine tuning method to adapt a pretrained model to a target dataset without ground truth flows. This design is aimed at stable convergence and better accuracy over conventional self-supervision methods. We achieve meaningful improvements over state-of-the-art optical flow models on Sintel and KITTI benchmarks.
• Score: 16.406213579356795
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: A training pipeline for optical flow CNNs consists of a pretraining stage on a synthetic dataset followed by a fine tuning stage on a target dataset. However, obtaining ground truth flows from a target video requires a tremendous effort. This paper proposes a practical fine tuning method to adapt a pretrained model to a target dataset without ground truth flows, which has not been explored extensively. Specifically, we propose a flow supervisor for self-supervision, which consists of parameter separation and a student output connection. This design is aimed at stable convergence and better accuracy over conventional self-supervision methods which are unstable on the fine tuning task. Experimental results show the effectiveness of our method compared to different self-supervision methods for semi-supervised learning. In addition, we achieve meaningful improvements over state-of-the-art optical flow models on Sintel and KITTI benchmarks by exploiting additional unlabeled datasets. Code is available at https://github.com/iwbn/flow-supervisor.

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