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Weakly supervised instance segmentation has gained popularity because itreduces high annotation cost of pixel-level masks required for model training.Recent approaches for weakly supervised instance segmentation detect andsegment objects using appearance information obtained from a static image.However, it poses the challenge of identifying objects with anon-discriminatory appearance. In this study, we address this problem by usingmotion information from image sequences. We propose a two-stream encoder thatleverages appearance and motion features extracted from images and opticalflows. Additionally, we propose a novel pairwise loss that considers bothappearance and motion information to supervise segmentation. We conductedextensive evaluations on the YouTube-VIS 2019 benchmark dataset. Our resultsdemonstrate that the proposed method improves the Average Precision of thestate-of-the-art method by 3.1.

Weakly Supervised Instance Segmentation using Motion Information via Optical Flow