  Weakly supervised instance segmentation has gained popularity because it
reduces high annotation cost of pixel-level masks required for model training.
Recent approaches for weakly supervised instance segmentation detect and
segment objects using appearance information obtained from a static image.
However, it poses the challenge of identifying objects with a
non-discriminatory appearance. In this study, we address this problem by using
motion information from image sequences. We propose a two-stream encoder that
leverages appearance and motion features extracted from images and optical
flows. Additionally, we propose a novel pairwise loss that considers both
appearance and motion information to supervise segmentation. We conducted
extensive evaluations on the YouTube-VIS 2019 benchmark dataset. Our results
demonstrate that the proposed method improves the Average Precision of the
state-of-the-art method by 3.1.
