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While Video Instance Segmentation (VIS) has seen rapid progress, currentapproaches struggle to predict high-quality masks with accurate boundarydetails. Moreover, the predicted segmentations often fluctuate over time,suggesting that temporal consistency cues are neglected or not fully utilized.In this paper, we set out to tackle these issues, with the aim of achievinghighly detailed and more temporally stable mask predictions for VIS. We firstpropose the Video Mask Transfiner (VMT) method, capable of leveragingfine-grained high-resolution features thanks to a highly efficient videotransformer structure. Our VMT detects and groups sparse error-pronespatio-temporal regions of each tracklet in the video segment, which are thenrefined using both local and instance-level cues. Second, we identify that thecoarse boundary annotations of the popular YouTube-VIS dataset constitute amajor limiting factor. Based on our VMT architecture, we therefore design anautomated annotation refinement approach by iterative training andself-correction. To benchmark high-quality mask predictions for VIS, weintroduce the HQ-YTVIS dataset, consisting of a manually re-annotated test setand our automatically refined training data. We compare VMT with the mostrecent state-of-the-art methods on the HQ-YTVIS, as well as the Youtube-VIS,OVIS and BDD100K MOTS benchmarks. Experimental results clearly demonstrate theefficacy and effectiveness of our method on segmenting complex and dynamicobjects, by capturing precise details.

Video Mask Transfiner for High-Quality Video Instance Segmentation