Efficient Video Instance Segmentation via Tracklet Query and Proposal

• URL: http://arxiv.org/abs/2203.01853v1
• Date: Thu, 3 Mar 2022 17:00:11 GMT
• Title: Efficient Video Instance Segmentation via Tracklet Query and Proposal
• Authors: Jialian Wu, Sudhir Yarram, Hui Liang, Tian Lan, Junsong Yuan, Jayan Eledath, and Gerard Medioni
• Abstract summary: Video Instance aims to simultaneously classify, segment, and track multiple object instances in videos. Most clip-level methods are neither end-to-end learnable nor real-time. This paper proposes EfficientVIS, a fully end-to-end framework with efficient training and inference.
• Score: 62.897552852894854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Instance Segmentation (VIS) aims to simultaneously classify, segment, and track multiple object instances in videos. Recent clip-level VIS takes a short video clip as input each time showing stronger performance than frame-level VIS (tracking-by-segmentation), as more temporal context from multiple frames is utilized. Yet, most clip-level methods are neither end-to-end learnable nor real-time. These limitations are addressed by the recent VIS transformer (VisTR) which performs VIS end-to-end within a clip. However, VisTR suffers from long training time due to its frame-wise dense attention. In addition, VisTR is not fully end-to-end learnable in multiple video clips as it requires a hand-crafted data association to link instance tracklets between successive clips. This paper proposes EfficientVIS, a fully end-to-end framework with efficient training and inference. At the core are tracklet query and tracklet proposal that associate and segment regions-of-interest (RoIs) across space and time by an iterative query-video interaction. We further propose a correspondence learning that makes tracklets linking between clips end-to-end learnable. Compared to VisTR, EfficientVIS requires 15x fewer training epochs while achieving state-of-the-art accuracy on the YouTube-VIS benchmark. Meanwhile, our method enables whole video instance segmentation in a single end-to-end pass without data association at all.

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