Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation

• URL: http://arxiv.org/abs/2111.07529v1
• Date: Mon, 15 Nov 2021 04:15:57 GMT
• Title: Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation
• Authors: Anirudh S Chakravarthy, Won-Dong Jang, Zudi Lin, Donglai Wei, Song Bai, Hanspeter Pfister
• Abstract summary: Video instance segmentation aims to detect, segment, and track objects in a video. Current approaches extend image-level segmentation algorithms to the temporal domain. We propose a video instance segmentation method that alleviates the problem due to missing detections.
• Score: 51.68840525174265
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Video instance segmentation aims to detect, segment, and track objects in a video. Current approaches extend image-level segmentation algorithms to the temporal domain. However, this results in temporally inconsistent masks. In this work, we identify the mask quality due to temporal stability as a performance bottleneck. Motivated by this, we propose a video instance segmentation method that alleviates the problem due to missing detections. Since this cannot be solved simply using spatial information, we leverage temporal context using inter-frame attentions. This allows our network to refocus on missing objects using box predictions from the neighbouring frame, thereby overcoming missing detections. Our method significantly outperforms previous state-of-the-art algorithms using the Mask R-CNN backbone, by achieving 35.1% mAP on the YouTube-VIS benchmark. Additionally, our method is completely online and requires no future frames. Our code is publicly available at https://github.com/anirudh-chakravarthy/ObjProp.

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