Towards Open-Vocabulary Video Instance Segmentation

• URL: http://arxiv.org/abs/2304.01715v2
• Date: Sun, 6 Aug 2023 20:08:58 GMT
• Title: Towards Open-Vocabulary Video Instance Segmentation
• Authors: Haochen Wang, Cilin Yan, Shuai Wang, Xiaolong Jiang, XU Tang, Yao Hu, Weidi Xie, Efstratios Gavves
• Abstract summary: Video Instance aims at segmenting and categorizing objects in videos from a closed set of training categories. We introduce the novel task of Open-Vocabulary Video Instance, which aims to simultaneously segment, track, and classify objects in videos from open-set categories. To benchmark Open-Vocabulary VIS, we collect a Large-Vocabulary Video Instance dataset (LV-VIS), that contains well-annotated objects from 1,196 diverse categories.
• Score: 61.469232166803465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video Instance Segmentation (VIS) aims at segmenting and categorizing objects in videos from a closed set of training categories, lacking the generalization ability to handle novel categories in real-world videos. To address this limitation, we make the following three contributions. First, we introduce the novel task of Open-Vocabulary Video Instance Segmentation, which aims to simultaneously segment, track, and classify objects in videos from open-set categories, including novel categories unseen during training. Second, to benchmark Open-Vocabulary VIS, we collect a Large-Vocabulary Video Instance Segmentation dataset (LV-VIS), that contains well-annotated objects from 1,196 diverse categories, significantly surpassing the category size of existing datasets by more than one order of magnitude. Third, we propose an efficient Memory-Induced Transformer architecture, OV2Seg, to first achieve Open-Vocabulary VIS in an end-to-end manner with near real-time inference speed. Extensive experiments on LV-VIS and four existing VIS datasets demonstrate the strong zero-shot generalization ability of OV2Seg on novel categories. The dataset and code are released here https://github.com/haochenheheda/LVVIS.

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