A2VIS: Amodal-Aware Approach to Video Instance Segmentation

• URL: http://arxiv.org/abs/2412.01147v1
• Date: Mon, 02 Dec 2024 05:44:29 GMT
• Title: A2VIS: Amodal-Aware Approach to Video Instance Segmentation
• Authors: Minh Tran, Thang Pham, Winston Bounsavy, Tri Nguyen, Ngan Le,
• Abstract summary: We propose a novel framework, Amodal-Aware Video Instance (A2VIS), which incorporates amodal representations to achieve a reliable comprehensive understanding of objects in video. Amodal-Aware Video Instance (A2VIS) incorporates amodal representations to achieve a reliable comprehensive understanding of both visible and occluded parts of objects in video.
• Score: 8.082593574401704
• License:
• Abstract: Handling occlusion remains a significant challenge for video instance-level tasks like Multiple Object Tracking (MOT) and Video Instance Segmentation (VIS). In this paper, we propose a novel framework, Amodal-Aware Video Instance Segmentation (A2VIS), which incorporates amodal representations to achieve a reliable and comprehensive understanding of both visible and occluded parts of objects in a video. The key intuition is that awareness of amodal segmentation through spatiotemporal dimension enables a stable stream of object information. In scenarios where objects are partially or completely hidden from view, amodal segmentation offers more consistency and less dramatic changes along the temporal axis compared to visible segmentation. Hence, both amodal and visible information from all clips can be integrated into one global instance prototype. To effectively address the challenge of video amodal segmentation, we introduce the spatiotemporal-prior Amodal Mask Head, which leverages visible information intra clips while extracting amodal characteristics inter clips. Through extensive experiments and ablation studies, we show that A2VIS excels in both MOT and VIS tasks in identifying and tracking object instances with a keen understanding of their full shape.

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