A2VIS:模式感知视频实例分割方法

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Minh Tran , Thang Pham , Winston Bounsavy , Tri Nguyen , Ngan Le
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引用次数: 0

摘要

对于视频实例级任务,如多目标跟踪(MOT)和视频实例分割(VIS),处理遮挡仍然是一个重大挑战。在本文中,我们提出了一个新的框架,即模式感知视频实例分割(A2VIS),它结合了模式表示来实现对视频中物体的可见部分和遮挡部分的可靠和全面的理解。关键的直觉是,通过时空维度对模态分割的感知可以实现稳定的对象信息流。在物体部分或完全隐藏在视野之外的情况下,与可见分割相比,模态分割在时间轴上提供了更多的一致性和更少的戏剧性变化。因此,所有剪辑中的模态和可见信息都可以集成到一个全局实例原型中。为了有效地解决视频模态分割的挑战,我们引入了时空先验模态掩模头,它利用片段内的可见信息,同时提取片段间的模态特征。通过广泛的实验和消融研究,我们表明A2VIS在识别和跟踪目标实例方面表现出色,并对其完整形状有敏锐的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A2VIS: Amodal-Aware Approach to Video Instance Segmentation

A2VIS: Amodal-Aware Approach to Video Instance Segmentation
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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来源期刊
Image and Vision Computing
Image and Vision Computing 工程技术-工程:电子与电气
CiteScore
8.50
自引率
8.50%
发文量
143
审稿时长
7.8 months
期刊介绍: Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.
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