OW-VISCap: Open-World Video Instance Segmentation and Captioning

• URL: http://arxiv.org/abs/2404.03657v1
• Date: Thu, 4 Apr 2024 17:59:58 GMT
• Title: OW-VISCap: Open-World Video Instance Segmentation and Captioning
• Authors: Anwesa Choudhuri, Girish Chowdhary, Alexander G. Schwing,
• Abstract summary: We propose an approach to jointly segment, track, and caption previously seen or unseen objects in a video. We generate rich descriptive and object-centric captions for each detected object via a masked attention augmented LLM input. Our approach matches or surpasses state-of-the-art on three tasks.
• Score: 95.6696714640357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-world video instance segmentation is an important video understanding task. Yet most methods either operate in a closed-world setting, require an additional user-input, or use classic region-based proposals to identify never before seen objects. Further, these methods only assign a one-word label to detected objects, and don't generate rich object-centric descriptions. They also often suffer from highly overlapping predictions. To address these issues, we propose Open-World Video Instance Segmentation and Captioning (OW-VISCap), an approach to jointly segment, track, and caption previously seen or unseen objects in a video. For this, we introduce open-world object queries to discover never before seen objects without additional user-input. We generate rich and descriptive object-centric captions for each detected object via a masked attention augmented LLM input. We introduce an inter-query contrastive loss to ensure that the object queries differ from one another. Our generalized approach matches or surpasses state-of-the-art on three tasks: open-world video instance segmentation on the BURST dataset, dense video object captioning on the VidSTG dataset, and closed-world video instance segmentation on the OVIS dataset.

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