ViCaS: A Dataset for Combining Holistic and Pixel-level Video Understanding using Captions with Grounded Segmentation

• URL: http://arxiv.org/abs/2412.09754v2
• Date: Tue, 17 Dec 2024 21:14:50 GMT
• Title: ViCaS: A Dataset for Combining Holistic and Pixel-level Video Understanding using Captions with Grounded Segmentation
• Authors: Ali Athar, Xueqing Deng, Liang-Chieh Chen,
• Abstract summary: This paper introduces ViCaS, a new dataset containing thousands of challenging videos. Our benchmark evaluates models on holistic/high-level understanding and language-guided, pixel-precise segmentation.
• Score: 14.534308478766476
• License:
• Abstract: Recent advances in multimodal large language models (MLLMs) have expanded research in video understanding, primarily focusing on high-level tasks such as video captioning and question-answering. Meanwhile, a smaller body of work addresses dense, pixel-precise segmentation tasks, which typically involve category-guided or referral-based object segmentation. Although both research directions are essential for developing models with human-level video comprehension, they have largely evolved separately, with distinct benchmarks and architectures. This paper aims to unify these efforts by introducing ViCaS, a new dataset containing thousands of challenging videos, each annotated with detailed, human-written captions and temporally consistent, pixel-accurate masks for multiple objects with phrase grounding. Our benchmark evaluates models on both holistic/high-level understanding and language-guided, pixel-precise segmentation. We also present carefully validated evaluation measures and propose an effective model architecture that can tackle our benchmark. The project page is at https://ali2500.github.io/vicas-project/

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