Video-CSR: Complex Video Digest Creation for Visual-Language Models
Keywords: dataset, video, multimodal, caption, summarization, retrieval
TL;DR: A new task and dataset for long form video summarization.
Abstract: We present a novel task and human annotated dataset for evaluating the ability for visual-language models to generate captions and summaries for real-world video clips, which we call **Video-CSR** (**C**aptioning, **S**ummarization and **R**etrieval). The dataset contains 4.8K YouTube video clips of 20-60 seconds in duration and covers a wide range of topics and interests. Each video clip corresponds to 5 independently annotated captions (1 sentence) and summaries (3-10 sentences). Given any video selected from the dataset and its corresponding ASR information, we evaluate visual-language models on either caption or summary generation that is grounded in both the visual and auditory content of the video. Additionally, models are also evaluated on caption- and summary-based retrieval tasks, where the summary-based retrieval task requires the identification of a target video given _excerpts_ of a given summary. Given the novel nature of the paragraph-length video summarization task, we perform extensive comparative analyses of different existing evaluation metrics and their alignment with human preferences. Finally, we propose a foundation model with competitive generation and retrieval capabilities that serves as a baseline for the Video-CSR task. We aim for Video-CSR to serve as a useful evaluation set in the age of large language models and complex multi-modal tasks.
Primary Area: datasets and benchmarks
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Submission Number: 3120
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