Video Instruction Tuning with Synthetic Data
Keywords: Video instruction dataset, video-language model
TL;DR: The largest high-quality synthetic dataset specifically for video instruction-following
Abstract: The development of video large multimodal models (LMMs) has been hindered by the difficulty of curating large amounts of high-quality raw data from the web. To address this, we consider an alternative approach, creating a high-quality synthetic dataset specifically for video instruction-following, namely LLaVA-Video-178K. This dataset includes key tasks such as detailed captioning, open-ended question-answering (QA), and multiple-choice QA. By training on this proposed dataset, in combination with existing visual instruction tuning data, we introduce LLaVA-Video, a new video LMM. Our experiments demonstrate that LLaVA-Video achieves strong performance across various video benchmarks, highlighting the effectiveness of our dataset. We plan to release the dataset, its generation pipeline, and the model checkpoints.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 2539
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