Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input

• URL: http://arxiv.org/abs/2408.15542v1
• Date: Wed, 28 Aug 2024 05:34:14 GMT
• Title: Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input
• Authors: Jiajun Liu, Yibing Wang, Hanghang Ma, Xiaoping Wu, Xiaoqi Ma, Xiaoming Wei, Jianbin Jiao, Enhua Wu, Jie Hu,
• Abstract summary: Kangaroo is a powerful Video LMM aimed at addressing the challenges of processing long videos. Data curation system to build a large-scale dataset with high-quality annotations for vision-language pre-training and instruction tuning. curriculum training pipeline with gradually increasing resolution and number of input frames to accommodate long videos.
• Score: 34.50993235961505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rapid advancements have been made in extending Large Language Models (LLMs) to Large Multi-modal Models (LMMs). However, extending input modality of LLMs to video data remains a challenging endeavor, especially for long videos. Due to insufficient access to large-scale high-quality video data and the excessive compression of visual features, current methods exhibit limitations in effectively processing long videos. In this paper, we introduce Kangaroo, a powerful Video LMM aimed at addressing these challenges. Confronted with issue of inadequate training data, we develop a data curation system to build a large-scale dataset with high-quality annotations for vision-language pre-training and instruction tuning. In addition, we design a curriculum training pipeline with gradually increasing resolution and number of input frames to accommodate long videos. Evaluation results demonstrate that, with 8B parameters, Kangaroo achieves state-of-the-art performance across a variety of video understanding benchmarks while exhibiting competitive results on others. Particularly, on benchmarks specialized for long videos, Kangaroo excels some larger models with over 10B parameters and proprietary models.

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