Video-STaR: Self-Training Enables Video Instruction Tuning with Any
  Supervision
Authors: name: Orr Zohar, name: Xiaohan Wang, name: Yonatan Bitton, name: Idan Szpektor, name: Serena Yeung-Levy

The performance of Large Vision Language Models (LVLMs) is dependent on the
size and quality of their training datasets. Existing video instruction tuning
datasets lack diversity as they are derived by prompting large language models
with video captions to generate question-answer pairs, and are therefore mostly
descriptive. Meanwhile, many labeled video datasets with diverse labels and
supervision exist - however, we find that their integration into LVLMs is
non-trivial. Herein, we present Video Self-Training with augmented Reasoning
(Video-STaR), the first video self-training approach. Video-STaR allows the
utilization of any labeled video dataset for video instruction tuning. In
Video-STaR, an LVLM cycles between instruction generation and finetuning, which
we show (I) improves general video understanding and (II) adapts LVLMs to novel
downstream tasks with existing supervision. During generation, an LVLM is
prompted to propose an answer. The answers are then filtered only to those that
contain the original video labels, and the LVLM is then re-trained on the
generated dataset. By only training on generated answers that contain the
correct video labels, Video-STaR utilizes these existing video labels as weak
supervision for video instruction tuning. Our results demonstrate that
Video-STaR-enhanced LVLMs exhibit improved performance in (I) general video QA,
where TempCompass performance improved by 10%, and (II) on downstream tasks,
where Video-STaR improved Kinetics700-QA accuracy by 20% and action quality
assessment on FineDiving by 15%.