  Recent studies have demonstrated the effectiveness of Large Language Models
(LLMs) as reasoning modules that can deconstruct complex tasks into more
manageable sub-tasks, particularly when applied to visual reasoning tasks for
images. In contrast, this paper introduces a Video Understanding and Reasoning
Framework (VURF) based on the reasoning power of LLMs. Ours is a novel approach
to extend the utility of LLMs in the context of video tasks, leveraging their
capacity to generalize from minimal input and output demonstrations within a
contextual framework. By presenting LLMs with pairs of instructions and their
corresponding high-level programs, we harness their contextual learning
capabilities to generate executable visual programs for video understanding. To
enhance program's accuracy and robustness, we implement two important
strategies. Firstly, we employ a feedback-generation approach, powered by
GPT-3.5, to rectify errors in programs utilizing unsupported functions.
Secondly, taking motivation from recent works on self refinement of LLM
outputs, we introduce an iterative procedure for improving the quality of the
in-context examples by aligning the initial outputs to the outputs that would
have been generated had the LLM not been bound by the structure of the
in-context examples. Our results on several video-specific tasks, including
visual QA, video anticipation, pose estimation and multi-video QA illustrate
the efficacy of these enhancements in improving the performance of visual
programming approaches for video tasks.
