Video question answering (VideoQA) aims to answer natural language questions according to the given videos. Although existing models perform well in the factoid VideoQA task, they still face challenges in deep video understanding (DVU) task, which focuses on story videos. Compared to factoid videos, the most significant feature of story videos is storylines, which are composed of complex interactions and long-range evolvement of core story topics including characters, actions and locations. Understanding these topics requires models to possess DVU capability. However, existing DVU datasets rarely organize questions according to these story topics, making them difficult to comprehensively assess VideoQA models' DVU capability of complex storylines. Additionally, the question quantity and video length of these dataset are limited by high labor costs of handcrafted dataset building method. In this paper, we devise a large language model based multi-agent collaboration framework, StoryMind, to automatically generate a new large-scale DVU dataset. The dataset, FriendsQA, derived from the renowned sitcom Friends with an average episode length of 1,358 seconds, contains 44.6K questions evenly distributed across 14 fine-grained topics. Finally, We conduct comprehensive experiments on 10 state-of-the-art VideoQA models using the FriendsQA dataset.

Deep video understanding (DVU) is often considered a challenge due to the aim of interpreting a video with storyline, which is designed to solve two levels of problems: predicting the human interaction in scene-level and identifying the relationship between two entities in movie-level. Based on our understanding of the movie characteristics and analysis of DVU tasks, in this paper, we propose a four-stage method to solve the task, which includes video structuring, shot based instance search, interaction & relation prediction and shot-scene summary & Question Answering (QA) with ChatGPT. In these four stages, shot based instance search allows accurate identification and tracking of characters at an appropriate video granularity. Using ChatGPT in QA, on the one hand, can narrow the answer space, on the other hand, with the help of the powerful text understanding ability, ChatGPT can help us answer the questions by giving background knowledge. We rank first in movie-level group 2 and scene-level group 1, second in movie-level group 1 and scene-level group 2 in ACM MM 2023 Grand Challenge.