因果混沌！用于基于动态视觉场景的更长因果链上的综合因果动作问答的数据集
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
AI总结：
针对因果视频问答中现有数据集因果推理深度不足的问题，利用卡通特性构建CausalChaos!数据集，含因果链等问题并引入错误答案挖掘，为因果关系建模等发展助力。
摘要：
因果视频问答日益受关注，但现有数据集因果推理缺乏深度。我们利用卡通特性构建了CausalChaos!，一个基于“猫和老鼠”卡通系列的新颖且具挑战性的因果“为什么”问答数据集。我们的问题涉及连接角色与视觉场景间多个动态交互的因果链，还引入了硬错误答案挖掘。模型虽表现不错，但仍有改进空间，新数据集将推动该领域发展。
英文摘要：
Causal video question answering (QA) has garnered increasing interest, yet existing datasets often lack depth in causal reasoning. To address this gap, we capitalize on the unique properties of cartoons and construct CausalChaos!, a novel, challenging causal Why-QA dataset built upon the iconic "Tom and Jerry" cartoon series. Cartoons use the principles of animation that allow animators to create expressive, unambiguous causal relationships between events to form a coherent storyline. Utilizing these properties, along with thought-provoking questions and multi-level answers (answer and detailed causal explanation), our questions involve causal chains that interconnect multiple dynamic interactions between characters and visual scenes. These factors demand models to solve more challenging, yet well-defined causal relationships. We also introduce hard incorrect answer mining, including a causally confusing version that is even more challenging. While models perform well, there is much room for improvement, especially, on open-ended answers. We identify more advanced/explicit causal relationship modeling & joint modeling of vision and language as the immediate areas for future efforts to focus upon. Along with the other complementary datasets, our new challenging dataset will pave the way for these developments in the field. Project Page: https://github.com/LUNAProject22/CausalChaos
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
