Summary of Videoespresso: a Large-scale Chain-of-thought Dataset For Fine-grained Video Reasoning Via Core Frame Selection, by Songhao Han et al.
VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection
by Songhao Han, Wei Huang, Hairong Shi, Le Zhuo, Xiu Su, Shifeng Zhang, Xu Zhou, Xiaojuan Qi, Yue Liao, Si Liu
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces VideoEspresso, a novel video question-answering (VideoQA) dataset that addresses the challenges of existing datasets in multimodal understanding and complex reasoning. The dataset features video QA pairs with multimodal annotations of intermediate reasoning steps, constructed using a semantic-aware method to reduce redundancy. The authors also develop video Chain-of-Thought (CoT) annotations to guide Large Vision Language Models (LVLMs) in extracting logical relationships from QA pairs and video content. To exploit the potential of high-quality VideoQA pairs, the paper proposes a Hybrid LVLMs Collaboration framework that adaptively selects core frames and performs CoT reasoning using multimodal evidence. The authors evaluate their method on a proposed benchmark with 14 tasks against 9 popular LVLMs, demonstrating superior video reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way of understanding videos by creating a special dataset called VideoEspresso. This dataset helps computers better understand videos and answer questions about them. It does this by adding extra information to the videos that shows how the computer should think about the video to come up with an answer. The authors also created a way for computers to use this extra information to reason about the video and come up with answers. They tested their method on many different types of videos and it worked better than other methods. |
Keywords
» Artificial intelligence » Question answering
