Summary of Question-answering Dense Video Events, by Hangyu Qin et al.
Question-Answering Dense Video Events
by Hangyu Qin, Junbin Xiao, Angela Yao
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper introduces question-answering dense video events (QADVE), a novel task that requires answering and grounding questions about multiple events occurring in long videos. The authors present the DeVE-QA dataset, featuring 78K questions about 26K events on 10.6K long videos, and demonstrate that existing multimodal large language models (MLLMs) struggle to perform well in this task. To address this challenge, the authors propose a novel training-free MLLM approach called DeVi, which incorporates hierarchical captioning, temporal event memory, and self-consistency checking modules. Experimental results show that DeVi outperforms existing MLLMs in QADVE, achieving significant increases in accuracy on DeVE-QA (4.1%) and NExT-GQA (3.7%). This work highlights the importance of reasoning about multiple events over extended time periods for question-answering applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine trying to answer questions about what’s happening in a long video, like a movie or a sports game. It’s harder than it seems! Researchers created a new task called QADVE that requires answering and grounding questions about multiple events occurring in these videos. They also built a special dataset with lots of examples (78K!) for testing. The good news is that they found a way to improve the performance of existing AI models by adding new features like hierarchical captioning, memory, and self-checking. With this new approach, called DeVi, AI can do a much better job at answering questions about what’s happening in long videos. |
Keywords
* Artificial intelligence * Grounding * Question answering
