Summary of Cinepile: a Long Video Question Answering Dataset and Benchmark, by Ruchit Rawal et al.
CinePile: A Long Video Question Answering Dataset and Benchmark
by Ruchit Rawal, Khalid Saifullah, Miquel Farré, Ronen Basri, David Jacobs, Gowthami Somepalli, Tom Goldstein
First submitted to arxiv on: 14 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 The paper presents CinePile, a novel dataset and benchmark designed for authentic long-form video understanding. The existing datasets fall short of providing genuine comprehension challenges as many tasks can be tackled by analyzing just one or a few random frames. To address this issue, the authors created a question-answer dataset using advanced LLMs with human-in-the-loop, building upon human-generated raw data. The comprehensive dataset comprises 305,000 multiple-choice questions covering various aspects, including temporal comprehension and multimodal understanding. The findings indicate that fine-tuning models can lead to significant improvements in their performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way for computers to understand long videos by making a special type of question-answering test. Currently, most video tests are too easy because they only use one or two frames from the video. The authors made a huge dataset with over 300,000 questions that cover many different aspects of understanding a video, like what’s happening in it and why. They also tested how well computer models can do on this new test by fine-tuning them to get better results. |
Keywords
» Artificial intelligence » Fine tuning » Question answering
