Summary of Freeva: Offline Mllm As Training-free Video Assistant, by Wenhao Wu
FreeVA: Offline MLLM as Training-Free Video Assistant
by Wenhao Wu
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper, FreeVA, investigates the application of Multimodal Large Language Models (MLLMs) in the video domain without additional training. The study reveals surprising findings: firstly, a zero-shot video question-answering model leveraging offline image-based MLLM outperforms state-of-the-art methods that involve video instruction tuning on benchmarks like MSVD-QA, ActivityNet-QA, and MSRVTT-QA. Secondly, initializing with an image-based MLLM and fine-tuning using video instruction tuning does not lead to better performance compared to not training at all. Additionally, the study highlights the influence of changes in the GPT API version on evaluation metrics, emphasizing the importance of standardizing comparisons between different methods. FreeVA aims to provide a plug-and-play baseline for evaluating existing MLLMs in the video domain and encourages researchers to reconsider whether current video MLLM methods have truly acquired knowledge beyond image MLLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models can work with videos. It’s like asking questions about a movie without seeing it before. The researchers found some surprising things: first, a model that doesn’t need any extra training can answer video questions just as well as more complicated models. Second, making these models learn from videos doesn’t always make them better. Finally, the way we measure how good these models are is important and can be influenced by small changes in the tools used. The goal of this study is to help others evaluate their own models for working with videos and to encourage researchers to think about what they’ve learned. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Instruction tuning » Question answering » Zero shot
