Summary of Videoicl: Confidence-based Iterative In-context Learning For Out-of-distribution Video Understanding, by Kangsan Kim et al.
VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding
by Kangsan Kim, Geon Park, Youngwan Lee, Woongyeong Yeo, Sung Ju Hwang
First submitted to arxiv on: 3 Dec 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 Recent advancements in video large multimodal models (LMMs) have significantly improved their video understanding and reasoning capabilities. However, their performance drops on out-of-distribution (OOD) tasks that are underrepresented in training data. Traditional methods like fine-tuning on OOD datasets are impractical due to high computational costs. The authors propose VideoICL, a novel framework for OOD video understanding, introducing similarity-based relevant example selection and confidence-based iterative inference. This approach improves performance by extending effective context length without incurring high costs. Experimental results on multiple benchmarks demonstrate significant gains, especially in domain-specific scenarios, laying the groundwork for broader video comprehension applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have made big improvements in computers that can understand videos and learn from them. However, when these computers are asked to do things they haven’t seen before, their performance drops. To address this issue, a new method called VideoICL was developed. This method selects the most relevant examples from what it has learned and uses them to make better predictions. It also adjusts its confidence level based on how well it does, so if it’s not sure about something, it will try again until it gets it right. The results show that this approach can significantly improve performance, especially in specific situations, making it a useful tool for many applications. |
Keywords
» Artificial intelligence » Context length » Fine tuning » Inference
