Summary of Exploring Efficient Foundational Multi-modal Models For Video Summarization, by Karan Samel et al.
Exploring Efficient Foundational Multi-modal Models for Video Summarization
by Karan Samel, Apoorva Beedu, Nitish Sontakke, Irfan Essa
First submitted to arxiv on: 9 Oct 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 Recently, foundational models have been combined to perform tasks on video, such as video summarization. These models align outputs from each modality-specific model into the same embedding space through pre-training, which is computationally expensive. To alleviate these issues, we propose a plug-and-play video language model that directly uses text generated from each input modality without pre-training alignment overhead. Instead of fine-tuning, we leverage few-shot instruction adaptation strategies. We compare the performance and computational costs of our plug-and-play style method with baseline tuning methods. Our results show the generalizability and data efficiency of these methods during domain shift. This analysis presents practical insights on how to leverage multi-modal foundational models for effective results given realistic compute and data limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you can teach a computer to understand video by combining different types of input, like text, images, or audio. Recently, computers have learned to do this and perform tasks like summarizing videos. However, making these computers learn is very time-consuming and requires a lot of data. To make it faster and more efficient, we created a new way for the computer to understand video called the plug-and-play video language model. This method doesn’t need as much training or data as other methods do. We compared our new method with older ones to see how well they work and how much time and data they require. Our results show that our new method is better at adapting to different situations and can be very useful when we don’t have a lot of data. |
Keywords
» Artificial intelligence » Alignment » Embedding space » Few shot » Fine tuning » Language model » Multi modal » Summarization
