Summary of Shotluck Holmes: a Family Of Efficient Small-scale Large Language Vision Models For Video Captioning and Summarization, by Richard Luo et al.
Shotluck Holmes: A Family of Efficient Small-Scale Large Language Vision Models For Video Captioning and Summarization
by Richard Luo, Austin Peng, Adithya Vasudev, Rishabh Jain
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 The proposed family of large language vision models (LLVMs), called Shotluck Holmes, tackles the challenge of understanding videos by processing shot-by-shot semantic information. Leveraging better pretraining and data collection strategies, Shotluck Holmes extends the abilities of existing small LLVMs from single frames to sequences of frames. This enables superior performance on video captioning and summary tasks, surpassing state-of-the-art results with significantly smaller and more computationally efficient models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Shotluck Holmes is a new approach to understanding videos by breaking them down into shorter segments or shots. It’s like trying to understand a story by looking at individual frames instead of just the whole movie. The current methods for video summarization and captioning are good, but they don’t take into account the smaller details within each shot. This project aims to improve those methods by creating more efficient models that can process these small details. The result is better performance on tasks like generating captions or summaries for videos. |
Keywords
» Artificial intelligence » Pretraining » Summarization
