Summary of Whats in a Video: Factorized Autoregressive Decoding For Online Dense Video Captioning, by Aj Piergiovanni et al.
Whats in a Video: Factorized Autoregressive Decoding for Online Dense Video Captioning
by AJ Piergiovanni, Dahun Kim, Michael S. Ryoo, Isaac Noble, Anelia Angelova
First submitted to arxiv on: 22 Nov 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 model generates dense captions for videos using an efficient, online approach that outputs detailed and temporally aligned descriptions without requiring access to future frames. The autoregressive factorized decoding architecture models the sequence of visual features for each time segment, allowing for localized descriptions and leveraging context from previous segments. This approach enables frequent and detailed captioning that accurately describes video content, rather than mimicking training data. Additionally, an optimization for efficient training and inference is proposed, which allows scaling to longer videos while using 20% less compute. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our model helps machines understand videos better by generating accurate captions about what’s happening in the video. Instead of processing the whole video at once, our approach processes the video segment by segment, allowing it to provide more detailed and frequent descriptions of the video content. This can be useful for tasks like automatic video tagging or harvesting large-scale video data. |
Keywords
» Artificial intelligence » Autoregressive » Inference » Optimization
