Summary of Svitt-ego: a Sparse Video-text Transformer For Egocentric Video, by Hector A. Valdez and Kyle Min and Subarna Tripathi
SViTT-Ego: A Sparse Video-Text Transformer for Egocentric Video
by Hector A. Valdez, Kyle Min, Subarna Tripathi
First submitted to arxiv on: 13 Jun 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 The paper introduces SViTT-Ego, a novel egocentric video-text transformer model that incorporates edge and node sparsification for efficient pretraining. The authors propose a new objective, EgoNCE, instead of InfoNCE, and demonstrate its effectiveness on the EgoClip dataset. Compared to LAVILA large, SViTT-Ego achieves a 2.8% accuracy gain on EgoMCQ (intra-video) with no additional data augmentation techniques, making it suitable for pretraining on memory-limited devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to train machines to understand videos and text together, which is useful for things like summarizing videos or answering questions about what’s happening in them. This method uses special models that are good at understanding video and text together, and it makes the training process more efficient so it can be done on devices with limited memory. |
Keywords
» Artificial intelligence » Data augmentation » Pretraining » Transformer
