Summary of Vedit: Latent Prediction Architecture For Procedural Video Representation Learning, by Han Lin et al.
VEDIT: Latent Prediction Architecture For Procedural Video Representation Learning
by Han Lin, Tushar Nagarajan, Nicolas Ballas, Mido Assran, Mojtaba Komeili, Mohit Bansal, Koustuv Sinha
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Procedural video representation learning aims to develop an agent that can anticipate and forecast future video inputs given present video input and textual annotations. Prior works rely on pretraining visual encoders and prediction models with language supervision. This paper explores whether extending compute-intensive pretraining is necessary for learning video clip sequences with noisy text supervision. The authors show that a strong, frozen pretrained visual encoder combined with a well-designed prediction model can achieve state-of-the-art performance in forecasting and procedural planning without requiring additional supervision from language or ASR. Instead of learning representations from pixel space, the method utilizes latent embedding spaces of publicly available vision encoders. Conditioning on frozen clip-level embeddings allows the prediction model to learn robust representations for forecasting through iterative denoising using diffusion transformers. The authors demonstrate their approach’s effectiveness across five procedural learning tasks on four datasets (NIV, CrossTask, COIN, and Ego4D-v2), advancing strong baselines in long-horizon action anticipation, step forecasting, task classification, and procedure planning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about developing an artificial intelligence that can predict what will happen next in a video. Currently, AI systems need to be trained using lots of data and language information to make good predictions. This study shows that you don’t always need all that extra training to get accurate results. The authors use a powerful pre-trained computer vision model combined with a new prediction technique to achieve the best results without needing additional language or audio information. They test their approach on various tasks, such as predicting what actions will be taken in a video and classifying the tasks being performed. Their method performs well across multiple datasets and is an important step forward in developing AI systems that can understand videos. |
Keywords
» Artificial intelligence » Classification » Diffusion » Encoder » Pretraining » Representation learning
