Summary of Hvm-1: Large-scale Video Models Pretrained with Nearly 5000 Hours Of Human-like Video Data, by A. Emin Orhan
HVM-1: Large-scale video models pretrained with nearly 5000 hours of human-like video data
by A. Emin Orhan
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC)
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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 This paper introduces Human-like Video Models (HVM-1), large-scale video models trained using nearly 5000 hours of curated human-like video data. The HVM-1 models are pretrained with the spatiotemporal masked autoencoder (ST-MAE) algorithm and released in two variants, each with a different spatial resolution. The authors evaluate the performance of these models in downstream few-shot video and image recognition tasks and compare them against a model pretrained with 1330 hours of short action-oriented video clips from YouTube (Kinetics-700). Despite differences between the pretraining datasets, HVM-1 models perform competitively against the Kinetics-700 model. The study also shows that HVM-1 models learn more accurate and robust object representations compared to image-based MAE algorithm-trained models on the same data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new kind of AI model that can understand videos better. They trained this model using thousands of hours of video recordings, most of which show people doing everyday things like walking or eating. The authors tested this model and compared it to another model that was trained using shorter action-oriented videos from YouTube. Surprisingly, their model performs just as well despite the big difference in what they were shown. This new model is better at understanding objects in videos and can learn more quickly than other models that only look at still images. |
Keywords
» Artificial intelligence » Autoencoder » Few shot » Mae » Pretraining » Spatiotemporal
