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V-JEPA: Revisiting Feature Prediction for Learning Visual Representations from Video (Explained)

Yannic Kilcher
296K subscribers
47K views 1 year ago #meta #vjepa #unsupervisedlearning
#vjepa #meta #unsupervisedlearning V-JEPA is a method for unsupervised representation learning of video data by using only latent representation prediction as objective function. Weights & Biases course on Structured LLM Outputs: https://wandb.me/course-yannic OUTLINE: 0:00 - Intro 1:45 - Predictive Feature Principle 8:00 - Weights & Biases course on Structured LLM Outputs 9:45 - The original JEPA architecture 27:30 - V-JEPA Concept 33:15 - V-JEPA Architecture 44:30 - Experimental Results 46:30 - Qualitative Evaluation via Decoding Blog: https://ai.meta.com/blog/v-jepa-yann-... Paper: https://ai.meta.com/research/publicat... Abstract: This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sourc …...more
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V-JEPA: Revisiting Feature Prediction for Learning Visual Representations from Video (Explained)
1.2KLikes
47,991Views
2024Feb 19
#vjepa #meta #unsupervisedlearning V-JEPA is a method for unsupervised representation learning of video data by using only latent representation prediction as objective function. Weights & Biases course on Structured LLM Outputs: https://wandb.me/course-yannic OUTLINE: 0:00 - Intro 1:45 - Predictive Feature Principle 8:00 - Weights & Biases course on Structured LLM Outputs 9:45 - The original JEPA architecture 27:30 - V-JEPA Concept 33:15 - V-JEPA Architecture 44:30 - Experimental Results 46:30 - Qualitative Evaluation via Decoding Blog: https://ai.meta.com/blog/v-jepa-yann-... Paper: https://ai.meta.com/research/publicat... Abstract: This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision. The models are trained on 2 million videos collected from public datasets and are evaluated on downstream image and video tasks. Our results show that learning by predicting video features leads to versatile visual representations that perform well on both motion and appearance-based tasks, without adaption of the model’s parameters; e.g., using a frozen backbone, our largest model, a ViT-H/16 trained only on videos, obtains 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet1K. Authors: Adrien Bardes Quentin Garrido Xinlei Chen Michael Rabbat Yann LeCun Mido Assran Nicolas Ballas Jean Ponce Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube:    / yannickilcher   Twitter:   / ykilcher   Discord: https://ykilcher.com/discord LinkedIn:   / ykilcher   If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon:   / yannickilcher   Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

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Yannic Kilcher

296K subscribers