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Happy to share our latest work: V-JEPA 2, a world model trained from millions of videos, that enables visual understanding, planning and physical reasoning!
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I’m thrilled to share that I have joined AMI Labs as a Member of Technical Staff! 🚀
We are building the future of AI in the physical world and actively hiring from all our offices. Reach out to me if you are interested in research roles in our Paris office!
We are building the future of AI in the physical world and actively hiring from all our offices. Reach out to me if you are interested in research roles in our Paris office!
Job alert 🚨 My team @AIatMeta is looking for a PhD intern to join us in 2025 in Paris. We are working on self-supervised learning from video, world modelling and JEPA ! Apply here or reach out directly:
metacareers.com/jobs/168411027…
metacareers.com/jobs/168411027…
Amazing opportunity @AIatMeta
I am recruiting a PhD intern to work with me @AIatMeta (GenAI) around LLMs, tool use, and / or the GAIA benchmark.View quoted post on X
Ideal profile: near the end of your PhD, willing to be based in Paris, starting in May/June 2024. DM if you’re interested!
Today I am thrilled to announce the project I have been working on for a while, V-JEPA, a vision model solely trained from large-scale video data in a self-supervised way, with a Joint-Embedding Predictive Architecture !
Today we’re releasing V-JEPA, a method for teaching machines to understand and model the physical world by watching videos. This work is another important step towards @ylecun’s outlined vision of AI models that use a learned understanding of the world to plan, reason and accomplish complex tasks.
Details ➡️ bit.ly/49fCeaM
We're releasing a collection of V-JEPA vision models trained with a feature prediction objective using self-supervised learning. The models are able to understand and predict what is going on in a video, even with limited information.
It learns by predicting missing or obscured parts of a video in its internal feature space. Unlike generative approaches that fill in missing pixels, this flexible approach enables up to 6x improvements in training and sample efficiency.
The models were pre-trained on entirely unlabeled data, and a small amount of labeled data can be used to train a task-specific prediction head on top after pre-training.
Our results show that, using a frozen backbone, our top V-JEPA models achieve 82.0% on Kinetics-400, 72.2% on Something-Something-v2 and 77.9% on ImageNet1K — competitive with or exceeding previous leading video models.
We believe that this work is an important milestone on the path to advancing machine intelligence.View quoted post on X
