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Introducing VL-JEPA: Vision-Language Joint Embedding Predictive Architecture for streaming, live action recognition, retrieval, VQA, and classification tasks with better performance and higher efficiency than large VLMs. • VL-JEPA is the first non-generative model that can perform general-domain vision-language tasks in real-time, built on a joint embedding predictive architecture. •...

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Pascale Fung

7,509 subscribers

90,522 просмотров • 10 месяцев назад •via X (Twitter)

ОбразованиеНаука и технологии
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Комментарии: 10

Фото профиля harpreet
harpreet10 месяцев назад

This looks interesting. Do you have a repo or model weights released yet?

Фото профиля Himanshu Kumar
Himanshu Kumar10 месяцев назад

VL-JEPA predicts joint embeddings; model lacks generative capabilities.

Фото профиля Dhruv Diddi
Dhruv Diddi10 месяцев назад

Thanks for sharing! 🤗🦾💯

Фото профиля Ananth Kashyap
Ananth Kashyap10 месяцев назад

is the model released yet? would love to try it out

Фото профиля Ab Ospan
Ab Ospan10 месяцев назад

Amazing work, congrats! 🎉 Do you have a repo or a demo to try?

Фото профиля Archaneya
Archaneya10 месяцев назад

@ylecun Ohh this is cool...

Фото профиля Suresh
Suresh10 месяцев назад

Joint embeddings bridge vision and language, non-generative efficiency unlocks real-time multimodal

Фото профиля Vignesh S.
Vignesh S.10 месяцев назад

Any repo to try this on?

Фото профиля Pronouns: I/My/Mine
Pronouns: I/My/Mine9 месяцев назад

PK fungi steals like PK

Фото профиля Chris
Chris10 месяцев назад

@bycloudai

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Our vision is for AI that uses world models to adapt in new and dynamic environments and efficiently learn new skills. We’re sharing V-JEPA 2, a new world model with state-of-the-art performance in visual understanding and prediction. V-JEPA 2 is a 1.2 billion-parameter model, trained on video, that can enable zero-shot planning in robots—allowing them to plan and execute tasks in unfamiliar environments. Learn more about V-JEPA 2 ➡️ As we continue working toward our goal of achieving advanced machine intelligence (AMI), we’re also releasing three new benchmarks for evaluating how well existing models can reason about the physical world from video. Learn more and download the new benchmarks ➡️

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310,986 просмотров • 1 год назад

Hey Yann LeCun we just ran an action conditioned JEPA model on the OpenArms in less than 2 days
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Hey Yann LeCun we just ran an action conditioned JEPA model on the OpenArms in less than 2 days

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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 Yann LeCun’s outlined vision of AI models that use a learned understanding of the world to plan, reason and accomplish complex tasks. Details ➡️ 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.
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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 Yann LeCun’s outlined vision of AI models that use a learned understanding of the world to plan, reason and accomplish complex tasks. Details ➡️ 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.

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