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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. •... We demonstrate in controlled experiments that VL-JEPA, trained with latent space embedding prediction, outperforms VLMs that rely on data space token prediction. • We show that VL-JEPA delivers significant efficiency gains over VLMs for online video streaming applications, thanks to its non-autoregressive design and native support for selective decoding. • We highlight that our VL-JEPA model, with an unified model architecture, can effectively handle a wide range of classification, retrieval, and VQA tasks at the same time. by Delong Chen (陈德龙) Mustafa Shukor Théo Moutakanni Willy Jade Lei Yu Tejaswi Kasarla Allen Bolourchi Yann LeCun Pascale Fungshow more

Pascale Fung

7,509 subscribers

90,522 views • 10 months ago •via X (Twitter)

Education Science & Technology

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10 Comments

harpreet10 months ago

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

Himanshu Kumar10 months ago

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

Dhruv Diddi10 months ago

Thanks for sharing! 🤗🦾💯

Ananth Kashyap10 months ago

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

Ab Ospan10 months ago

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

Archaneya10 months ago

@ylecun Ohh this is cool...

Suresh10 months ago

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

Vignesh S.10 months ago

Any repo to try this on?

Pronouns: I/My/Mine9 months ago

PK fungi steals like PK

Chris10 months ago

@bycloudai

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310,986 views • 1 year ago

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

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