V-JEPA: The next step toward Yann LeCun’s vision of advanced machine intelligence [R]

blog: [https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/](https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/)

paper: [https://ai.meta.com/research/publications/revisiting-feature-prediction-for-learning-visual-representations-from-video/](https://ai.meta.com/research/publications/revisiting-feature-prediction-for-learning-visual-representations-from-video/)

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**Abstract:**

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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 …

abstract collection datasets examples feature image jepa machinelearning negative paper prediction public supervision text unsupervised unsupervised learning video videos vision vision models