June 18, 2023
This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample target blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transformers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction.
Written by
Quentin Duval
Piotr Bojanowski
Nicolas Ballas
Publisher
CVPR
Research Topics
Core Machine Learning
February 15, 2024
Danny Deng, Hongkuan Zhou, Hanqing Zeng, Yinglong Xia, Chris Leung (AI), Jianbo Li, Rajgopal Kannan, Viktor Prasanna
February 15, 2024
February 15, 2024
Adrien Bardes, Quentin Garrido, Xinlei Chen, Michael Rabbat, Yann LeCun, Mido Assran, Nicolas Ballas, Jean Ponce
February 15, 2024
January 09, 2024
Less Wright, Adnan Hoque
January 09, 2024
January 06, 2024
Geng Ji, Wentao Jiang, Jiang Li, Fahmid Morshed Fahid, Zhengxing Chen, Yinghua Li, Jun Xiao, Chongxi Bao, Zheqing (Bill) Zhu
January 06, 2024
Product experiences
Foundational models
Product experiences
Latest news
Foundational models