Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
This addresses the problem of reducing reliance on manual data augmentations for computer vision researchers, though it is incremental as it builds on existing self-supervised and transformer-based approaches.
The paper tackles learning semantic image representations without hand-crafted augmentations by introducing I-JEPA, a non-generative self-supervised method that predicts target block representations from a context block, achieving strong downstream performance on tasks like linear classification and object counting.
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.