DIP: Unsupervised Dense In-Context Post-training of Visual Representations

Sophia Sirko-Galouchenko, Spyros Gidaris, Antonin Vobecky, Andrei Bursuc, Nicolas THOME

Abstract: We introduce DIP, a novel unsupervised post-training method designed to enhance dense representations in largescale pretrained vision encoders for in-context scene understanding. Unlike prior approaches using complex self-distillation architectures, our method trains the vision encoder using pseudo-tasks that simulate downstream in-context scenarios, inspired by meta-learning principles. To enable post-training on unlabeled data, we propose an automatic mechanism for generating in-context tasks that combines a pretrained diffusion model and the vision encoder. DIP is simple, unsupervised, and computationally efficient, requiring under 9 hours on a single A100 GPU. By learning dense representations through pseudo in-context tasks, it achieves strong performance across a variety of downstream real-world in-context scene understanding tasks. It outperforms both the initial vision encoder and prior methods, offering a practical and effective solution for improving dense representations.