DIP: Unsupervised Dense In-Context Post-training of Visual
  Representations
We introduce DIP, a novel unsupervised post-training method designed to
enhance dense image representations in large-scale pretrained vision encoders
for in-context scene understanding. Unlike prior approaches that rely on
complex self-distillation architectures, our method trains the vision encoder
using pseudo-tasks that explicitly 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 itself. DIP is
simple, unsupervised, and computationally efficient, requiring less than 9
hours on a single A100 GPU. By learning dense representations through pseudo
in-context tasks, it achieves strong performance across a wide 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. Code available here:
https://github.com/sirkosophia/DIP
685ab6570e4ad7e2197587ae
Sophia Sirko-Galouchenko
claimed_verified
2025-06-24T17:44:07.015Z
67e426c3118deb3746243ce3
/avatars/cfca0c2d8e4b683bc046f4d4fe0b716a.svg
Sophia Sirko-Galouchenko
user
SophiaSirko
685ab6570e4ad7e2197587af
Spyros Gidaris
685ab6570e4ad7e2197587b0
Antonin Vobecky
685ab6570e4ad7e2197587b1
Andrei Bursuc
685ab6570e4ad7e2197587b2
Nicolas Thome
DIP: Unsupervised Dense In-Context Post-training of Visual
  Representations
We introduce DIP, a novel unsupervised post-training method designed to
enhance dense image representations in large-scale pretrained vision encoders
for in-context scene understanding. Unlike prior approaches that rely on
complex self-distillation architectures, our method trains the vision encoder
using pseudo-tasks that explicitly 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 itself. DIP is
simple, unsupervised, and computationally efficient, requiring less than 9
hours on a single A100 GPU. By learning dense representations through pseudo
in-context tasks, it achieves strong performance across a wide 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. Code available here:
https://github.com/sirkosophia/DIP
A novel unsupervised post-training method improves dense image representations using pseudo-tasks and a pretrained diffusion model for in-context scene understanding.
DIP vision encoders self-distillation diffusion model in-context tasks meta-learning dense representations scene understanding