1/n 🚀New paper out - accepted at #ICCV2025!

Introducing DIP: unsupervised post-training that enhances dense features in pretrained ViTs for dense in-context scene understanding

Below: Low-shot in-context semantic segmentation examples. DIP features outperform DINOv2!

Big thanks to @loickch.bsky.social and @louisserrano.bsky.social for this amazing collaboration!

Despite the absence of high-resolution ground truth features, we find that training JAFAR at low upsampling ratios and resolutions generalizes remarkably well to significantly higher output scales (4/n)

Given an image, JAFAR builds high-res queries at the target resolution and low-res, semantically enriched keys using spatial feature modulation to power a cross-resolution attention mechanism that interpolates the low-resolution features from the foundation vision encoder (3/n)

Foundation Vision Encoders produce rich, semantically meaningful features—but at low spatial resolution—requiring feature upsampling for dense vision tasks. JAFAR tackles this in a single step, without relying on any downstream task supervision (2/n)

🚀Thrilled to introduce JAFAR—a lightweight, flexible, plug-and-play module that upsamples features from any Foundation Vision Encoder to any desired output resolution (1/n)

Paper : arxiv.org/abs/2506.11136
Project Page: jafar-upsampler.github.io
Github: github.com/PaulCouairon...