1/n Introducing ReDi (Representation Diffusion): a new generative approach that leverages a diffusion model to jointly capture – Low-level image details (via VAE latents) – High-level semantic features (via DINOv2)🧵

🧵 Excited to share our latest work: FUTURIST - A unified transformer architecture for multimodal semantic future prediction, is accepted to #CVPR2025! Here's how it works (1/n) 👇 Links to the arxiv and github below

ILIAS: Instance-Level Image retrieval At Scale @gkordo.bsky.social, Vladan Stojnić @annetka.bsky.social Pavel Šuma, Nikolaos-Antonios Ypsilantis @nikos-efth.bsky.social Zakaria Laskar,Jiří Matas, Ondřej Chum, @gtolias.bsky.social tl;dr: SigLIP rules. Lots of ablations arxiv.org/abs/2502.11748 1/

EQ-VAE: Such a simple & cool trick to regularize multiple kinds of autoencoders: align reconstruction of transformed latents w/ the corresponding transformed inputs. 🚀REPA: 4x training speedup 🚀MaskGIT: 2x training speedup 🚀DiT-XL/2: 7x faster convergence Kudos @nicolabourbaki.bsky.social et al.

1/n🚀If you’re working on generative image modeling, check out our latest work! We introduce EQ-VAE, a simple yet powerful regularization approach that makes latent representations equivariant to spatial transformations, leading to smoother latents and better generative models.👇

For PhD and MSc students interested in a research visit to Prague/VRG in 2025: we're open to hosting short-term collaborations or internships on a range of computer vision topics. If this sounds exciting, reach out by e-mail! We'd love to discuss potential projects. Some examples 🧵 #Internship #CV

1/n 🚀 Excited to share our latest work: DINO-Foresight, a new framework for predicting the future states of scenes using Vision Foundation Model features! Links to the arXiv and Github 👇