3 papers
cs.CV2026
Task Alignment: A Simple Proxy for Practical Model Merging Across Diverse Vision Tasks
Pau de Jorge, César Roberto de Souza, Björn Michele +5
Efficiently merging several models fine-tuned for different tasks, but stemming from the same pretrained base model, is of great practical interest. Despite extensive prior work, m…
cs.CV2025
LUDVIG: Learning-Free Uplifting of 2D Visual Features to Gaussian Splatting Scenes
Juliette Marrie, Romain Menegaux, Michael Arbel +2
We address the problem of extending the capabilities of vision foundation models such as DINO, SAM, and CLIP, to 3D tasks. Specifically, we introduce a novel method to uplift 2D im…
cs.CV2025
DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers
Mert Bulent Sariyildiz, Philippe Weinzaepfel, Thomas Lucas +3
Recent multi-teacher distillation methods have unified the encoders of multiple foundation models into a single encoder, achieving competitive performance on core vision tasks like…