activity
20242026
collaborators
Showing cs.CVShow all

7 papers · 1 filter

cs.CV2026

VGGT-

Jianyuan Wang, Minghao Chen, Shangzhan Zhang +7

Recent feed-forward reconstruction models, such as VGGT, have proven competitive with traditional optimization-based reconstructors while also providing geometry-aware features use…

cs.CV2026

AdaDINO: Context-Adaptive DINO-Distilled Vision Foundation Models for Efficient Open-Vocabulary Edge Inference

Yiwei Zhao, Yi Zheng, Huapeng Su +9

Always-on contextual AI runs language-aligned vision foundation models (VFMs) on edge devices, where the on-device model is the dominant continuous compute cost under strict latenc…

cs.CV2026

Efficient Universal Perception Encoder

Chenchen Zhu, Saksham Suri, Cijo Jose +8

Running AI models on smart edge devices can unlock versatile user experiences, but presents challenges due to limited compute and the need to handle multiple tasks simultaneously.…

cs.CV2026

CHMv2: Improvements in Global Canopy Height Mapping using DINOv3

John Brandt, Seungeun Yi, Jamie Tolan +9

Accurate canopy height information is essential for quantifying forest carbon, monitoring restoration and degradation, and assessing habitat structure, yet high-fidelity measuremen…

cs.CV2025

DINOv3

Oriane Siméoni, Huy V. Vo, Maximilian Seitzer +23

Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. B…

cs.CV2025

Back to the Features: DINO as a Foundation for Video World Models

Federico Baldassarre, Marc Szafraniec, Basile Terver +6

We present DINO-world, a powerful generalist video world model trained to predict future frames in the latent space of DINOv2. By leveraging a pre-trained image encoder and trainin…