Recent breakthroughs in vision-language models (VLMs) start a new page in the vision
community. The VLMs provide stronger and more generalizable feature embeddings …

We introduce Metric3D v2, a geometric foundation model designed for zero-shot metric
depth and surface normal estimation from single images, critical for accurate 3D recovery …

In recent decades the vision community has witnessed remarkable progress in visual
recognition partially owing to advancements in dataset benchmarks. Notably the established …

Cross-modal transformers have demonstrated superiority in various vision tasks by
effectively integrating different modalities. This paper first critiques prior token exchange …

Multi-task scene understanding aims to design models that can simultaneously predict
several scene understanding tasks with one versatile model. Previous studies typically …

Data-fusion networks have shown significant promise for RGB-thermal scene parsing.
However, the majority of existing studies have relied on symmetric duplex encoders for …

In this work, we show that synthetic data created by generative models is complementary to
computer graphics (CG) rendered data for achieving remarkable generalization …

Multi-task perception system to simultaneously perceive various kinds of objects is essential
for autonomous driving. Existing perception frameworks always rely on multi-labeled …

Recent technological advancements have significantly improved indoor autonomous vision
systems (IAVSs), underscoring the critical need to enhance their capability to interpret real …

Monocular depth estimation (MDE) is a challenging task in computer vision, often hindered
by the cost and scarcity of high-quality labeled datasets. We tackle this challenge using …