25 citations · 91 across the 16 of their papers we have counts for
16 papers
Featurising Pixels from Dynamic 3D Scenes with Linear In-Context Learners
Nikita Araslanov, Martin Sundermeyer, Hidenobu Matsuki +2
One of the most exciting applications of vision models involve pixel-level reasoning. Despite the abundance of vision foundation models, we still lack representations that effectiv…
TAPNext++: What's Next for Tracking Any Point (TAP)?
Sebastian Jung, Artem Zholus, Martin Sundermeyer +6
Tracking-Any-Point (TAP) models aim to track any point through a video which is a crucial task in AR/XR and robotics applications. The recently introduced TAPNext approach proposes…
Self-supervised Latent Space Optimization with Nebula Variational Coding
Yida Wang, David Joseph Tan, Nassir Navab +1
Deep learning approaches process data in a layer-by-layer way with intermediate (or latent) features. We aim at designing a general solution to optimize the latent manifolds to imp…
Towards Real-Time Open-Vocabulary Video Instance Segmentation
Bin Yan, Martin Sundermeyer, David Joseph Tan +2
In this paper, we address the challenge of performing open-vocabulary video instance segmentation (OV-VIS) in real-time. We analyze the computational bottlenecks of state-of-the-ar…
RaNeuS: Ray-adaptive Neural Surface Reconstruction
Yida Wang, David Joseph Tan, Nassir Navab +1
Our objective is to leverage a differentiable radiance field \eg NeRF to reconstruct detailed 3D surfaces in addition to producing the standard novel view renderings. There have be…
SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
Lukas Hoyer, David Joseph Tan, Muhammad Ferjad Naeem +2
In semi-supervised semantic segmentation, a model is trained with a limited number of labeled images along with a large corpus of unlabeled images to reduce the high annotation eff…