21 citations · 48 across the 7 of their papers we have counts for
8 papers · 1 filter
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…
SoftPool++: An Encoder-Decoder Network for Point Cloud Completion
Yida Wang, David Joseph Tan, Nassir Navab +1
We propose a novel convolutional operator for the task of point cloud completion. One striking characteristic of our approach is that, conversely to related work it does not requir…
Learning Local Displacements for Point Cloud Completion
Yida Wang, David Joseph Tan, Nassir Navab +1
We propose a novel approach aimed at object and semantic scene completion from a partial scan represented as a 3D point cloud. Our architecture relies on three novel layers that ar…
Transformers in Action: Weakly Supervised Action Segmentation
John Ridley, Huseyin Coskun, David Joseph Tan +2
The video action segmentation task is regularly explored under weaker forms of supervision, such as transcript supervision, where a list of actions is easier to obtain than dense f…
A Divide et Impera Approach for 3D Shape Reconstruction from Multiple Views
Riccardo Spezialetti, David Joseph Tan, Alessio Tonioni +2
Estimating the 3D shape of an object from a single or multiple images has gained popularity thanks to the recent breakthroughs powered by deep learning. Most approaches regress the…
SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification
Yida Wang, David Joseph Tan, Nassir Navab +1
Point clouds are often the default choice for many applications as they exhibit more flexibility and efficiency than volumetric data. Nevertheless, their unorganized nature -- poin…