3 citations · 7 across the 6 of their papers we have counts for
6 papers
Halfway to 3D: Ensembling 2.5D and 3D Models for Robust COVID-19 CT Diagnosis
Tuan-Anh Yang, Bao V. Q. Bui, Chanh-Quang Vo-Van +1
We propose a deep learning framework for COVID-19 detection and disease classification from chest CT scans that integrates both 2.5D and 3D representations to capture complementary…
SHREC 2025: Protein surface shape retrieval including electrostatic potential
Taher Yacoub, Camille Depenveiller, Atsushi Tatsuma +25
This SHREC 2025 track dedicated to protein surface shape retrieval involved 9 participating teams. We evaluated the performance in retrieval of 15 proposed methods on a large datas…
The Fourth Monocular Depth Estimation Challenge
Anton Obukhov, Matteo Poggi, Fabio Tosi +54
This paper presents the results of the fourth edition of the Monocular Depth Estimation Challenge (MDEC), which focuses on zero-shot generalization to the SYNS-Patches benchmark, a…
MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image Classification
Tuan-Anh Yang, Truong-Son Hy, Phuong D. Dao
This paper introduces a novel multiscale object-based graph neural network called MOB-GCN for hyperspectral image (HSI) classification. The central aim of this study is to enhance…
TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval
Trung-Nghia Le, Tam V. Nguyen, Minh-Quan Le +30
3D object retrieval is an important yet challenging task that has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issu…
SketchANIMAR: Sketch-based 3D Animal Fine-Grained Retrieval
Trung-Nghia Le, Tam V. Nguyen, Minh-Quan Le +31
The retrieval of 3D objects has gained significant importance in recent years due to its broad range of applications in computer vision, computer graphics, virtual reality, and aug…