21 citations · 25 across the 23 of their papers we have counts for
5 papers · 1 filter
Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models
Hamidreza Dastmalchi, Aijun An, Ali Cheraghian +2
Test-time adaptation (TTA) of 3D point clouds is crucial for mitigating discrepancies between training and testing samples in real-world scenarios, particularly when handling corru…
TPCL: Task Progressive Curriculum Learning for Robust Visual Question Answering
Ahmed Akl, Abdelwahed Khamis, Zhe Wang +3
Visual Question Answering (VQA) systems are notoriously brittle under distribution shifts and data scarcity. While previous solutions-such as ensemble methods and data augmentation…
Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free Adaptor
Sahar Ahmadi, Ali Cheraghian, Morteza Saberi +4
Recent advances in deep learning for processing point clouds hold increased interest in Few-Shot Class Incremental Learning (FSCIL) for 3D computer vision. This paper introduces a…
3D Point Cloud Network Pruning: When Some Weights Do not Matter
Amrijit Biswas, Md. Ismail Hossain, M M Lutfe Elahi +4
A point cloud is a crucial geometric data structure utilized in numerous applications. The adoption of deep neural networks referred to as Point Cloud Neural Networks (PC- NNs), fo…
Backpropagation-free Network for 3D Test-time Adaptation
Yanshuo Wang, Ali Cheraghian, Zeeshan Hayder +7
Real-world systems often encounter new data over time, which leads to experiencing target domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally h…