22 citations · 34 across the 8 of their papers we have counts for
8 papers
Compressing Multi-Task Model for Autonomous Driving via Pruning and Knowledge Distillation
Jiayuan Wang, Q. M. Jonathan Wu, Ning Zhang +2
Autonomous driving systems rely on panoptic perception to jointly handle object detection, drivable area segmentation, and lane line segmentation. Although multi-task learning is a…
A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles
Jiayuan Wang, Farhad Pourpanah, Q. M. Jonathan Wu +1
Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in co…
Few-Shot Inspired Generative Zero-Shot Learning
Md Shakil Ahamed Shohag, Q. M. Jonathan Wu, Farhad Pourpanah
Generative zero-shot learning (ZSL) methods typically synthesize visual features for unseen classes using predefined semantic attributes, followed by training a fully supervised cl…
SAM2Auto: Auto Annotation Using FLASH
Arash Rocky, Q. M. Jonathan Wu
Vision-Language Models (VLMs) lag behind Large Language Models due to the scarcity of annotated datasets, as creating paired visual-textual annotations is labor-intensive and expen…
One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labeling
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Federated Learning (FL) is a promising approach for privacy-preserving collaborative learning. However, it faces significant challenges when dealing with domain shifts, especially…
Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data. Despite recent advance…
Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation
Salim Rezvani, Farhad Pourpanah, Chee Peng Lim +1
This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants…
An Ensemble Semi-Supervised Adaptive Resonance Theory Model with Explanation Capability for Pattern Classification
Farhad Pourpanah, Chee Peng Lim, Ali Etemad +1
Most semi-supervised learning (SSL) models entail complex structures and iterative training processes as well as face difficulties in interpreting their predictions to users. To ad…