2.7k citations · 3.5k across the 17 of their papers we have counts for
5 papers · 1 filter
One-shot federated unsupervised domain adaptation with Smoothed Knowledge Distillation and teacher refinement
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Federated unsupervised domain adaptation (FUDA) addresses the challenge of adapting models to an unlabeled target domain using decentralized source domains while preserving data pr…
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…
A Review of Deep Learning for Video Captioning
Moloud Abdar, Meenakshi Kollati, Swaraja Kuraparthi +8
Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work in the fields of computer vision, natural language processing (NLP), linguistics, and…
A Review of Generalized Zero-Shot Learning Methods
Farhad Pourpanah, Moloud Abdar, Yuxuan Luo +5
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To add…