15 citations · 22 across the 3 of their papers we have counts for
6 papers
D2Fusion: Dual-domain Fusion with Feature Superposition for Deepfake Detection
Xueqi Qiu, Xingyu Miao, Fan Wan +5
Deepfake detection is crucial for curbing the harm it causes to society. However, current Deepfake detection methods fail to thoroughly explore artifact information across differen…
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation
Yumin Zhang, Yan Gao, Haoran Duan +4
Transformer-based foundation models (FMs) have recently demonstrated remarkable performance in medical image segmentation. However, scaling these models is challenging due to the l…
A Circular Construction Product Ontology for End-of-Life Decision-Making
Kwabena Adu-Duodu, Stanly Wilson, Yinhao Li +8
Efficient management of end-of-life (EoL) products is critical for advancing circularity in supply chains, particularly within the construction industry where EoL strategies are hi…
Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields
Xingyu Miao, Haoran Duan, Yang Bai +5
In this work, we propose a method that leverages CLIP feature distillation, achieving efficient 3D segmentation through language guidance. Unlike previous methods that rely on mult…
Exemplar-condensed Federated Class-incremental Learning
Rui Sun, Yumin Zhang, Varun Ojha +4
We propose Exemplar-Condensed federated class-incremental learning (ECoral) to distil the training characteristics of real images from streaming data into informative rehearsal exe…
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data
Xiufang Shi, Wei Zhang, Yuheng Li +5
In federated learning, the heterogeneity of client data has a great impact on the performance of model training. Many heterogeneity issues in this process are raised by non-indepen…