2 citations · 3 across the 3 of their papers we have counts for
5 papers
FedBST: Bilateral Self-Training for Unsupervised Federated Classification
Aijing Yu, Kuangpu Guo, Jian Liang +1
Unsupervised federated learning with Vision-Language Models (e.g., CLIP) offers a promising solution for label-free classification, yet its performance often degrades under severe…
Refining Open-Vocabulary Semantic Segmentation via Regional Semantics and Visual Prototypes
Aijing Yu, Zhengbo Wang, Lijun Sheng +2
Contrastive Language-Image Pre-training (CLIP) has shown superior performance in the image-level open-vocabulary classification task, benefiting from its powerful alignment of visu…
Topological collapse of higher-order interactions bottlenecks collective intelligence in AI agent societies
Shuo Lu, Weicheng Meng, Aijing Yu +4
Current paradigms in artificial intelligence concentrate on scaling the capabilities of individual models, yet the collective behaviour of interacting agents is shaped by the topol…
Which Model to Transfer? A Survey on Transferability Estimation
Yuhe Ding, Bo Jiang, Aijing Yu +2
Transfer learning methods endeavor to leverage relevant knowledge from existing source pre-trained models or datasets to solve downstream target tasks. With the increase in the sca…
LAMP-HQ: A Large-Scale Multi-Pose High-Quality Database and Benchmark for NIR-VIS Face Recognition
Aijing Yu, Haoxue Wu, Huaibo Huang +2
Near-infrared-visible (NIR-VIS) heterogeneous face recognition matches NIR to corresponding VIS face images. However, due to the sensing gap, NIR images often lose some identity in…