The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …

Federated learning (FL) is an emerging distributed machine learning method that empowers
in-situ model training on decentralized edge devices. However, multiple simultaneous FL …

Cued Speech (CS) is a visual coding tool to encode spoken languages at the phonetic level,
which combines lip-reading and hand gestures to effectively assist communication among …

Heterogeneous pedestrian images are captured by visible and infrared cameras with
different spectrums, which play an important role in night-time video surveillance. However …

Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

We present COALA, a vision-centric Federated Learning (FL) platform, and a suite of
benchmarks for practical FL scenarios, which we categorize into three levels: task, data, and …

Data is the essential fuel for deep neural networks (DNNs), and its quality affects the
practical performance of DNNs. In real-world training scenarios, the successful …

Freeform handwriting authentication verifies a person's identity from their writing style and
habits in messy handwriting data. This technique has gained widespread attention in recent …

Most person re-identification (re-ID) approaches are based on representation learning of
pedestrian images, which assume that the person's appearance captured by cameras in the …