4 papers
From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection
Lincan Cai, Jingxuan Kang, Shuang Li +4
Pretrained vision-language models (VLMs), e.g., CLIP, demonstrate impressive zero-shot capabilities on downstream tasks. Prior research highlights the crucial role of visual augmen…
Large Language Models Enhanced Hyperbolic Space Recommender Systems
Wentao Cheng, Zhida Qin, Zexue Wu +2
Large Language Models (LLMs) have attracted significant attention in recommender systems for their excellent world knowledge capabilities. However, existing methods that rely on Eu…
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
Yuepeng He, Pengzhan Zhou, Yijun Zhai +4
Vehicle speed prediction is crucial for intelligent transportation systems, promoting more reliable autonomous driving by accurately predicting future vehicle conditions. Due to va…
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles
Yijun Zhai, Pengzhan Zhou, Yuepeng He +5
The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great pote…