8 papers
Cross-Visibility-Gap Identity Persistence for 4D Scene Graphs
Jiahui Hu, Li Gao, Yu Liu +1
4D scene graphs require persistent object identity when an object disappears inside an opaque carrier and later reappears as a fresh detection. When appearance and target-centric m…
Revealing After Overwriting: An Exponential POMDP OPE Lower Bound under History-Dependent Logging
Youyu Luo, Pengzhan Zhou, Zhida Qin +4
Multi-step revealing can make off-policy evaluation tractable under memoryless logging. With history-dependent logging, state decodability and target-relevant evidence can separate…
Adaptive Cross-Modal Fusion with Sparse Attention for Pedestrian Crossing Intention Prediction
Md Mahfuzur Rahman, Pengzhan Zhou, A F M Abdun Noor +5
Predicting pedestrian crossing intention is a safety-critical task for autonomous driving, yet existing approaches often rely on single-modal inputs or dense multimodal fusion stra…
FedSODA: Federated Fine-tuning of LLMs via Similarity Group Pruning and Orchestrated Distillation Alignment
Manning Zhu, Songtao Guo, Pengzhan Zhou +3
Federated fine-tuning (FFT) of large language models (LLMs) has recently emerged as a promising solution to enable domain-specific adaptation while preserving data privacy. Despite…
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…
FedAH: Aggregated Head for Personalized Federated Learning
Pengzhan Zhou, Yuepeng He, Yijun Zhai +5
Recently, Federated Learning (FL) has gained popularity for its privacy-preserving and collaborative learning capabilities. Personalized Federated Learning (PFL), building upon FL,…
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…