12 citations · 16 across the 12 of their papers we have counts for
5 papers · 1 filter
Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model
Shuchang Lyu, Qi Zhao, Guangliang Cheng +4
Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation (UDA-RSSeg) addresses the challenge of adapting a model trained on source domain data to target domain sampl…
BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation
Zheng Zhou, Wenquan Feng, Shuchang Lyu +3
Dataset Distillation (DD) is an emerging technique that compresses large-scale datasets into significantly smaller synthesized datasets while preserving high test performance and e…
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving
Sihao Wu, Jiaxu Liu, Xiangyu Yin +5
The integration of Large Language Models (LLMs) into autonomous driving systems demonstrates strong common sense and reasoning abilities, effectively addressing the pitfalls of pur…
OVGNet: A Unified Visual-Linguistic Framework for Open-Vocabulary Robotic Grasping
Li Meng, Zhao Qi, Lyu Shuchang +4
Recognizing and grasping novel-category objects remains a crucial yet challenging problem in real-world robotic applications. Despite its significance, limited research has been co…
BACON: Bayesian Optimal Condensation Framework for Dataset Distillation
Zheng Zhou, Hongbo Zhao, Guangliang Cheng +4
Dataset Distillation (DD) aims to distill knowledge from extensive datasets into more compact ones while preserving performance on the test set, thereby reducing storage costs and…