1 citations · 1 across the 1 of their papers we have counts for
7 papers
Domain-Division based Progressive Learning for Source-Free Domain Adaptation
Pan Liu, Jing Li, Meng Zhao +3
With growing privacy and portability concerns, source-free domain adaptation requires only a source pre-trained model and an unlabeled target domain, allowing for effective adaptat…
LFM: Leveraging Foundation Models for Source-Free Universal Domain Adaptation
Jing Li, Pan Liu, Meng Zhao +7
Source-free universal domain adaptation (SF-UniDA) adapts a pre-trained source model to an unlabeled target domain under both covariate and label shifts, without access to source d…
CoVar: Confidence-Variance-Guided Pseudo-Label Selection for Semi-Supervised Learning
Jinshi Liu, Lei He, Pan Liu
Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class…
An Industrial-Scale Insurance LLM Achieving Verifiable Domain Mastery and Hallucination Control without Competence Trade-offs
Qian Zhu, Xinnan Guo, Jingjing Huo +5
Adapting Large Language Models (LLMs) to high-stakes vertical domains like insurance presents a significant challenge: scenarios demand strict adherence to complex regulations and…
Software-Hardware Co-optimization for Modular E2E AV Paradigm: A Unified Framework of Optimization Approaches, Simulation Environment and Evaluation Metrics
Chengzhi Ji, Xingfeng Li, Zhaodong Lv +4
Modular end-to-end (ME2E) autonomous driving paradigms combine modular interpretability with global optimization capability and have demonstrated strong performance. However, exist…
GContextFormer: A global context-aware hybrid multi-head attention approach with scaled additive aggregation for multimodal trajectory prediction
Yuzhi Chen, Yuanchang Xie, Lei Zhao +3
Multimodal trajectory prediction generates multiple plausible future trajectories to address vehicle motion uncertainty from intention ambiguity and execution variability. However,…