5 papers
OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition
Xiangqing Lu, Tao Zhu, Liang Yu +6
LLMs increasingly support sensor-based human activity recognition (HAR) by linking motion signals with activity semantics and physical knowledge. Retrieval-augmented generation (RA…
A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model
Kaidong Zhang, Jian Zhang, Rongtao Xu +20
Vision-Language-Action (VLA) models have emerged as a powerful paradigm for open-world robot manipulation, but their practical deployment is often constrained by cost: billion-scal…
LCGC: Learning from Consistency Gradient Conflicting for Class-Imbalanced Semi-Supervised Debiasing
Weiwei Xing, Yue Cheng, Hongzhu Yi +5
Classifiers often learn to be biased corresponding to the class-imbalanced dataset, especially under the semi-supervised learning (SSL) set. While previous work tries to appropriat…
LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data
Jiajun Zhang, Boyang Qiang, Xiaoyu Guo +3
Discovering the underlying Directed Acyclic Graph (DAG) from time series observational data is highly challenging due to the dynamic nature and complex nonlinear interactions betwe…
Neuromorphic spatiotemporal optical flow: Enabling ultrafast visual perception beyond human capabilities
Shengbo Wang, Jingwen Zhao, Tongming Pu +14
Optical flow, inspired by the mechanisms of biological visual systems, calculates spatial motion vectors within visual scenes that are necessary for enabling robotics to excel in c…