6 papers
HGA-Quant: Heterogeneity-Aware Quantization for AutoRegressive Visual Generation Models
Xinmeng Liu, Xianglong Yan, Kaicheng Yang +2
Recently, AutoRegressive Visual Generation (ARVG) models have emerged as a competitive paradigm for high-quality image synthesis. However, their substantial computational and memor…
Amplified moments and a refinement of the quasi-Riemann hypothesis
Hejian Sang, Yuanda Xu
Recently, OpenAI announced a uniform zero-free half-plane for finite-order Hecke -functions over , together with its transfer to Dirichlet -funct…
HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets
Lulu Ban, Tao Zhu, Xiangqing Lu +7
Cross-dataset Human Activity Recognition (HAR) suffers from limited model generalization, hindering its practical deployment. To address this critical challenge, inspired by the su…
PTMs-TSCIL Pre-Trained Models Based Class-Incremental Learning
Yuanlong Wu, Mingxing Nie, Tao Zhu +3
Class-incremental learning (CIL) for time series data faces critical challenges in balancing stability against catastrophic forgetting and plasticity for new knowledge acquisition,…
FLOW: Fusing and Shuffling Global and Local Views for Cross-User Human Activity Recognition with IMUs
Qi Qiu, Tao Zhu, Furong Duan +3
Inertial Measurement Unit (IMU) sensors are widely employed for Human Activity Recognition (HAR) due to their portability, energy efficiency, and growing research interest. However…
P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer
Shuangjian Li, Tao Zhu, Mingxing Nie +3
Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data. This limits their usefulness in many fields such as…