Haibo Li^*, Yongbo Yu, Zhenbo Zhao, Xiaokang Tang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 653-676, 2024, DOI:10.32604/cmc.2023.046424

Abstract Accurate forecasting of time series is crucial across various domains. Many prediction tasks rely on effectively segmenting, matching, and time series data alignment. For instance, regardless of time series with the same granularity, segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy. However, these events of varying granularity frequently intersect with each other, which may possess unequal durations. Even minor differences can result in significant errors when matching time series with future trends. Besides, directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead… More >

Zheng Shi, Wanru Song^*, Junhao Shan, Feng Liu

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3467-3488, 2023, DOI:10.32604/cmc.2023.045849

Abstract Visible-infrared Cross-modality Person Re-identification (VI-ReID) is a critical technology in smart public facilities such as cities, campuses and libraries. It aims to match pedestrians in visible light and infrared images for video surveillance, which poses a challenge in exploring cross-modal shared information accurately and efficiently. Therefore, multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes. However, existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks, the fusion module. This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature… More >