Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition

As a fundamental aspect of human life, two-person interactions contain meaningful information about people's activities, relationships, and social settings. Human action recognition serves as the foundation for many smart applications, with a strong focus on personal privacy. However, recognizing two-person interactions poses more challenges due to increased body occlusion and overlap compared to single-person actions. In this paper, we propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition. Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information. To achieve this, we introduce a designed frame selection method named Interval Frame Sampling (IFS), which efficiently samples frames from videos, capturing more discriminative information in a relatively short processing time. Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions. Finally, we apply a transformer to perform self-attention on the learned features for the final classification. Extensive experiments are conducted on two large-scale datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The results show that our network outperforms state-of-the-art approaches across all standard evaluation settings

New forming method of manufacturing cylindrical parts with nano/ultrafine grained structures by power spinning based on small plastic strains

A new spinning method to manufacture the cylindrical parts with nano/ultrafine grained structures is proposed, which consists of quenching, power spinning and recrystallization annealing. The microstructural evolution during the different process stages and macroforming quality of the spun parts made of ASTM 1020 steel are investigated. The results show that the microstructures of the ferrites and pearlites in the ASTM 1020 steel are transformed to the lath martensites after quenching. The martensite laths obtained by quenching are refined to 87 nm and a small amount of nanoscale deformation twins with an average thickness of 20 nm is generated after performing a 3-pass stagger spinning with 55% thinning ratio of wall thickness, where the equivalent strain required is only 0.92. The equiaxial ferritic grains with an average size of 160 nm and nano-carbides are generated by subsequent recrystallization annealing at 480°C for 30 min. The spun parts with high dimensional precision and low surface roughness are obtained by the forming method developed in this work, combining quenching with 3-pass stagger spinning and recrystallization annealing