Semantic-Disentangled Transformer With Noun-Verb Embedding for Compositional Action Recognition.

Recognizing actions performed on unseen objects, known as Compositional Action Recognition (CAR), has attracted increasing attention in recent years. The main challenge is to overcome the distribution shift of "action-objects" pairs between the training and testing sets. Previous works for CAR usually introduce extra information (e.

Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semisupervised Skeleton-Based Action Recognition.

Contrastive learning has been successfully leveraged to learn action representations for addressing the problem of semisupervised skeleton-based action recognition. However, most contrastive learning-based methods only contrast global features mixing spatiotemporal information, which confuses the spatial- and temporal-specific information reflecting different semantic at the frame level and joint level. Thus, we propose a novel spatiotemporal decouple-and-squeeze contrastive learning (SDS-CL) framework to comprehensively learn more abundant representations of skeleton-based actions by jointly contrasting spatial-squeezing features, temporal-squeezing features, and global features.

[Effects of no-tillage and fertilization on paddy soil CH4 and N2O emissions and their greenhouse effect in central China].

By using static chamber-gas chromatographic techniques, the CH4 and N2O emissions from the paddy soil in southeast Hubei were measured. Four treatments were installed, i.e.