Action coordination in human structure is indispensable for the spatial constraints of 2D joints to recover 3D pose. Usually, action coordination is represented as a long-range dependence among body parts. However, there are two main challenges in modeling long-range dependencies. First, joints should not only be constrained by other individual joints but also be modulated by the body parts. Second, existing methods make networks deeper to learn dependencies between non-linked parts. They introduce uncorrelated noise and increase the model size. In this paper, we utilize a pyramid structure to better learn potential long-range dependencies. It can capture the correlation across joints and groups, which complements the context of the human sub-structure. In an effective cross-scale way, it captures the pyramid-structured long-range dependence. Specifically, we propose a novel Pyramid Graph Attention (PGA) module to capture long-range cross-scale dependencies. It concatenates information from various scales into a compact sequence, and then computes the correlation between scales in parallel. Combining PGA with graph convolution modules, we develop a Pyramid Graph Transformer (PGFormer) for 3D human pose estimation, which is a lightweight multi-scale transformer architecture. It encapsulates human sub-structures into self-attention by pooling. Extensive experiments show that our approach achieves lower error and smaller model size than state-of-the-art methods on Human3.6 M and MPI-INF-3DHP datasets.

The frequent occurrence of nonagriculturalization events has posed significant challenges to global food security and sustainable development. Despite the emerging deep learning (DL) algorithms demonstrating effective capability in capturing changes from remote sensing imagery, they have not consistently maintained favorable performance in cropland semantic change detection (CropSCD) tasks. The primary challenge lies in the natural contradiction between the diverse change classes and the sparse availability of change samples. Furthermore, the scarcity of CropSCD datasets also restricts the capabilities of data-driven models. Therefore, in order to encode diverse semantics from a small amount of change pixels, a memory-guided network (MeGNet) dedicated to CropSCD tasks is proposed. In particular, a class-aware memory module is introduced in the MeGNet to preserve change semantics, which can guide the model to distinguish different change classes. Moreover, a high-resolution CropSCD dataset is also constructed to alleviate the issue of insufficient dataset. The CropSCD dataset comprises 4141 pairs of images, each with a size of$512\times 512$, and is annotated with corresponding labels for eight cropland change classes. Comparative experiments have substantiated the superiority of MeGNet over current state-of-the-art (SOTA) methods, with the highest mean-F1 and mean intersection over union (mIoU) of 71.44% and 58.01% on the high-resolution semantic change detection (HRSCD) dataset, and those of 55.42% and 42.14% on the CropSCD dataset. These results have validated the feasibility and potential of the proposed MeGNet and CropSCD dataset in CropSCD tasks.