Transformer-based models have made significant progress in edge detection, but their high computational cost is prohibitive. Recently, vision Mamba have shown excellent ability in efficiently capturing long-range dependencies. Drawing inspiration from this, we propose a novel edge detector with Mamba, termed EDMB, to efficiently generate high-quality multi-granularity edges. In EDMB, Mamba is combined with a global-local architecture, therefore it can focus on both global information and fine-grained cues. The fine-grained cues play a crucial role in edge detection, but are usually ignored by ordinary Mamba. We design a novel decoder to construct learnable Gaussian distributions by fusing global features and fine-grained features. And the multi-grained edges are generated by sampling from the distributions. In order to make multi-granularity edges applicable to single-label data, we introduce Evidence Lower Bound loss to supervise the learning of the distributions. On the multi-label dataset BSDS500, our proposed EDMB achieves competitive single-granularity ODS 0.837 and multi-granularity ODS 0.851 without multi-scale test or extra PASCAL-VOC data. Remarkably, EDMB can be extended to single-label datasets such as NYUDv2 and BIPED. The source code is available at https://github.com/Li-yachuan/EDMB.

Large deformation is a key issue for deformable medical image registration. Decomposing a large deformation into several small deformations is an efficient solution. Current decomposition methods can be classed into iteration-based and non-iterative-based approaches. However, compared to iteration-based methods, non-iterative-based methods have faster inference times but registration accuracy gaps. To alleviate this limitation, we design a novel Non-iterative Pyramid Network (NIPNet). Firstly, Our Dual-domain Feature Extraction Module (DFEM) extracts global and local features in the frequency and spatial domains, respectively. Hence, the model considers global and local deformations. Secondly, a Multi-scale Localization Information Fusion Module (MIFM) is applied to fuse the localization information of adjacent scales to assist the current level in obtaining a more accurate deformation field. Finally, a Pyramid Self-distillation Loss (PDL) is introduced to improve the registration accuracy by treating the final deformation field as a teacher to guide the intermediate deformation field. By conducting intensive experiments on two typical 3D brain MRI datasets, we verify that the proposed NIPNet outperforms SOTA iterative-based methods and requires only a similar runtime as non-iterative approaches. Code is available at https://github.com/JXT210/NIPNet.

Smoothing of the graph convolution is not conducive to characterizing local differences of point cloud. To solve this problem, we propose a Differential Graph Convolutional Network (Differ-GCN) for point cloud analysis. First, we propose a new graph construction strategy that can make similar nodes in the local space belong to the same graph, which can better represent the local commonality. After that, the features of the graph are extracted by the similarity matrix. Some of the smoothing information of the graph is removed to optimize the over-smoothing nodes and combined with the local difference of the points to get the beneficial features for downstream tasks. Finally, each neighbor point is processed to generate a mask, and pooling is performed through the mask to reduce information loss. The experiment results show that Differ-GCN performs excellent in object classification and part segmentation. The processing speed of Differ-GCN for point cloud is much faster than the state-of-the-art methods.