Current semantic segmentation approaches for point cloud scenes heavily rely on manual labeling, while research on unsupervised semantic segmentation methods specifically for raw point clouds is still in its early stages. Unsupervised point cloud learning poses significant challenges due to the absence of annotation information and the lack of pre-training. The development of effective strategies is crucial in this context. In this paper, we propose a novel prototype library-driven unsupervised point cloud semantic segmentation strategy that utilizes Structure Learning and Consistent Reasoning (P-SLCR). First, we propose a Consistent Structure Learning to establish structural feature learning between consistent points and the library of consistent prototypes by selecting high-quality features. Second, we propose a Semantic Relation Consistent Reasoning that constructs a prototype inter-relation matrix between consistent and ambiguous prototype libraries separately. This process ensures the preservation of semantic consistency by imposing constraints on consistent and ambiguous prototype libraries through the prototype inter-relation matrix. Finally, our method was extensively evaluated on the S3DIS, SemanticKITTI, and Scannet datasets, achieving the best performance compared to unsupervised methods. Specifically, the mIoU of 47.1% is achieved for Area-5 of the S3DIS dataset, surpassing the classical fully supervised method PointNet by 2.5%.

Magnetic Resonance Imaging (MRI) is widely used in medical diagnosis due to its excellent ability to image soft tissues. However, its scanning process suffers from issues such as long imaging times and slow reconstruction speeds, which limit its clinical efficiency. To address these limitations, we propose a novel accelerated MRI reconstruction method, termed CRAVS-Net. This method combines complex residual modeling with multihead attention mechanisms to effectively integrate amplitude and phase information from MRI images. Additionally, a multistage residual structure and a channel attention fusion module are introduced to enhance the modeling depth of multi-channel features and the utilization of cross-scale contextual information. Experiments on the NYU knee dataset demonstrate that CRAVSNet outperforms existing state-of-the-art approaches in terms of SSIM and PSNR, and exhibits robust reconstruction performance and strong generalization ability.

With the continuous development of intelligent autonomous aerial vehicles (AAV) technology, efficient and accurate sensing of surrounding objects through onboard sensors has become an important research direction. Among these, the object detection is one of the most common perception techniques, but generic object detection methods have low-detection performance in remote sensing images. To address this, we propose the weighted learnable recursive aggregation detection framework, which aims to improve the detection performance in AAV remote sensing images. The network maintains efficiency while ensuring the accuracy of detection. First, to improve the fusion capability of multiscale characterization of small objects, we design a multichannel weights learnable recursive aggregation network. The network improves the multiscale representation fusion capability by dynamically fusing different layers of scale features while recursively aggregating different layers of features. In addition, we design a multichannel recursive residual fusion mechanism for this framework, which is capable of extracting object feature information at different spatial scales and enhances the ability of multiscale characterization of the object. Then, we migrate the multichannel weights learnable recursive aggregation network to different detection frameworks to verify its generalization. Finally, we perform experimental validation using VisDrone 2019, AI-TOD dataset and comparing it with existing methods. The proposed model improves the [email protected] value by 2.1% over the benchmark model on the VisDrone 2019 dataset. On the AI-TOD dataset, our proposed algorithm improves the [email protected] by 1.7% compared with the benchmark model. Also working on the LLVIP dataset, our algorithm achieves 89.8%.