Representing valuable semantics in irregular 3-D point clouds with spatial-variant relationships is challenging. Current shared/weighted point convolution methods have limited efficiency and struggle to represent distinctive weight matrices precisely. In thisarticle, we present guided convolution (GuidedConv) operation, a plug-and-play point convolution operator for 3-D point cloud analysis. Compared to existing point convolution methods, GuidedConv implements a guided-share method for the weight matrices, which dynamically assigns corresponding weight matrices to points depending on their local spatial relationships without increasing the kernels in a brute-force way or equipping sophisticated auxiliary networks. The key of GuidedConv is to dynamically assigns weight matrices generated by kernel base to the points according to a guided mask, which is learned adaptively from the spatial relationships of 3-D point clouds in a data-driven manner. Guided masks establish a one-of-a-kind correspondence between the weight matrices and the points. In this way, GuidedConv possesses powerful capabilities to characterize different semantics and is more efficient and precise than current point convolution operators. Extensive qualitative and quantitative evaluations clearly show integrating GuidedConv into several relatively typical networks without modifying configurations to achieve on-par or better performances than state-of-the-art point cloud classification and segmentation methods on several benchmark datasets. Thorough ablation studies and visualizations demonstrate the flexibility and effectiveness of GuidedConv.

Weakly supervised video anomaly detection (WSVAD) aims to retrieve temporal intervals containing anomalous events within untrimmed videos, leveraging video-level annotations. The existing methodologies exhibit unsatisfactory performance, primarily attributed to the absence of frame-level annotations. To alleviate this issue, we argue that representations exhibiting high similarity within local regions can offer dependable semantic knowledge. With this insight, we introduce a novel pseudo-label optimization mechanism grounded in the similarity of local features, specifically designed to direct framelevel anomaly predictions. Besides, we have noticed that the majority of videos utilized for anomaly detection originate from surveillance scenarios and are captured by stationary cameras. Consequently, the frames within a given video share consistent background properties. Motivated by this observation, we propose an intra-frame background-foreground separation strategy for each frame to extract discriminative visual representations. Given the considerable similarity in the backgrounds of most frames, the distinctions among the features of diverse frames become subtly indistinct. As a result, to ensure the rationality of predictions, we encourage maximizing the inter-class variance between normal and abnormal frames. Extensive experiments and ablation studies, encompassing both coarse-grained and fine-grained, have been conducted on XD-Violence, UCF-Crime, and ShanghaiTech benchmark datasets. The results demonstrate that our proposed method achieves substantial improvements, outperforming the current state-of-the-art approaches.

The evolution towards 6G is set to transform Vehicle-to-Everything (V2X) networks by introducing advanced technologies such as Six-Dimensional Movable Antenna (6DMA). This technology endows Roadside Units (RSUs) with dynamic beam-steering capabilities, enabling adaptive coverage. However, traditional RSU deployment strategies, optimized for static coverage, are fundamentally mismatched with these new dynamic capabilities, leading to a critical deployment–optimization mismatch. This paper addresses this challenge by proposing DyDO, a novel Dynamic Deployment and Optimization framework for the utilization of 6DMA-RSUs. Our framework strategically decouples the problem into two modules operating on distinct timescales. On a slow timescale, an offline deployment module analyzes long-term historical traffic data to identify optimal RSU locations. This is guided by a newly proposed metric, the Dynamic Potential Score (DPS), which quantifies a location’s intrinsic value for dynamic adaptation by integrating spatial concentration, temporal volatility, and traffic magnitude. On a fast timescale, an online control module employs an efficient Sequential Angular Search (SAS) algorithm to perform real-time, adaptive beam steering based on immediate traffic patterns. Extensive experiments on a large-scale, real-world trajectory dataset demonstrate that DyDO outperforms conventional static deployment methodologies. This work highlights the necessity of dynamic-aware deployment to fully unlock the potential of 6DMA in future V2X systems.

Recently, significant advancements have been made in 3D point cloud analysis by leveraging transformer architecture in 3D space. However, it remains challenging to effectively implement local and global learning within irregular and sparse structures of 3D point clouds. This paper presents the Adaptive Interaction Transformer (AIFormer), a novel hierarchical transformer architecture designed to enhance 3D point cloud analysis by fusing local and global features through the adaptive interaction of features. Specifically, AIFormer mainly consists of several stacked AIFormer Blocks. Each AIFormer module employs the Local Relation Aggregation Module and the Global Context Aggregation Module, respectively, to extract local details of relationships within the reference point and long-range dependencies between reference points. Then, the local and global features are fused using the Adaptive Interaction Module for adaptive interaction to optimize the point representation. Additionally, the AIFormer Block further designs geometric relation functions and contextual relative semantic encoding to enhance local and global feature extraction capabilities, respectively. Extensive experiments on three popular 3D point cloud datasets verify that AIFormer achieves state-of-the-art or comparable performances. Our comprehensive ablation study further validates the effectiveness and soundness of the AIFormer design.