Surrogate-Assisted Evolutionary Algorithms (SAEAs) are widely used for expensive Black-Box Optimization. However, their reliance on rigid, manually designed components such as infill criteria and evolutionary strategies during the search process limits their flexibility across tasks. To address these limitations, we propose Dual-Control Bi-Space Surrogate-Assisted Evolutionary Algorithm (DB-SAEA), a Meta-Black-Box Optimization (MetaBBO) framework tailored for multi-objective problems. DB-SAEA learns a meta-policy that jointly regulates candidate generation and infill criterion selection, enabling dual control. The bi-space Exploratory Landscape Analysis (ELA) module in DB-SAEA adopts an attention-based architecture to capture optimization states from both true and surrogate evaluation spaces, while ensuring scalability across problem dimensions, population sizes, and objectives. Additionally, we integrate TabPFN as the surrogate model for accurate and efficient prediction with uncertainty estimation. The framework is trained via reinforcement learning, leveraging parallel sampling and centralized training to enhance efficiency and transferability across tasks. Experimental results demonstrate that DB-SAEA not only outperforms state-of-the-art baselines across diverse benchmarks, but also exhibits strong zero-shot transfer to unseen tasks with higher-dimensional settings. This work introduces the first MetaBBO framework with dual-level control over SAEAs and a bi-space ELA that captures surrogate model information.

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%.

Abstract Channel-Spatial mixed attention methods have been proven to enhance the representation capability of convolutional neural networks. Most existing channel-spatial mixed attention methods decouple the channel domain and spatial domain, investing different orders of computational complexity in calculating attention for the two domains. However, both the channel domain and spatial domain are equally important and deserve comparable levels of complexity in attention computation. To solve this problem, we propose a novel channel-spatial mixed attention method, which is called D ual-domain B alanced C hannel- S patial M ixed A ttention (DBCSMA). The input tensor is first decomposed into channel and spatial domains using global pooling. Then, the same order of complexity is dedicated to calculating attention in both the channel domain and the spatial domain, and a mixed attention tensor is generated using multiplicative aggregation. Finally, input channel information and spatial information are simultaneously recalibrated using multiplication of this mixed attention tensor and the input tensor. Experimental results demonstrate that DBCSMA achieves superior performance compared to most existing channel-spatial mixed attention methods while using lower complexity.

Weakly-supervised point cloud semantic segmentation (WS-PCS) has attracted increasing attention due to the challenge of sparse annotations. A central problem is how to effectively extract informative features from the annotated points, enabling reliable supervision. Although many existing works extend 2D graph convolution to 3D point cloud data, 2D convolution inherently assumes feature localization, which is an assumption that does not hold in point clouds, and lacks consistent semantic offsets. To address this, we propose a novel Bilateral Graph Convolutional (BGC) method, which refines graph edges into two categories: regular edges and offset edges, providing improved guidance for WS-PCS. Firstly, we create the Local Bilateral Relations (LBR) module to learn the relational features of edges in local point cloud graphs, encompassing both regular and offset edges. To the best of our knowledge, we are the first to utilize offset edges to capture irregular semantic offsets in point cloud data. Secondly, we propose the Adaptive Pooling (AP) module, which adaptively pools edge information learned from LBR, enhancing the feature characterization ability by incorporating salient and pervasive features. Finally, we design BGC as BGC-Net and evaluate its performance against recent networks on four datasets, achieving state-of-the-art results.

Road crack detection in complex scenarios is challenged by road surface backgrounds such as water stains, oil stains, and printed road signs. To better address these issues, we propose a Multimodal Road Crack Segmentation Network–TMENet, based on complex scenes. In this article, we introduced text guidance in road crack segmentation tasks to supplement more detailed crack features. Firstly, we designed the Textual Crack Enhancer module(TCE Module) to fine-tune the Text Transformer in the Clipseg model to enhance its ability to parse specific crack descriptions. Secondly, we designed a multiscale Mamba module(Ms-Mamba Module) that integrates multiscale composite convolution and residual connections to improve the sensitivity of the model to local and multiscale details and enhance the network’s ability to recover lost features. Then, we designed a Laplacian edge detection module (LED module) that combines deep visual features and high-frequency edge information to enhance the crack edge detection capability in complex scenes through a refined feature adjustment mechanism. Finally, TMENet achieved the best results in comparative testing with seven state-of-the-art segmentation networks on the CampCrack800, AsphaltCrack300, Crackseg9K, and CRKWH100 datasets. In addition, TMENet has achieved excellent results in actual road testing, and the test results show that TMENet is an effective auxiliary method to improve the accuracy of road safety detection.

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%.

Semantic segmentation of large-scale point clouds under weak supervision is challenging due to the limited annotations. Current methods typically rely on the implicit use of annotation information to supervise the network, thereby constraining the capacity to characterize features at annotation points. In this article, we propose a query points component reasoning (QPCR) framework, which enhances the utilization of annotation information by introducing them into the middle layer, thereby refining the features used for semantic segmentation. First, we propose the semantic annotations component code reasoning (SACC Reasoning) module, which uses annotations of query points to supervise the semantic information of gradual query points. The semantic annotations component code (SACC) Reasoning module can guide the learning of feature representations of semantic annotations at multiple scales. Second, we create the semantic primitive component code reasoning (SPCC Reasoning) module, which addresses the issue of supervising the network using existing annotation point information that may suppress active features. The semantic primitive component code (SPCC) Reasoning module infers semantic primitive labels of query points using the decoding layer and uses them to guide the corresponding coding layer in learning semantic primitives. Finally, our QPCR method achieves state-of-the-art (SOTA) results on public benchmark datasets. Specifically, even with only 1% of annotated points, our QPCR method achieves the highest mIoU accuracy, i.e., 65.4% on S3DIS Area 5, 56.4% on SensatUrban and 55.3% mIoU on SemanticKITTI, respectively.

Point cloud semantic segmentation predicts the semantic class of each point, which can help AI machines perceive the real 3-D world. Recently, precision for large-scale point cloud is limited by complex scenarios, data occlusion, and massive data, which remains. This letter proposes neural radiance field convolution (NeRFConv) for large-scale point cloud semantic analysis. First, to conquer some existing methods represent the point cloud local space as the relative position without considering the rotation property of the point cloud. A new spatial direction representation called neural radiance field 7-D is constructed to denote the local point cloud space, which is unaffected by the rotation of the coordinate axes. Then, to be robust against the varying density of the point cloud, an adaptive convolution method is proposed to aggregate the local point cloud features and obtain more expressive local features. Finally, the proposed method achieves state-of-the-art performance on the test dataset. In particular, mIoU of 58.1% and 58.3% were achieved the SensatUrban and SementicKITTI datasets, respectively.