Gait recognition aims to identify individuals based on walking patterns in a long-range, contactless manner. While camera-based methods have advanced significantly, their performance deteriorates under poor lighting conditions. LiDAR offers a promising alternative by capturing accurate 3D gait information regardless of illumination. However, effectively integrating heterogeneous data from diverse sensors, such as LiDAR and cameras, remains a key challenge for cross-modal gait recognition. Existing approaches often minimize modality discrepancy directly, which can lead to class collapse and damage to inter-class discriminability. To overcome these limitations, we propose a Semantic-Guided Cross-modal Gait recognition framework, SG-CrossGait, that introduces text features as the prototype space to bridge camera and LiDAR modalities. We design structured Gait Description Factors (GDF) and leverage multimodal large language models (MLLMs) for automatic factor annotation and text generation, enriching existing datasets with textual descriptions, yielding SUSTech1K-Text and FreeGait-Text. A CLIP-based pipeline aligns multi-grained representations from both modalities to the text prototype space. We further propose the Dual-stream Cross-attention Fusion (DCF) module for fine-grained feature integration and the Semantic-Guided Feature Decoupling (SGFD) module to disentangle shared and modality-specific features. A Multi-task Training (MT) scheme incorporating Gait Attribute Recognition (GAR) further enhances intra-class compactness. Extensive experiments validate the effectiveness of our approach. On SUSTech1K-Text, our method achieves 61% accuracy in LiDAR-to-Camera recognition, outperforming the state-of-the-art method by 8.3%. We also release the Gait-Text benchmark to promote future research at the intersection of gait analysis and vision-language learning. Code and datasets are available at: https://github.com/O-VIGIA/SCCG.git.

3D synthetic-to-real unsupervised domain adaptive seg-mentation is crucial to annotating new domains. Self-training is a competitive approach for this task, but its performance is limited by different sensor sampling patterns (i.e., variations in point density) and incomplete training strate-gies. In this work, we propose a density-guided translator (DGT), which translates point density between domains, and integrates it into a two-stage self-training pipeline named DGT-ST. First, in contrast to existing works that simulta-neously conduct data generation and feature/output align-ment within unstable adversarial training, we employ the non-learnable DGT to bridge the domain gap at the in-put level. Second, to provide a well-initialized model for self-training, we propose a category-level adversarial net-work in stage one that utilizes the prototype to prevent neg-ative transfer. Finally, by leveraging the designs above, a domain-mixed self-training method with source-aware consistency loss is proposed in stage two to narrow the domain gap further. Experiments on two synthetic-to-real segmentation tasks (SynLiDAR → semanticKITTI and SynL- iDAR → semanticPOSS) demonstrate that DGT-ST outper-forms state-of-the-art methods, achieving 9.4% and 4.3% mIoU improvements, respectively. Code is available at https://github.com/yuan-zm/DGT-ST.

Compared to fully supervised 3D large-scale point cloud segmentation methods, which necessitate extensive manual point-wise annotations, weakly supervised segmentation has emerged as a popular approach for significantly reducing labeling costs while maintaining effectiveness. However, the existing methods have exhibited inferior segmentation performance and unsatisfactory generalization capabilities in some scenarios with unique structures (e.g., building facades). In this paper, we propose an effective and generalized weakly supervised semantic segmentation framework, called multi-stage scene-level constraints (MSC), to solve the above problem. To address the issue regarding inadequate labeled data, we use pseudo-labels for unlabeled data and propose an uncertainty-guided adaptive reweighting strategy to reduce the negative impact of erroneous pseudo-labeled data on the model learning process. To address the class imbalance issue, we employ multi-stage scene-level constraints (i.e., encoder, decoder, and classifier stages) to treat each class equally and improve perception ability of the model for each class. Evaluations conducted on multiple large-scale point cloud datasets collected in different scenarios, including building facades, indoor scenes, outdoor scenes, and UAV scenes, show that our MSC achieves a large gain over the existing weakly supervised methods and even surpasses some fully supervised methods.

Semantic segmentation of building facade point clouds has diverse applications. The development of semantic segmentation methods is inextricably linked to datasets. The available building facade datasets suffer from a lack of abundant semantic categories and data completeness. To compensate for these shortcomings, we propose a new building facade dataset characterized by various categories and relatively complete 3-D building facades. In addition, most existing methods focus on fully supervised learning, which relies on manually labeling large-scale point cloud data and results in high time and labor costs. In this article, we propose an effective weakly supervised building facade segmentation approach, called spatial adaptive fusion consistency contrastive constraint (SAF-C3), to solve the above problem. We first design a multirandom point cloud augmentor as an auxiliary supervision branch to enhance the learning ability of the original network branch. Then, we present a spatial adaptive fusion (SAF) module to extract discriminative features for building facade point clouds. Finally, we propose a spatial consistency contrastive constraint to explore the contrastive property in feature space and to ensure the predictive consistency among the augmentation and original branches. The proposed method achieves a significant performance improvement against the state-of-the-art methods on two building facade point cloud datasets through extensive experiments. In particular, the performance of SAF-C3 with 1% labels significantly surpasses the baseline network with 100% labels.

The networks are required to be capable of learning low-level features well when applied to remote sensing image semantic segmentation task. To capture accurate and abundant low-level semantic information, the early feature extractor layer is crucial to the whole network, because all the subsequent features are inferred from that base. To address the low-level feature extraction issue and overcome the shortcomings of traditional convolution like too many parameters or limited receptive field, some novel convolution units have been proposed in the literature. In this paper, we propose two elaborately designed and portable yet effective convolution units, i.e., directional convolution (DC) and large field convolution (LFC), combined as the extractor of low-level semantic features. DC is designed to extract directional features from specific directions, and LFC can achieve a large receptive field with few parameters. Experiment results on two public datasets provide evidence that our convolution units can help deep learning networks improve performance stably and comprehensively compared to the baseline networks.

Unsupervised domain adaptation (UDA) segmentation aims to leverage labeled source data to make accurate predictions on unlabeled target data. The key is to make the segmentation network learn domain-invariant representations. In this work, we propose a prototype-guided multitask adversarial network (PMAN) to achieve this. First, we propose an intensity-aware segmentation network (IAS-Net) that leverages the private intensity information of target data to substantially facilitate feature learning of the target domain. Second, the category-level cross-domain feature alignment strategy is introduced to flee the side effects of global feature alignment. It employs the prototype (class centroid) and includes two essential operations: 1) build an auxiliary nonparametric classifier to evaluate the semantic alignment degree of each point based on the prediction consistency between the main and auxiliary classifiers and 2) introduce two class-conditional point-to-prototype learning objectives for better alignment. One is to explicitly perform category-level feature alignment in a progressive manner, and the other aims to shape the source feature representation to be discriminative. Extensive experiments reveal that our PMAN outperforms state-of-the-art results on two benchmark datasets.

Unsupervised domain adaptation commonly uses cycle generative networks to produce synthesis data from source to target domains. Unfortunately, translated samples cannot effectively preserve semantic information from input sources, resulting in bad or low adaptability of the network to segment target data. This work proposes an advantageous domain translation mechanism to improve the perceptual ability of the network for accurate unlabeled target data segmentation. Our domain translation employs patchwise contrastive learning to improve the semantic content consistency between input and translated images. Our approach was applied to unsupervised domain adaptation based abdominal organ segmentation. The experimental results demonstrate the effectiveness of our framework that outperforms other methods.