Jingcheng Yang¹, Lili Fan², Hongmei Liu^1,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4725-4740, 2025, DOI:10.32604/cmc.2025.062963 - 19 May 2025

Abstract With the advancement of deep learning in the automotive domain, more and more researchers are focusing on autonomous driving. Among these tasks, free space detection is particularly crucial. Currently, many model-based approaches have achieved autonomous driving on well-structured urban roads, but these efforts primarily focus on urban road environments. In contrast, there are fewer deep learning methods specifically designed for off-road traversable area detection, and their effectiveness is not yet satisfactory. This is because detecting traversable areas in complex outdoor environments poses significant challenges, and current methods often rely on single-image inputs, which do not… More >

Yiming Tang^*, Yanqiu Chen

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1707-1726, 2025, DOI:10.32604/cmc.2025.062412 - 16 April 2025

Abstract Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision. Few-shot segmentation methods aim to address this problem by recognizing objects from specific target classes with a few provided examples. Previous approaches for few-shot semantic segmentation typically represent target classes using class prototypes. These prototypes are matched with the features of the query set to get segmentation results. However, class prototypes are usually obtained by applying global average pooling on masked support images. Global pooling discards much structural information, which may reduce the accuracy of model predictions. To… More >

Kaiyang Han¹, Xiaoqiang Jia¹, Yangfei Lin², Tsutomu Yoshinaga², Yalong Li², Jiale Wu^2,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2971-2988, 2025, DOI:10.32604/cmc.2025.061563 - 16 April 2025

Abstract With the rapid development of artificial intelligence and the Internet of Things, along with the growing demand for privacy-preserving transmission, the need for efficient and secure communication systems has become increasingly urgent. Traditional communication methods transmit data at the bit level without considering its semantic significance, leading to redundant transmission overhead and reduced efficiency. Semantic communication addresses this issue by extracting and transmitting only the most meaningful semantic information, thereby improving bandwidth efficiency. However, despite reducing the volume of data, it remains vulnerable to privacy risks, as semantic features may still expose sensitive information. To… More >

Min Yao^1,*, Guangjie Hu¹, Yaozu Zhang²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2751-2771, 2025, DOI:10.32604/cmc.2025.060860 - 16 April 2025

Abstract Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing. Despite the success of Convolutional Neural Networks (CNNs), they often fail to capture inter-layer feature relationships and fully leverage contextual information, leading to the loss of important details. Additionally, due to significant intra-class variation and small inter-class differences in remote sensing images, CNNs may experience class confusion. To address these issues, we propose a novel Category-Guided Feature Collaborative Learning Network (CG-FCLNet), which enables fine-grained feature extraction and adaptive fusion. Specifically, we design a Feature Collaborative Learning Module (FCLM)… More >

Xiaoyan Shao¹, Jiaqi Han^1,*, Lingling Li^1,*, Xuezhuan Zhao^1,2,3,4, Jingjing Yan¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 595-617, 2025, DOI:10.32604/cmc.2025.060295 - 26 March 2025

Abstract The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods. End-to-end model designs have gained significant attention for improving training efficiency. Most current algorithms rely on Convolutional Neural Networks (CNNs) for feature extraction. Although CNNs are proficient at capturing local features, they often struggle with global context, leading to incomplete and false Class Activation Mapping (CAM). To address these limitations, this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation (CPEWS) model, which improves feature extraction by utilizing the Vision Transformer… More >

Shijie Xiang, Dong Zhou, Dan Tian^*, Zihao Wang

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 497-515, 2025, DOI:10.32604/cmc.2025.060244 - 26 March 2025

Abstract Real-time semantic segmentation tasks place stringent demands on network inference speed, often requiring a reduction in network depth to decrease computational load. However, shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy. Therefore, balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation. To address these challenges, this paper proposes a lightweight bilateral dual-residual network. By introducing a novel residual structure combined with feature extraction and fusion modules, the proposed network significantly enhances representational capacity while reducing computational costs. Specifically, an improved compound… More >

Woongkyu Park¹, Yeongyu Choi², Mahammad Shareef Mekala³, Gyu Sang Choi¹, Kook-Yeol Yoo¹, Ho-youl Jung^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3965-3981, 2025, DOI:10.32604/cmc.2025.059966 - 06 March 2025

Abstract Designing fast and accurate neural networks is becoming essential in various vision tasks. Recently, the use of attention mechanisms has increased, aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input. In this paper, we concentrate on squeeze-and-excitation (SE)-based channel attention, considering the trade-off between latency and accuracy. We propose a variation of the SE module, called squeeze-and-excitation with layer normalization (SELN), in which layer normalization (LN) replaces the sigmoid activation function. This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention. In… More >

Jingjing Yan¹, Xuyang Zhuang^2,*, Xuezhuan Zhao^1,2, Xiaoyan Shao^1,*, Jiaqi Han¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5363-5386, 2025, DOI:10.32604/cmc.2025.059709 - 06 March 2025

Abstract The key to the success of few-shot semantic segmentation (FSS) depends on the efficient use of limited annotated support set to accurately segment novel classes in the query set. Due to the few samples in the support set, FSS faces challenges such as intra-class differences, background (BG) mismatches between query and support sets, and ambiguous segmentation between the foreground (FG) and BG in the query set. To address these issues, The paper propose a multi-module network called CAMSNet, which includes four modules: the General Information Module (GIM), the Class Activation Map Aggregation (CAMA) module, the… More >

Xueli Shen, Meng Wang^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4631-4647, 2025, DOI:10.32604/cmc.2025.059149 - 06 March 2025

Abstract In the field of Weakly Supervised Semantic Segmentation (WSSS), methods based on image-level annotation face challenges in accurately capturing objects of varying sizes, lacking sensitivity to image details, and having high computational costs. To address these issues, we improve the dual-branch architecture of the Conformer as the fundamental network for generating class activation graphs, proposing a multi-scale efficient weakly-supervised semantic segmentation method based on the improved Conformer. In the Convolution Neural Network (CNN) branch, a cross-scale feature integration convolution module is designed, incorporating multi-receptive field convolution layers to enhance the model’s ability to capture long-range… More >

Thai-Viet Dang^1,*, Nhu-Nghia Bui¹, Phan Xuan Tan^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2001-2026, 2025, DOI:10.32604/cmc.2025.060605 - 17 February 2025

Abstract Due to the necessity for lightweight and efficient network models, deploying semantic segmentation models on mobile robots (MRs) is a formidable task. The fundamental limitation of the problem lies in the training performance, the ability to effectively exploit the dataset, and the ability to adapt to complex environments when deploying the model. By utilizing the knowledge distillation techniques, the article strives to overcome the above challenges with the inheritance of the advantages of both the teacher model and the student model. More precisely, the ResNet152-PSP-Net model’s characteristics are utilized to train the ResNet18-PSP-Net model. Pyramid… More >