Search Results (27)

Satellite segmentation is vital for spacecraft perception, supporting tasks like structural analysis, fault detection, and in-orbit servicing. However, the generalization of existing methods is severely limited by the scarcity of target satellite data and substantial morphological differences between target satellites and training samples, leading to suboptimal performance in real-world scenarios. In this work, we propose a novel diffusion-based framework for few-shot satellite segmentation, named DiffSatSeg, which leverages the powerful compositional generalization capability of diffusion models to address the challenges inherent in satellite segmentation tasks. Specifically, we propose a parameter-efficient fine-tuning strategy that fully exploits the strong prior knowledge of diffusion models while effectively accommodating the unique structural characteristics of satellites as rare targets. We further propose a segmentation mechanism based on distributional similarity, designed to overcome the limited generalization capability of conventional segmentation models when encountering novel satellite targets with substantial inter-class variations. Finally, we design a consistency learning strategy to suppress redundant texture details in diffusion features, thereby mitigating their interference in segmentation. Extensive experiments demonstrate that our method achieves state-of-the-art performance, yielding a remarkable 33.6% improvement over existing approaches even when only a single target satellite image is available. Notably, our framework also enables reference-based segmentation, which holds great potential for practical deployment and real-world applications. Full article

Google Earth Engine (GEE) has become one of the most widely used platforms for Land Use and Land Cover (LULC) research, offering cloud-based access to petabyte-scale datasets and scalable analytical tools. While earlier reviews provided valuable overviews of data and applications, this study synthesizes 72 selected articles published between 2016 and February 2025 to examine the evolution of GEE–LULC research. Results show exponential growth in publications, with Landsat and Sentinel imagery dominating datasets and Random Forest (RF) and Support Vector Machine (SVM) remaining the most common classifiers. Geographically, output is concentrated in China and India, reflecting regional leadership in GEE adoption. Despite its strengths, GEE faces persistent challenges, including memory limits, restricted support for advanced Deep Learning (DL), and reliance on labeled data. Promising directions include integrating few-shot semantic segmentation and hybrid workflows combining GEE scalability with local Graphics Processing Unit (GPU) computing. By bridging platform-focused and application-focused studies, this review provides a comprehensive synthesis of GEE–LULC research and outlines actionable pathways for advancing scalable and Artificial Intelligence (AI)-enabled geospatial analysis. Full article

Few-Shot Semantic Segmentation (FSS) faces significant challenges in modeling complex backgrounds and maintaining prediction consistency due to limited training samples. Existing methods oversimplify backgrounds as single negative classes and rely solely on pixel-level alignments. To address these issues, we propose a symmetry-aware superpixel-enhanced FSS framework with a symmetric dual-branch architecture that explicitly models the superpixel region-graph in both the support and query branches. First, top–down cross-layer fusion injects low-level edge and texture cues into high-level semantics to build a more complete representation of complex backgrounds, improving foreground–background separability and boundary quality. Second, images are partitioned into superpixels and aggregated into “superpixel tokens” to construct a Region Adjacency Graph (RAG). Support-set prototypes are used to initialize query-pixel predictions, which are then projected into the superpixel space for cross-image prototype alignment with support superpixels. We further perform message passing/energy minimization on the RAG to enhance intra-region consistency and boundary adherence, and finally back-project the predictions to the pixel space. Lastly, by aggregating homogeneous semantic information, we construct robust foreground and background prototype representations, enhancing the model’s ability to perceive both seen and novel targets. Extensive experiments on the PASCAL-$5^i$ and COCO-${20}^i$ benchmarks demonstrate that our proposed model achieves superior segmentation performance over the baseline and remains competitive with existing FSS methods. Full article

Coastal zones, as critical intersections of ecosystems, resource utilization, and socioeconomic activities, exhibit complex and diverse land cover types with frequent changes. Acquiring large-scale, high-quality annotated data in these areas is costly and time-consuming, which makes rule-based segmentation methods reliant on extensive annotations impractical. Few-shot semantic segmentation, which enables effective generalization from limited labeled samples, thus becomes essential for coastal region analysis. In this work, we propose an optimized few-shot segmentation method based on the Segment Anything Model (SAM) with a frozen-parameter segmentation backbone to improve generalization. To address the high visual similarity among coastal vegetation classes, we design a cross-matching module integrated with a hyper-correlation pyramid to enhance fine-grained visual correspondence. Additionally, a self-matching module is introduced to mitigate scale variations caused by UAV altitude changes. Furthermore, we construct a novel few-shot segmentation dataset, OUC-UAV-SEG-$2^{\mathrm{i}}$, based on the OUC-UAV-SEG dataset, to alleviate data scarcity. In quantitative experiments, the suggested approach outperforms existing models in mIoU and FB-IoU under ResNet50/101 (e.g., ResNet50’s 1-shot/5-shot mIoU rises by 4.69% and 4.50% vs. SOTA), and an ablation study shows adding CMM, SMM, and SAM boosts Mean mIoU by 4.69% over the original HSNet, significantly improving few-shot semantic segmentation performance. Full article

Generalized few-shot semantic segmentation (GFSS), which requires strong segmentation performance on novel classes while retaining the performance on base classes, is attracting increasing attention. Recent studies have demonstrated the effectiveness of applying visual prompts to solve GFSS problems, but there are still unresolved issues. Due to the confusion between the backgrounds and novel classes foreground during base class pre-training, the learned base visual prompts will mislead the novel visual prompts during novel class fine-tuning, leading to sub-optimal results. This paper proposes a background-enhanced visual prompting Transformer (Beh-VPT) to solve the problem. Specifically, we innovatively propose background visual prompts, which can learn potential novel class information in the background during base class pre-training and transfer the information to novel visual prompts during novel class fine-tuning via our proposed Hybrid Causal Attention Module. Additionally, we propose a background-enhanced segmentation head that is used in conjunction with background prompts to enhance the model’s capacity for learning novel classes. Considering the GFSS settings that take into account both base and novel classes, we introduce Singular Value Fine-Tuning in the non-meta learning paradigm to further unleash the full potential of the model. Extensive experiments show that the proposed method achieves state-of-the-art performance for GFSS on PASCAL-$5^{\mathrm{i}}$ and COCO-${20}^{\mathrm{i}}$ datasets. For example, considering both base and novel classes, the improvements in mIoU range from 0.47% to 1.08% (COCO-${20}^{\mathrm{i}}$) in the one-shot and five-shot scenarios, respectively. In addition, our method does not cause a fallback of mIoU in base classes relative to the baseline. Full article

Few-shot segmentation (FSS) aims to segment a query image with a few support images. However, there can be large differences between images from the same category, and similarities between different categories, making it a challenging task. In addition, most FSS methods use powerful encoders to extract features from the training class, which makes the model pay more attention to the features of the ‘seen’ class, and perform poorly on the segmentation task of ‘unseen’ classes. In this work, we propose a novel end-to-end model, called GFormer. GFormer has four components: encoder, prototype extractor, adversarial prototype generator, and decoder. Our encoder makes simple modifications to VIT to reduce the focus on image content, using a prototype extractor to extract prototype features from a range of support images. We further introduce different classes that are similar to the support image categories as negative examples, taking the support image categories as positive examples. We use the adversarial prototype generator to extract the adversarial prototypes from the positive and negative examples. The decoder segments the query images under the guidance of the prototypes. We conduct extensive experiments on a variety of unknown classes. The results verify the feasibility of the proposed model and prove that the proposed model has strong generalization performance for new classes. Full article

This study proposes a model for leafy vegetable disease detection and segmentation based on a few-shot learning framework and a prototype attention mechanism, with the aim of addressing the challenges of complex backgrounds and few-shot problems. Experimental results show that the proposed method performs excellently in both object detection and semantic segmentation tasks. In the object detection task, the model achieves a precision of 0.93, recall of 0.90, accuracy of 0.91, mAP@50 of 0.91, and mAP@75 of 0.90. In the semantic segmentation task, the precision is 0.95, recall is 0.92, accuracy is 0.93, mAP@50 is 0.92, and mAP@75 is 0.92. These results show that the proposed method significantly outperforms the traditional methods, such as YOLOv10 and TinySegformer, validating the advantages of the prototype attention mechanism in enhancing model robustness and fine-grained feature expression. Furthermore, the prototype loss function, which optimizes the distance relationship between samples and category prototypes, significantly improves the model’s ability to discriminate between categories. The proposed method shows great potential in agricultural disease detection, particularly in scenarios with few samples and complex backgrounds, offering broad application prospects. Full article

Few-shot semantic segmentation (FSS) models aim to segment unseen target objects in a query image with scarce annotated support samples. This challenging task requires an effective utilization of support information contained in the limited support set. However, the majority of existing FSS methods either compressed support features into several prototype vectors or constructed pixel-wise support-query correlations to guide the segmentation, which failed in effectively utilizing the support information from the global–local perspective. In this paper, we propose Global–Local Query-Support Cross-Attention (GLQSCA), where both global semantics and local details are exploited. Implemented with multi-head attention in a transformer architecture, GLQSCA treats every query pixel as a token, aggregates the segmentation label from the support mask values (weighted by the similarities with all foreground prototypes (global information)), and supports pixels (local information). Experiments show that our GLQSCA significantly surpasses state-of-the-art methods on the standard FSS benchmarks PASCAL-5ⁱ and COCO-20ⁱ. Full article

Few-Shot Semantic Segmentation (FSS) has drawn massive attention recently due to its remarkable ability to segment novel-class objects given only a handful of support samples. However, current FSS methods mainly focus on natural images and pay little attention to more practical and challenging scenarios, e.g., remote sensing image segmentation. In the field of remote sensing image analysis, the characteristics of remote sensing images, like complex backgrounds and tiny foreground objects, make novel-class segmentation challenging. To cope with these obstacles, we propose a Class-Aware Self- and Cross-Attention Network (CSCANet) for FSS in remote sensing imagery, consisting of a lightweight self-attention module and a supervised prior-guided cross-attention module. Concretely, the self-attention module abstracts robust unseen-class information from support features, while the cross-attention module generates a superior quality query attention map for directing the network to focus on novel objects. Experiments demonstrate that our CSCANet achieves outstanding performance on the standard remote sensing FSS benchmark iSAID-5ⁱ, surpassing the existing state-of-the-art FSS models across all combinations of backbone networks and K-shot settings. Full article

Nowadays, autonomous driving technology has become widely prevalent. The intelligent vehicles have been equipped with various sensors (e.g., vision sensors, LiDAR, depth cameras etc.). Among them, the vision systems with tailored semantic segmentation and perception algorithms play critical roles in scene understanding. However, the traditional supervised semantic segmentation needs a large number of pixel-level manual annotations to complete model training. Although few-shot methods reduce the annotation work to some extent, they are still labor intensive. In this paper, a self-supervised few-shot semantic segmentation method based on Multi-task Learning and Dense Attention Computation (dubbed MLDAC) is proposed. The salient part of an image is split into two parts; one of them serves as the support mask for few-shot segmentation, while cross-entropy losses are calculated between the other part and the entire region with the predicted results separately as multi-task learning so as to improve the model’s generalization ability. Swin Transformer is used as our backbone to extract feature maps at different scales. These feature maps are then input to multiple levels of dense attention computation blocks to enhance pixel-level correspondence. The final prediction results are obtained through inter-scale mixing and feature skip connection. The experimental results indicate that MLDAC obtains 55.1% and 26.8% one-shot mIoU self-supervised few-shot segmentation on the PASCAL-$5^i$ and COCO-${20}^i$ datasets, respectively. In addition, it achieves 78.1% on the FSS-1000 few-shot dataset, proving its efficacy. Full article

With the rapid advancement of modern hardware technology, breakthroughs have been made in many areas of artificial intelligence research, leading to the direction of machine replacement or assistance in various fields. However, most artificial intelligence or deep learning techniques require large amounts of training data and are typically applicable to a single task objective. Acquiring such large training datasets can be particularly challenging, especially in domains like medical imaging. In the field of image processing, few-shot image segmentation is an area of active research. Recent studies have employed deep learning and meta-learning approaches to enable models to segment objects in images with only a small amount of training data, allowing them to quickly adapt to new task objectives. This paper proposes a network architecture for meta-learning few-shot image segmentation, utilizing a meta-learning classification weight transfer network to generate masks for few-shot image segmentation. The architecture leverages pre-trained classification weight transfers to generate informative prior masks and employs pre-trained feature extraction architecture for feature extraction of query and support images. Furthermore, it utilizes a Feature Enrichment Module to adaptively propagate information from finer features to coarser features in a top-down manner for query image feature extraction. Finally, a classification module is employed for query image segmentation prediction. Experimental results demonstrate that compared to the baseline using the mean Intersection over Union (mIOU) as the evaluation metric, the accuracy increases by 1.7% in the one-shot experiment and by 2.6% in the five-shot experiment. Thus, compared to the baseline, the proposed architecture with meta-learning classification weight transfer network for mask generation exhibits superior performance in few-shot image segmentation. Full article

Few-shot segmentation (FSS) is a cutting-edge technology that can meet requirements using a small workload. With the development of China Aerospace Engineering, FSS plays a fundamental role in astronaut working scene (AWS) intelligent parsing. Although mainstream FSS methods have made considerable breakthroughs in natural data, they are not suitable for AWSs. AWSs are characterized by a similar foreground (FG) and background (BG), indistinguishable categories, and the strong influence of light, all of which place higher demands on FSS methods. We design a pixel-wise and class-wise network (PCNet) to match support and query features using pixel-wise and class-wise semantic cues. Specifically, PCNet extracts pixel-wise semantic information at each layer of the backbone using novel cross-attention. Dense prototypes are further utilized to extract class-wise semantic cues as a supplement. In addition, the deep prototype is distilled in reverse to the shallow layer to improve its quality. Furthermore, we customize a dataset for AWSs and conduct abundant experiments. The results indicate that PCNet outperforms the published best method by 4.34% and 5.15% in accuracy under one-shot and five-shot settings, respectively. Moreover, PCNet compares favorably with the traditional semantic segmentation model under the 13-shot setting. Full article

In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model’s robust capability to address the unique challenges presented by the language’s structural intricacies. Full article

With the advent of large-scale datasets, significant advancements have been made in image semantic segmentation. However, the annotation of these datasets necessitates substantial human and financial resources. Therefore, the focus of research has shifted towards few-shot semantic segmentation, which leverages a small number of labeled samples to effectively segment unknown categories. The current mainstream methods are to use the meta-learning framework to achieve model generalization, and the main challenges are as follows. (1) The trained model will be biased towards the seen class, so the model will misactivate the seen class when segmenting the unseen class, which makes it difficult to achieve the idealized class agnostic effect. (2) When the sample size is limited, there exists an intra-class gap between the provided support images and the query images, significantly impacting the model’s generalization capability. To solve the above two problems, we propose a network with prototype complementarity characteristics (PCNet). Specifically, we first generate a self-support query prototype based on the query image. Through the self-distillation, the query prototype and the support prototype perform feature complementary learning, which effectively reduces the influence of the intra-class gap on the model generalization. A standard semantic segmentation model is introduced to segment the seen classes during the training process to achieve accurate irrelevant class shielding. After that, we use the rough prediction map to extract its background prototype and shield the background in the query image by the background prototype. In this way, we obtain more accurate fine-grained segmentation results. The proposed method exhibits superiority in extensive experiments conducted on the PASCAL-$5^i$ and COCO-${20}^i$ datasets. We achieve new state-of-the-art results in the few-shot semantic segmentation task, with an mIoU of 71.27% and 51.71% in the 5-shot setting, respectively. Comprehensive ablation experiments and visualization studies show that the proposed method has a significant effect on small-sample semantic segmentation. Full article

The annotations used during the training process are crucial for the inference results of remote sensing images (RSIs) based on a deep learning framework. Unlabeled RSIs can be obtained relatively easily. However, pixel-level annotation is a process that necessitates a high level of expertise and experience. Consequently, the use of small sample training methods has attracted widespread attention as they help alleviate reliance on large amounts of high-quality labeled data and current deep learning methods. Moreover, research on small sample learning is still in its infancy owing to the unique challenges faced when completing semantic segmentation tasks with RSI. To better understand and stimulate future research that utilizes semantic segmentation tasks with small data, we summarized the supervised learning methods and challenges they face. We also reviewed the supervised approaches with data that are currently popular to help elucidate how to efficiently utilize a limited number of samples to address issues with semantic segmentation in RSI. The main methods discussed are self-supervised learning, semi-supervised learning, weakly supervised learning and few-shot methods. The solution of cross-domain challenges has also been discussed. Furthermore, multi-modal methods, prior knowledge constrained methods, and future research required to help optimize deep learning models for various downstream tasks in relation to RSI have been identified. Full article