The scarcity of high-quality annotated data in medical imaging significantly constrains the performance of deep learning-based segmentation models. While few-shot medical image segmentation (FSMIS) has emerged as a promising solution, existing methods exhibit critical limitations when handling scenarios with inter-class similarity between foreground-background regions and intra-class heterogeneity within foreground objects. Current prototype-based approaches focus primarily on the holistic extraction of the prototype from support images, failing to distinguish subtle anatomical variations and complex feature representations effectively. The AVT-ProNet features three innovative components: 1) An Adaptive Visual-Text Prototype Generation (AVPG) module leveraging CLIP’s cross-modal guide capabilities through adaptive prompting strategies; 2) a graph-based multiregion prototyping relationship optimization (GMPRO) module establishing structural relationships between decomposed subregion prototypes via graph neural networks; 3) a foreground-background prototyping contrast learning (FBPCL) strategy implementing dual-space optimization through inter-class separation and intra-class compactness. The synergistic integration of multi-modal guidance, structural relationship modeling, and contrastive prototype refinement enables our framework to overcome existing limitations in FSMIS. Comprehensive evaluations across multiple clinical scenarios (CHAOS, SABS, and CMR datasets under diverse training configurations) demonstrate superior performance over state-of-the-art approaches, including PANet, CAT-Net, DMAP, and recent PAMI baselines, Source code is available at https://github.com/394481125/AVT-ProNet.

To tackle the challenge of single-spectral object re-identification in complex and dynamic lighting scenarios, multi-spectral object re-identification, which integrates visible light and infrared information, is gradually taking the lead. Nevertheless, the significant heterogeneity across spectra causes formidable obstacles for this task. Most existing approaches alleviate inter-spectral disparities by amalgamating representations from different spectra, ignoring the selection of spectrum-specific crucial information. To address this issue, we propose a novel Representation Selective Coupling Network (RSCNet) for multi-spectral object re-identification. Specifically, we design an Attention-Fourier Token Sparsification (AFTS) module to adaptively sparse and join tokens from multi-spectral images in the attention domain and Fourier domain. This not only preserves spectrum-specific crucial information but also reduces inter-spectral gaps by selective coupling of multi-spectral representation. Meanwhile, to further align multi-spectral information and guide the model to learn more discriminative representation, we propose an Information Unification Constraint (IUC) learning strategy. Both feature-level information constraint and distribution-level information constraint are simultaneously deployed in IUC. Finally, we conduct extensive experiments on three multi-spectral object re-identification benchmarks, and the experimental results verify the effectiveness of our proposed method.

Remote sensing change detection (RSCD) holds significant research value in remote sensing (RS) image processing. In recent years, many researchers have achieved remarkable results in RSCD tasks using methods based on convolutional neural networks (CNNs) or Transformers. Considering the limited receptive field of CNN models and the high computational cost of Transformers, many researchers have combined the two approaches, yielding promising results. However, most current RSCD-based models focus solely on change and temporal information, overlooking their complementary relationship. Additionally, some multiscale feature fusion methods emphasize enhancing individual scales while neglecting the correlations between different scales. To address the above issues, we propose a multiscale complementary enhanced context fusion (MCECF) network. The network first introduces a global-local context aggregation module (GLCAM) to capture global-local context information while extracting multilevel feature maps. Subsequently, a complementary enhancement difference module (CEDM) is employed to complementarily aggregate the captured change and temporal information of bi-temporal RS image features. To fully leverage the correlations between multiscale features, a progressive decoder comprising a supervised spatial attention (SSA) mechanism and a multiscale complementary enhanced fusion module (MCEFM) was developed. Moreover, to tackle the disparity between changed and unchanged regions, a dual-branch dynamic attention fusion module (DAFM) was designed to enhance the model’s adaptability to diverse scenarios. We conducted comparative experiments on five RSCD datasets against nine state-of-the-art (SOTA) methods, and the results confirmed the effectiveness of the proposed MCECF in RSCD tasks. Our code will be made available athttps://github.com/kakuqikaduo/MCECF

Accurate segmentation of tissues and lesions is essential for diagnosis and treatment. State Space Models (SSMs) have gained attention for their linear complexity and ability to model long-range dependencies. However, the existing Mamba architecture relies on direct skip connections, which limits its ability to integrate multi-scale and multi-level features and handle boundary details effectively. To address these limitations, we propose the MSCD-VM-UNet architecture, which incorporates three novel modules: the Spatial Group Multi-Scale Attention Module (SGMAM), the Cross-Domain Feature Fusion Module (CDFFM), and the Attention-Based Feature Injection Module (ABFIM). The SGMAM captures multi-scale global and local information and adaptively adjusts feature importance to highlight key regions while suppressing noise. The CDFFM enhances boundary and detail handling by aligning semantic features from both the frequency and spatial domains. The ABFIM utilizes attention mechanisms to adaptively fuse and weigh features from different scales and semantics, promoting feature collaboration and improving the model's robustness in complex tasks. Experiments on multiple datasets show that these modules significantly enhance the accuracy of MSCD-VM-UNet, setting a new benchmark for medical image segmentation.

Researchers have been paying more attention to hyperspectral image (HSI) classification based on semi-supervised superpixel-level graph convolutional networks (SGCNs) due to their aggregation ability of rich contextual information. Although these SGCNs achieve good classification performance, the influence of the positional distributions among labeled superpixels has been overlooked. The locations of labeled superpixels, such as located at class boundaries or centers, exert a substantial influence on the final performance. To address this issue, this article proposed a novel graph neural network (GCN) method with the guidance of positional distributions of labeled superpixels, abbreviated as LPDGCN. Specifically, we first propose to utilize the sparse, low-rank as well as feature smoothness restrictions to optimize the initial superpixel graph structure because the connectivity relationships of labeled superpixels located at class boundaries or centers are easily influenced by spectral variation. Second, in order to effectively determine the positional distributions of labeled superpixels and make full use of the position relationships, we propose to utilize the information conflict from the above topology connectivity to determine the positional distributions of labeled superpixels and develop the reweighted strategy to weaken the influence of labeled superpixels located at class boundaries and strengthen the influence of that located at class centers. Finally, we evaluate the LPDGCN method on four public HSI datasets, demonstrating its superiority over other advanced classification methods in terms of three metrics, i.e., overall accuracy (OA), average accuracy (AA), and kappa coefficient (KC).

Image super-resolution (SR) stands as a pivotal process in the domains of image processing and computer vision, finding diverse applications in film, television, photography, surveillance, medical imaging, and remote sensing. In the context of remote sensing images (RSIs), the inherent challenge arises from low spatial resolution caused by factors such as sensor noise, orbit height, and weather conditions, necessitating SR reconstruction. An evident limitation of prevailing methods lies in their dependence on idealized fixed degradation models, which fail to capture the intricate degradation processes unique to remote sensing scenes. In response to these constraints, this article introduces an innovative blind image super-resolution reconstruction method tailored for remote sensing images. The proposed approach integrates convolution with a transformer and incorporates an amplitude-phase learning module (ALM) to comprehensively capture local and long-range dependencies while enhancing frequency information. The iterative optimization strategy refines texture information by carefully balancing structural and detail elements. Key contributions include a holistic approach to remote sensing image SR, ALM integration for precise feature representation, and the introduction of a patch-based frequency loss mechanism for evaluating frequency-domain features. Rigorous experiments demonstrate that compared with other state-of-the-art (SOTA) methods, the proposed algorithm delivers SR results with exceptional visual perception quality across three distinct remote sensing datasets.