In remotely sensed images, high intraclass variance and interclass similarity are ubiquitous due to complex scenes objects with multivariate features, making semantic segmentation a challenging task. Deep convolutional neural networks can solve this problem by modeling the context of features improving their discriminability. However, current learning paradigms model feature affinity in spatial dimension channel separately then fuse them sequential or parallel manner, leading suboptimal...

Robot-assisted rehabilitation is a growing field that can provide an intensity, quality, and quantity of treatment exceed therapist-mediated rehabilitation. Several control algorithms have been implemented in robots to develop patient-cooperative strategy with the capacity understand intention user suitable training. In this paper, we present upper-limb motion pattern recognition method using surface electromyography (sEMG) signals support vector machine (SVM) robot, ReRobot, which was built...

Since DCNNs (deep convolutional neural networks) have been successfully applied to various academic and industrial fields, semantic segmentation methods, based on DCNNs, are increasingly explored for remote-sensing image interpreting information extracting. It is still highly challenging due the presence of irregular target shapes, similarities inter – intra-class objects in large-scale high-resolution satellite images. A majority existing methods fuse multi-scale features that always fail...

High spatial resolution remote sensing images (HRRSIs) contain intricate details and varied spectral distributions, making their semantic segmentation a challenging task. To address this problem, it is crucial to adequately capture both local global contexts reduce ambiguity. While self-attention modules in vision transformers long-range context, they tend sacrifice details. In article, we propose geometric prior-guided interactive network (GPINet), hybrid that refines features across...

The detection of underground personnel is one the key technologies in computer vision. However, this technique susceptible to complex environments, resulting low accuracy and slow speed. To accurately detect coal mine operators we combine image features with K-means++ clustering anchor frames propose a new Re-parameterization YOLO (Rep-YOLO) algorithm. First, Criss-Cross-Vertical Channel Attention (CVCA) mechanism introduced at end network capture Long-Range Dependencies (LRDs) image. This...

Change detection is crucial for evaluating land use, cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity image content, poses challenges traditional change algorithms terms accuracy applicability. recent emergence deep learning methods has led to substantial progress field detection. However, existing frameworks often involve simplistic...

The rapid advancements in remote sensing technology have enabled the widespread availability of fine-resolution images (RSIs), offering rich spatial details and semantics. Despite applicability scalability transformers semantic segmentation RSIs by learning pairwise contextual affinity, they inevitably introduce irrelevant context, hindering accurate inference patch To address this, we propose a novel multi-head attention-attended module (AAM) that refines self-attention mechanism. AAM...

Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spatial domain, which frequently leads to less than optimal discrimination features. Considering inherent spectral qualities RSIs, it essential bolster these representations by incorporating context conjunction with information improve discriminative...

Nowadays, the tidal waves of deep convolution have promoted proliferation learning change detection (CD) methods. However, challenges still remain as most algorithms tend to poor detections small targets, unsmooth edges, and incomplete internal regions, largely because a lack effective features, context information, feature fusion. In this paper, multi-attention feature-constrained pixel-shuffle image fusion network (MapsNet) is proposed address in CD tasks. We first employ two-stream fully...

Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing images (RSIs). However, conventional methods predominantly focus on learning representations within spatial domain, often resulting suboptimal discriminative capabilities. Given intrinsic spectral characteristics RSIs, it becomes imperative to enhance potential these by integrating context alongside information. In this paper, we introduce spectrum-space collaborative network (SSCNet), which is...

Semantic segmentation of Remote Sensing Images (RSIs) entails assigning semantic labels to each pixel accurately. RSIs are rich in spatial and spectral data, revealing diverse material object characteristics. Yet, current RSI-focused computer vision models struggle with significant intra-class variation inter-class resemblance due limited data usage. We propose the Frequency Domain Feature-Guided Network (FFGNet) for RSI segmentation, influenced by digital signal processing theories. FFGNet...

Contextual information plays a pivotal role in the semantic segmentation of remote sensing imagery (RSI) due to imbalanced distributions and ubiquitous intra-class variants. The emergence transformer intrigues revolution vision tasks with its impressive scalability establishing long-range dependencies. However, local patterns, such as inherent structures spatial details, are broken tokenization transformer. Therefore, ICTNet is devised confront deficiencies mentioned above. Principally,...

Abstract Microbial infections continually present a major worldwide public healthcare threat, particularly in instances of impaired wound healing and biomedical implant fouling. The development new materials with the desired antimicrobial property to avoid treat infection is urgently needed care management. This study reports novel dual‐functional biodegradable dextran‐poly(ethylene glycol) (PEG) hydrogel covalently conjugated antibacterial Polymyxin B Vancomycin (Vanco). designed as...

Semantic segmentation of high-resolution remote sensing images (HRRSIs) presents unique challenges due to the intricate spatial and spectral characteristics these images. Traditional methods often prioritize information while underutilizing rich context, leading limited feature discrimination capabilities. To address issues, we propose a novel frequency attention-enhanced network (FAENet), which incorporates attention model (FreqA) jointly contexts. FreqA leverages discrete wavelet...

Large vision language models (LVLMs) are built upon large (LLMs) and incorporate non-textual modalities; they can perform various multimodal tasks. Applying LVLMs in remote sensing (RS) visual question answering (VQA) tasks take advantage of the powerful capabilities to promote development VQA RS. However, due greater complexity images compared natural images, general-domain tend poorly RS scenarios prone hallucination phenomena. Multi-agent debate for collaborative reasoning is commonly...

Polyp segmentation is crucial for early colorectal cancer detection, but accurately delineating polyps challenging due to their variations in size, shape, and texture low contrast with surrounding tissues. Existing methods often rely solely on spatial-domain processing, which struggles separate high-frequency features (edges, textures) from low-frequency ones (global structures), leading suboptimal performance. We propose the Dynamic Frequency-Decoupled Refinement Network (DFDRNet), a novel...