In the ecosystem, birds are important indicators that can sensitively reflect changes in the ecological environment and its health. However, bird monitoring has challenges due to species diversity, variable behaviors, and distinct morphological chara...
Deep learning methods that integrate convolutional neural networks (CNNs) and Transformers have achieved remarkable progress in hyperspectral image (HSI) classification. However, existing methods still suffer from insufficient multi-scale spatial&nda...
Pig detection in intelligent livestock farming is challenging due to the difficulty of jointly capturing global context and multi-scale features in complex environments. To address this, we propose MSCA-RTDETR (Multi-scale Content-Aware Real-Time Det...
An unescapable trend of hyperspectral image (HSI) has been toward classification with high accuracy and splendid performance. In recent years, Transformers have made remarkable progress in the HSI classification task. However, Transformer-based metho...
Axle-box bearings are safety-critical components in high-speed electric multiple units, and their vibration responses are affected by wheel–rail excitation, load variation, and environmental noise. Existing deep-learning diagnosis methods often...
Spatial transcriptomics links gene expression to tissue morphology but is too costly for routine clinical use, motivating prediction of expression from hematoxylin-and-eosin (H&E) images. FViT (Foundation-tokenized Vision Transformer) tokenizes a...
Existing optical remote sensing image change detection (CD) methods aim to learn an appropriate discriminate decision by analyzing the feature information of bitemporal images obtained at the same place. However, the complex scenes in high-resolution...
Vision transformers (ViTs) are increasingly utilized for HSI classification due to their outstanding performance. However, ViTs encounter challenges in capturing global dependencies among objects of varying sizes, and fail to effectively exploit the...
Spiking Neural Networks (SNNs) have drawn increasing attention for their event-driven and energy-efficient characteristics. However, achieving accurate and efficient inference within limited time-steps remains a major challenge. This paper proposes a...
Natural language is a complex system that exhibits robust statistical regularities. Here, we represent text as a trajectory in a high-dimensional embedding space generated by transformer-based language models, and quantify scale-dependent fluctuation...
Transformer-based methods have achieved remarkable performance in hyperspectral image (HSI) classification. However, their ability to learn discriminative multi-scale spatial–spectral features remains limited by the single-granularity token-to-...
Nowadays, the field of video-based action recognition is rapidly developing. Although Vision Transformers (ViT) have made great progress in static image processing, they are not yet fully optimized for dynamic video applications. Convolutional Neural...
Fracture networks control fluid flow, reservoir connectivity, and production performance in carbonate systems, yet their multiscale hierarchical organization remains difficult to characterize from heterogeneous geological and geophysical datasets. He...
Bacterial wound infection poses a major challenge in trauma care and can lead to severe complications such as sepsis and organ failure. Therefore, rapid and accurate identification of the pathogen, along with targeted intervention, is of vital import...
With rapid advancements in transformers, the reconstruction of hyperspectral images from RGB images, also known as spectral super-resolution (SSR), has made significant breakthroughs. However, existing transformer-based methods often struggle to bala...
The distinctive feature of hyperspectral images (HSIs) is their large number of spectral bands, which allows us to identify categories of ground objects by capturing discrepancies in spectral information. Convolutional neural networks (CNN) with atte...
Long-form podcast interviews require methods that distinguish episodes, speaker streams, windows, and chronological turns. This study presents a multiscale computational framework for 53 episodes of the Chinese podcast Yan Zhong Hua Shu (11,590 turns...
The fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data for classification has received widespread attention and has led to significant progress in research and remote sensing applications. However, existing common CNN...
To address the challenges of multi-scale defect perception and complex background interference in railway insulator detection, this paper proposes LSD-YOLO, a lightweight multi-scale fusion network based on an improved YOLO11n. The model integrates t...
Vision-and-language navigation (VLN) traditionally relies on explicit reasoning chains, which, despite being interpretable, impose severe constraints on inference efficiency and scalability in long-range environments. Existing multimodal large langua...
Electroencephalogram (EEG) is the most preferred and credible source for emotion recognition, where long-short range features and a multichannel relationship are crucial for performance because numerous physiological components function at various ti...
Reductionism and splitting application domain into disciplines and identify the smallest required model-granules, termed ”basic entity” combined with systematic construction of the behaviour equations for the basic entities, yields a systematic appro...
High-resolution image segmentation for landscape applications has garnered significant attention, particularly in the context of ultra-high-resolution (UHR) imagery. Current segmentation methodologies partition UHR images into standard patches for mu...
Sarcasm represents a language form where a discrepancy lies between the literal meanings and implied intention. Sarcasm detection is challenging with unimodal text without clearly understanding the context, based on which multimodal information is in...
Recently, Vision Transformers (ViTs) have been actively applied to fine-grained visual recognition (FGVR). ViT can effectively model the interdependencies between patch-divided object regions through an inherent self-attention mechanism. In addition,...
Semantic segmentation of ultra-high-resolution remote sensing (UHR-RS) imagery plays a critical role in land use and land cover analysis, yet it remains computationally intensive due to the enormous input size and high spatial complexity. Existing st...
Background: Automatic sleep staging is fundamental to sleep architecture analysis and the diagnosis of sleep disorders. Sleep staging requires jointly modeling complementary information across modalities, multi-scale physiological features, and tempo...
Deep learning has dramatically enhanced remote sensing change detection. However, existing neural network models often face challenges like false positives and missed detections due to factors like lighting changes, scale differences, and noise inter...
In recent years, the convenience and potential for information extraction offered by Remote Sensing Image–Text Retrieval (RSITR) have made it a significant focus of research in remote sensing (RS) knowledge services. Current mainstream methods...
Cross-scene classification of hyperspectral images attracts extensive research attention due to the prominent distribution discrepancies existing in hyperspectral images across different scenes. Most existing methods mitigate domain shift by expandin...
Hyperspectral and multispectral image fusion requires enhancing spatial details while preserving the pixel-wise spectral fidelity of hyperspectral observations. Transformer-based approaches provide effective contextual modeling, yet hierarchical toke...
Buildings are one of the most important goals of human transformation of the Earth’s surface. Therefore, building extraction (BE), such as in urban resource management and planning, is a task that is meaningful to actual production and life. Co...
In recent years, with the extensive application of deep learning in images, the task of remote sensing image change detection has witnessed a significant improvement. Several excellent methods based on Convolutional Neural Networks and emerging trans...
The precise mapping of rock joint traces is fundamental to the design and safety assessment of foundations, retaining structures, and underground cavities in building and civil engineering. Existing deep learning approaches either impose prohibitive...
Ship detection in optical images is critical for maritime supervision but faces challenges from scale variations and complex backgrounds. Existing detectors often struggle to balance global context modeling with computational efficiency. To address t...
Recently, with the remarkable advancements of deep learning in the field of image processing, convolutional neural networks (CNNs) have garnered widespread attention from researchers in the domain of hyperspectral image (HSI) classification. Moreover...
Accurate multivariate time series forecasting remains a major challenge in various real-world applications, primarily due to the limitations of existing models in capturing multiscale temporal dependencies and effectively integrating external data. T...
Steel plate defect detection is confronted with problems such as weak features of small defects, disconnection between physical priors and detection tasks, and semantic inconsistency of multi-scale fusion, which can easily lead to the misdetection of...
Crowd counting estimates the number and spatial distribution of people in images and videos, supporting smart city management and public safety. Existing methods often rely on intra-level feature refinement and simple cross-scale fusion, such as conc...
Large volumes of images shared on social media have made image captioning an important tool for social hotspot identification and public opinion monitoring, where accurate visual–language alignment is essential for reliable analysis. However, e...
Transformers have recently been applied to change detection (CD) of multitemporal remote sensing images because of their ability to model global information. However, the rigid patch partitioning in vanilla self-attention destroys spatial structures...
The Unmanned Aerial Vehicle (UAV) tracking tasks find extensive utility across various applications. However, current Transformer-based trackers are generally tailored for diverse scenarios and lack specific designs for UAV applications. Moreover, du...
Generative recommendation systems based on Large Language Models leverage their reasoning capabilities to capture users’ latent interests. However, aligning continuous user behavioral embeddings with the discrete semantic space of LLMs remains...
Accurate prediction of submitochondrial localization is fundamental to understanding mitochondrial biogenesis and cellular metabolic pathways. While deep representations from pre-trained protein language models (pLMs) have significantly advanced the...
Geographic Named Entity Recognition (Geo-NER) is a crucial task for extracting geography-related entities from unstructured text, and it plays an essential role in geographic information extraction and spatial semantic understanding. Traditional appr...
Slag tin content is a key quality indicator in top-blown furnace tin smelting. For accurate forecasting, noisy, minute-level process signals must be linked to sparse and delayed slag assays. Limited research has been conducted on the use of large lan...
Underwater image analysis is affected by light scattering, wavelength-dependent attenuation, low contrast, and suspended particles, which reduce the discriminative visual features. Current multi-scale Vision Transformers are not well-suited to these...
Building change detection (BCD) is crucial for urban construction and planning. The powerful discriminative ability of deep convolutions in deep learning-based BCD methods has considerably increased the accuracy and efficiency. However, dense and con...
Object detection in low-light scenes is difficult because weak illumination reduces local contrast, amplifies sensor noise, and makes small or occluded objects hard to localize. Existing enhancement-before-detection pipelines can improve visual brigh...
Tea bud targets are typically located in complex environments characterized by multi-scale variations, high density, and strong color resemblance to the background, which pose significant challenges for rapid and accurate detection. To address these...
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