Mamba, based on the state space model (SSM), offers an efficient alternative to the quadratic complexity of attention, showing promise for long-sequence data processing and global modeling in 3D object detection. However, applying it to this domain p...
The growth of aquaculture poses significant challenges for disease management, impacting economic sustainability and global food security. Traditional diagnostics are slow and require expertise, while current deep learning models, including CNNs and...
The accurate identification of natural disasters is crucial in ensuring effective post-disaster relief efforts. However, the existing models for disaster classification often incur high costs. To address this, we propose leveraging the most advanced...
Facial beauty prediction (FBP) is a cutting-edge task in deep learning that aims to equip machines with the ability to assess facial attractiveness in a human-like manner. In human perception, facial beauty is strongly associated with facial symmetry...
In the field of remote sensing (RS), change detection (CD) methods are critical for analyzing the quality of images shot over various geographical areas, particularly for high-resolution images. However, there are some shortcomings of the widely used...
Background: Recently, the adoption of AI-based technologies has been accelerating in the field of medical image analysis. For the early diagnosis and treatment planning of breast cancer, Automated Breast Ultrasound (ABUS) has emerged as a safe and no...
Trajectory prediction constitutes a key technology for intelligent systems to forecast future movements of dynamic agents, yet it faces significant challenges due to the uncertainty of motion behavior. We propose iDMa, a stochastic trajectory predict...
UAV-based video multi-object tracking (MOT) is a significant task in the field of remote sensing. However, current research still faces critical issues: (1) the limitations of the single architecture of DNNs inherently hinder performance improvement...
As fundamental geophysical information, the high-precision detection of shallow water bathymetry is critical data support for the utilization of island resources and coral reef protection delimitation. In recent years, the combination of active and p...
With the widespread use of encryption protocols on network data, fast and effective encryption traffic classification can improve the efficiency of traffic analysis. A resampling method combining Wasserstein GAN and random selection is proposed for s...
This study presents AMUSE++, an advanced speech enhancement framework that extends the MUSE++ model by redesigning its core Mamba module with two major improvements. First, the originally unidirectional one-dimensional (1D) Mamba is transformed into...
Hyperspectral image (HSI) classification requires models that can simultaneously capture spatial structures and spectral continuity. Although state space models (SSMs), particularly Mamba, have shown strong capability in long-sequence modeling, their...
Accurate segmentation of water bodies from high-resolution remote sensing imagery is crucial for water resource management and ecological monitoring. However, small and morphologically complex water bodies remain difficult to detect due to scale vari...
This study proposes a novel framework, Mamba-DQN, which integrates the state space-based time-series encoder Mamba-SSM into the Deep Q-Network (DQN) architecture to improve reinforcement learning performance in dynamic environments. Conventional rein...
To address the complexity of ship trajectory prediction, this study explored the efficacy of the Mamba model, a relatively new deep-learning framework. In order to evaluate the performance of the Mamba model relative to traditional models, which ofte...
The efficient and precise recognition of diarrhea-related behaviors in Jinnan calves is crucial for ensuring their healthy development. Nevertheless, conventional behavior recognition techniques are often limited by a notable decline in performance w...
With the rapid growth of urban populations and the expansion of metro networks, accurate energy consumption prediction has become a critical task for optimizing metro operations and supporting low-carbon city development. Traditional statistical and...
Unmanned Aerial Vehicles (UAVs) are increasingly being used in critical infrastructure, defense, and civilian applications, and face new cybersecurity threats. In this work, we present a novel hybrid deep learning architecture that combines Mamba, Ko...
To explore a more accurate prediction method for subgrade settlement induced by underpass construction, this study takes the existing railway project of Ningbo Yuanyi Road underpass as a case to construct a subgrade settlement prediction model based...
Mulberry (Morus spp.), as an economically significant crop in sericulture and medicinal applications, faces severe threats to leaf yield and quality from pest and disease infestations. Traditional detection methods relying on chemical pesticides and...
Pine wilt disease (PWD) is a highly destructive worldwide forest quarantine disease that has the potential to destroy entire pine forests in a relatively brief period, resulting in significant economic losses and environmental damage. Manual monitori...
Currently, most images captured by high-pixel devices such as mobile phones, camcorders, and drones are in RGGB format. However, image quality in extremely dark scenes often needs improvement. Traditional methods for processing these dark RGGB images...
Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource...
State space models (SSM) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently shown significant potential in long-sequence modeling. Since the complexity of transformers’ self-attention mechanism is quadratic...
12 December 2024
How to effectively extract spectral and spatial information and apply it to hyperspectral image classification (HSIC) has been a hot research topic. In recent years, the transformer-based HSIC models have attracted much interest due to their advantag...
Tomato blight significantly threatened tomato yield and quality, making precise disease detection essential for modern agricultural practices. Traditional segmentation models often struggle with over-segmentation and missed segmentation, particularly...
Traditional single image super-resolution (SISR) methods, which focus on integer scale super-resolution, often require separate training for each scale factor, leading to increased computational resource consumption. In this paper, we propose MambaSR...
Current calculation methods for the carbon content as received (Car) of coal rely on multiple instruments, leading to high costs for enterprises. There is a need for a cost-effective model that maintains accuracy in CO2 emission accounting. This stud...
In recent years, Mamba has gained increasing importance in the field of image restoration, gradually outperforming traditional convolutional neural networks (CNNs) and Transformers. However, the existing Mamba-based networks mainly focus on capturing...
Deep learning has recently achieved remarkable progress in hyperspectral image (HSI) classification. Among these advancements, the Transformer-based models have gained considerable attention due to their ability to establish long-range dependencies....
Remaining Useful Life (RUL) plays a critical role in prognostics and health management systems. It helps increase reliability and safety for the equipment used in the modern industry. The new idea proposed is the Mamba deep learning model, which aims...
As the resolution and data volume of remote sensing imagery continue to grow, achieving efficient compression without sacrificing reconstruction quality remains a major challenge, given that traditional handcrafted codecs often fail to balance rate-d...
Accurate stock price prediction requires the integration of heterogeneous data streams, yet conventional techniques struggle to simultaneously leverage fine-grained micro-stock features and broader macroeconomic indicators. To address this gap, we pr...
Accurate detection of wildlife on the Tibetan Plateau is particularly challenging due to complex natural environments, significant scale variations, and the limited availability of annotated data. To address these issues, we propose a semantic-guided...
Remote sensing change detection (RSCD) aims to utilize paired temporal remote sensing images to detect surface changes in the same area. Traditional CNN-based methods are limited by the size of the receptive field, making it difficult to capture the...
Remote sensing images often degrade during acquisition due to various environmental factors, leading to noise contamination and loss of texture details. Existing methods based on convolutional neural networks (CNNs) are limited by their local recepti...
Modern Earth observation combines high spatial resolution, wide swath, and dense temporal sampling, producing image grids and sequences far beyond the regime of standard vision benchmarks. Convolutional networks remain strong baselines but struggle t...
Detecting small split pins on transmission lines poses significant challenges, including low accuracy in complex backgrounds and slow inference speeds. To address these limitations, this study proposes a novel two-stage collaborative detection framew...
This study introduces an efficient fundus image enhancement framework based on an improved Mamba model and the Denoising Diffusion Probabilistic Model (DDPM). By integrating wavelet transform for local feature extraction and applying a reverse diffus...
Remote sensing object detection (RSOD) faces challenges such as large variations in target scale, diverse orientations, and complex backgrounds. Existing approaches struggle to simultaneously balance local feature extraction and global context modeli...
Accurate skin lesion classification is crucial for the early detection of malignant lesions, including melanoma, as well as improved patient outcomes. While convolutional neural networks (CNNs) excel at capturing local morphological features, they st...
Accurate State of Charge (SOC) estimation is challenged by battery aging and complex internal dynamics. This work introduces a novel framework, Mamba-PG, that leverages the Mamba architecture to integrate internal gas pressure—a direct indicato...
Timely and accurate detection of forest fires through unmanned aerial vehicle (UAV) remote sensing target detection technology is of paramount importance. However, multiscale targets and complex environmental interference in UAV remote sensing images...
Intelligent Transportation Systems (ITS) play a pivotal role in addressing traffic congestion, inefficiency, and safety concerns. Emergency lane control on highways is a critical ITS component, yet existing strategies often lack flexibility, theoreti...
The semantic segmentation of satellite and UAV remote sensing imagery is pivotal for address exploration, change detection, quantitative analysis and urban planning. Recent advancements have seen an influx of segmentation networks utilizing convoluti...
Detection of changes in remote sensing relies on clean multi-temporal images, but cloud cover may considerably degrade image quality. Cloud removal, a critical image-restoration task, demands effective modeling of long-range spatial dependencies to r...
Underwater image enhancement is a challenging task due to the unique optical properties of water, which often lead to color distortion, low contrast, and detail loss. At the present stage, the methods based on the CNN have the problem of insufficient...
Accurate meteorological information is crucial for the safety of civil aviation flights. Complete wind field information is particularly helpful for planning flight routes. To address the challenge of accurately reconstructing wind fields, this paper...
In remote sensing image processing, cloud and cloud shadow detection is of great significance, which can solve the problems of cloud occlusion and image distortion, and provide support for multiple fields. However, the traditional convolutional or Tr...
Obesity is a major public health challenge linked to increased risks of chronic diseases. Effective prevention and intervention strategies require accurate classification and identification of key determinants. This study aims to develop a robust dee...
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