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The black mamba
\"Fascinating images accompany information about the black mamba. The combination of high-interest subject matter and narrative text is intended for students in grades 3 through 7\"--Provided by publisher.
Book
YOLOv5_(m)amba: unmanned aerial vehicle object detection based on bidirectional dense feedback network and adaptive gate feature fusion
Addressing the problem that the object size in Unmanned Aerial Vehicles (UAVs) aerial images is too small and contains limited feature information, leading to existing detection algorithms having less than ideal performance in small object detection, we propose a UAV aerial object detection system named YOLv5_mamba based on bidirectional dense feedback network and adaptive gate feature fusion. This paper improves the You Only Look Once Version 5 (YOLOv5) algorithm by firstly introducing the Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) module from YOLOv8 into the backbone network to enhance the feature extraction capability of the backbone network. Furthermore, the mamba module and C2f module are introduced to construct a bidirectional dense feedback network to enhance the transfer of contextual information in the neck part. Thirdly, an adaptive gate feature fusion network is proposed to improve the head part of YOLOv5 and enhance its final detection capability. Experimental results on the public UAV aerial dataset VisDrone2019 demonstrate that the proposed algorithm improves the detection accuracy by 9.3% compared to the original YOLOv5 baseline network, showing better detection performance for small objects. For the UCAS_AOD dataset, the proposed algorithm outperforms YOLOv5-s by 9%. In the case of the DIOR dataset, the proposed algorithm exceeds YOLOv5-s by 12%.
Journal Article
Black mamba
An introduction to the characteristics and behavior of the black mamba snake, which lives in southern and eastern Africa.
Book
Deep Learning Empowered Microstructure Codebook: New Paradigm for Multi‐Parameter Tissue Characterization Estimation
Diffusion MRI (dMRI) enables the examination of microstructural profiles and tissue changes using specific microstructural modeling, but it requires long acquisition times and dense q‐space sampling. Current deep learning‐based methods are also limited by their inability to generalize across protocols and extend to new microstructural indices. This work introduces a novel framework that addresses these limitations by learning a microstructural codebook, facilitating accurate, rapid, and multi‐parameter microstructure imaging. Our approach integrates the spherical mean technique (SMT) with a hybrid Mamba‐CNN architecture and learnable tissue‐compartment kernels, effectively capturing multiscale spatial dependencies while linking spherical mean signals to biophysical microstructure models. This design enhances both interpretability and adaptability, enabling robust estimation of 24 microstructural metrics derived from 8 widely used biophysical diffusion models, even under undersampled acquisition conditions. Notably, the framework demonstrates strong generalization across diverse acquisition protocols and enables seamless adaptation to novel microstructural indices with minimal fine‐tuning, underscoring its flexibility and practical utility. Extensive experiments on multiple datasets confirm the method's superior accuracy, generalization, and transferability. This work presents a codebook‐driven framework for microstructure imaging that bridges biophysical modeling and deep learning to enable more interpretable and adaptable dMRI analysis. The code is available at https://github.com/1nlandempire/Microstructure‐codebook‐imaging. Key Points A learnable microstructure codebook maps parameterized spherical‐mean signals to multiple biophysical indices, enabling high‐quality multi‐parameter estimation from undersampled dMRI. Built on a hybrid Mamba‐CNN network, we introduce a novel parameterization of the spherical‐mean signal that substantially improves generalization across b‐values and acquisition protocols. The pretrained codebook supports minimal fine‐tuning to new microstructural indices, improving extensibility and interpretability. We propose DEMIC, a deep‐learning microstructure codebook framework for dMRI microstructure imaging: (1) accurate multi‐parameter estimation from undersampled data; (2) robust cross‐protocol and cross‐model generalization; and (3) flexible transfer to new microstructural indices via fine‐tuning.
Journal Article
HR-Mamba: Building Footprint Segmentation with Geometry-Driven Boundary Regularization
Building extraction underpins land-use assessment, urban planning, and disaster mitigation, yet dense urban scenes still cause missed small objects, target adhesion, and ragged contours. We present High-Resolution-Mamba (HR-Mamba), a high-resolution semantic segmentation network that augments a High-Resolution Network (HRNet) parallel backbone with edge-aware and sequence-state modeling. A Canny-enhanced, median-filtered stem stabilizes boundaries under noise; Involution-based residual blocks capture position-specific local geometry; and a Mamba-based State Space Models (Mamba-SSM) global branch captures cross-scale long-range dependencies with linear complexity. Training uses a composite loss of binary cross entropy (BCE), Dice loss, and Boundary loss, with weights selected by joint grid search. We further design a feature-driven adaptive post-processing pipeline that includes geometric feature analysis, multi-strategy simplification, multi-directional regularization, and topological consistency verification to produce regular, smooth, engineering-ready building outlines. On dense urban imagery, HR-Mamba improves F1-score from 80.95% to 83.93%, an absolute increase of 2.98% relative to HRNet. We conclude that HR-Mamba jointly enhances detail fidelity and global consistency and offers a generalizable route for high-resolution building extraction in remote sensing.
Journal Article
MambaOVD: a Mamba-based open-vocabulary object detection method
Open-vocabulary object detection (OVD) is a critical research area in computer vision, particularly for applications in autonomous driving and robotics. Many existing OVD methods adopt transformer architectures for image-text fusion, utilizing self-attention mechanisms to model complex dependencies. However, transformer-based approaches are often computationally demanding, limiting their practical deployment. To address this issue, we propose MambaOVD, a novel open-vocabulary object detection method based on the Mamba architecture. MambaOVD consists of four key modules: an image encoder, a text encoder, a Mamba-based image-text fusion module, and a detection head. The image encoder extracts visual features, the text encoder generates text embeddings, the fusion module integrates multimodal information using Mamba layers, and the detection head performs object localization and classification. To evaluate the effectiveness of MambaOVD, we trained the model on the Objects365 (V1) and GoldG datasets, and conducted testing on the LVIS minival and AutoMine datasets. Experimental results show that MambaOVD achieves superior performance compared to state-of-the-art (SOTA) models, including YOLO-World-S, GLIPv2_T, and DetCLIP_T, demonstrating advantages in both qualitative and quantitative evaluations.
Journal Article
A Survey on Visual Mamba
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 with image size, as well as increasing computational demands, researchers are currently exploring how to adapt Mamba for computer vision tasks. This paper is the first comprehensive survey that aims to provide an in-depth analysis of Mamba models within the domain of computer vision. It begins by exploring the foundational concepts contributing to Mamba’s success, including the SSM framework, selection mechanisms, and hardware-aware design. Then, we review these vision Mamba models by categorizing them into foundational models and those enhanced with techniques including convolution, recurrence, and attention to improve their sophistication. Furthermore, we investigate the widespread applications of Mamba in vision tasks, which include their use as a backbone in various levels of vision processing. This encompasses general visual tasks, medical visual tasks (e.g., 2D/3D segmentation, classification, image registration, etc.), and remote sensing visual tasks. In particular, we introduce general visual tasks from two levels: high/mid-level vision (e.g., object detection, segmentation, video classification, etc.) and low-level vision (e.g., image super-resolution, image restoration, visual generation, etc.). We hope this endeavor will spark additional interest within the community to address current challenges and further apply Mamba models in computer vision.
Journal Article
C2M-Mamba: drug-drug interaction prediction based on cross-modal cross-Mamba
Accurately predicting potential drug-drug interactions (DDIs) from multimodal data is critical for medication safety and adverse drug reaction prevention. Existing methods face challenges in modeling long-range dependencies and effectively integrating heterogeneous features from structured molecular data and unstructured text. To address these limitations, we propose C2M-Mamba, a cross-modal framework that integrates convolutional neural networks, Mamba, and cross-Mamba (CroMamba) to capture discriminative features from drug descriptions, SMILES sequences, and social media texts. The model efficiently handles long-range dependencies through state space models while enabling effective cross-modal fusion. Comprehensive evaluations on the DDIExtraction2013 dataset demonstrate that C2M-Mamba outperforms 10 state-of-the-art baselines, achieving 82.37% precision, 80.98% F1-score, and 88.73% AUC. The proposed approach also exhibits robust performance in handling class imbalance and provides interpretable predictions, offering a reliable solution for multimodal DDI prediction with potential applications in pharmacovigilance and personalized medicine.
Journal Article
Multi-scale feature fusion-based vision mamba for robust plant disease image classification on field-acquired plantdoc data
IntroductionExisting convolutional neural networks and Transformers cannot effectively capture fine-grained local lesion features and long-range contextual dependencies simultaneously in field-collected plant images. To address this research limitation, we aim to design an effective lightweight model suitable for plant disease identification in complex field scenarios.MethodsThis work proposes an improved Vision Mamba network for plant disease classification based on the challenging PlantDoc dataset. Three dedicated modules are embedded into the framework, including the Multi-Scale Feature Fusion Module (MFFM), Adaptive Channel Attention Mechanism (ACAM) and Lightweight Residual Connection (LRC). The MFFM fuses multi-scale texture, shape and semantic lesion features extracted from shallow, medium and deep network layers. The ACAM adaptively highlights disease-related feature channels and suppresses irrelevant background interference. The LRC structure is adopted to relieve the gradient vanishing problem existing in deep selective state space model (SSM) networks.ResultsExperimental results on the filtered PlantDoc dataset show that the presented model obtains an overall accuracy of 92.67%, macro precision of 91.83%, macro recall of 91.56% and macro F1-score of 91.70% on independent test samples, which outperforms the original Vision Mamba baseline by 5.33% in accuracy. Five-fold stratified cross-validation achieves stable accuracy at 92.41 ± 0.24%, and paired t-tests prove that the performance improvement is statistically significant with p<0.05. Ablation experiments confirm the combined contribution of the three designed modules.DiscussionError analysis and confusion matrix visualization reveal that the main classification errors are derived from high similarity among different plant disease categories. This study fully verifies the application potential of state space models in agricultural computer vision tasks. The proposed method can serve as an efficient technical scheme for intelligent identification of crop diseases and is well applicable to edge device deployment in precision agriculture practice.
Journal Article
Toward smart agriculture: a hybrid mamba-transformer vision framework for plant disease detection
Plant disease detection under complex field conditions remains a critical challenge for precision agriculture due to varying illumination, scale variations, subtle lesion patterns, and inter-class visual ambiguity. This study proposes MAFusionNet, a disease-aware hybrid vision framework integrating Mamba and Transformer architectures, with components explicitly designed for plant disease-specific challenges. The MAFusion Mixer operates parallel CS-Mamba and self-attention branches to simultaneously capture sequential lesion boundary evolution and global diseasecontext spatial relationships. The CS-Mamba branch employs the SS2D-LS Block with twodimensional selective scanning and Local-Selective enhancement for linear-complexity longrange modeling while preserving 2D lesion morphology. The PConv operator uses asymmetric directional kernels forming cross-shaped receptive fields to capture anisotropic disease patterns such as vein-aligned blights and directional rust streaks. We constructed PD40, a large-scale dataset with 80,369 expert-verified annotated images across 40 disease categories spanning eight major crops, with inter-annotator agreement Cohen's
= 0.874. Extensive experiments demonstrate that MAFusionNet achieves 94.7% mAP
and 81.8% mAP
on PD40, surpassing 25 state-of-the-art baselines including recent hybrid Mamba-Transformer detectors (CropMamba, HybridMamba, Mamba-DETR), with comprehensive ablation studies validating each component's non-redundant contribution. Edge deployment analysis on NVIDIA Jetson hardware demonstrates practical feasibility: the compressed MAFusionNet-T-Lite variant (8.7M parameters) achieves 89.3% mAP
at 18.4 FPS on Jetson Nano with 8.3W power consumption. The dataset and code are available at PD40-Dataset GitHub Repository.
Journal Article