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Multi-scale pooling learning for camouflaged instance segmentation
Camouflaged instance segmentation (CIS) focuses on handling instances that attempt to blend into the background. However, existing CIS methods...
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Local many-to-many matching via ROI feature decomposition for multi-object tracking
For multi-object tracking (MOT), data association often involves the process of matching appearance features. Typically, feature embedding for data...
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DFMA-ICH: a deformable mixed-attention model for intracranial hemorrhage lesion segmentation based on deep supervision
Intracranial hemorrhage (ICH) is a common and critical disease in clinical, with rapid progression, high disability, and mortality rates. Existing...
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LIP: Local Importance-Based Pooling
Spatial downsampling layers are favored in convolutional neural networks (CNNs) to downscale feature maps for larger receptive fields and less memory...
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Cracks identification using mask region-based denoised deformable convolutional network
Cracks are one of the critical structural defects in building assessment to determine the integrity of civil structure. Structural surveying process...
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Optimized deformable convolution network for detection and mitigation of ocular artifacts from EEG signal
Electroencephalogram (EEG) is the key component in the field of analyzing brain activity and behavior. EEG signals are affected by artifacts in the...
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Towards End-to-End Semi-Supervised Table Detection with Deformable Transformer
Table detection is the task of classifying and localizing table objects within document images. With the recent development in deep learning methods,...
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LiftReg: Limited Angle 2D/3D Deformable Registration
We propose LiftReg, a 2D/3D deformable registration approach. LiftReg is a deep registration framework which is trained using sets of digitally...
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RAOD: refined oriented detector with augmented feature in remote sensing images object detection
Object detection is a challenging task in remote sensing. Aerial images are distinguished by complex backgrounds, arbitrary orientations, and dense...
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Lip Reading Using Deformable 3D Convolution and Channel-Temporal Attention
At present, for lip-reading with isolated words, the front-end networks mostly use a combination of 3D convolutional layer and 2D convolutional...
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MSBC-Net: Automatic rectal cancer segmentation from MR scans
Accurate segmentation of rectal cancer and rectal wall based on high-resolution T2-weighted magnetic resonance imaging (MRI-HRT2) is the basis of...
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DT-MIL: Deformable Transformer for Multi-instance Learning on Histopathological Image
Learning informative representations is crucial for classification and prediction tasks on histopathological images. Due to the huge image size,...
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SARNet: Spatial Attention Residual Network for pedestrian and vehicle detection in large scenes
With the development of high-resolution camera technology, the shooting scene coverage has reached the square kilometer level, thousands of people...
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Driver action recognition using deformable and dilated faster R-CNN with optimized region proposals
Distracted driver action is the main cause of road traffic crashes, which threatens the security of human life and public property. Based on the...
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Real-time and accurate model of instance segmentation of foods
Instance segmentation of foods is an important technology to ensure the food success rate of meal-assisting robotics. However, due to foods have...
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Unified automated deep learning framework for segmentation and classification of liver tumors
Cancer is a devastating and deadly disease, and liver cancer is one of the leading causes of cancer deaths. Early detection of liver tumor is...
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ABC-Trans: a novel adaptive border-augmented cross-attention transformer for object detection
Transformer-based vision object detection has demonstrated superior performance due to its effective removal of the need for many hand-designed...
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SAR-ShipSwin: enhancing SAR ship detection with robustness in complex environment
Contemporary synthetic aperture radar (SAR) image processing techniques face various challenges, particularly in ship detection, background noise...
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Blood Leukocyte Object Detection According to Model Parameter-Transfer and Deformable Convolution
Currently, leukocyte detection has the problem of scarcity of labeled samples, so a focal dataset must be expanded by merging multiple datasets. At...
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Improving generalization for geometric variations in images for efficient deep learning
Deep Learning models for tasks such as image classification have a hard time adapting to the unseen geometric variations (such as scale, perspective,...
