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Methods/ASPP

ASPP

Atrous Spatial Pyramid Pooling

Computer VisionIntroduced 200083 papers
Source Paper

Description

Atrous Spatial Pyramid Pooling (ASPP) is a semantic segmentation module for resampling a given feature layer at multiple rates prior to convolution. This amounts to probing the original image with multiple filters that have complementary effective fields of view, thus capturing objects as well as useful image context at multiple scales. Rather than actually resampling features, the mapping is implemented using multiple parallel atrous convolutional layers with different sampling rates.

Papers Using This Method

SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling2025-04-17Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing2025-04-03DuckSegmentation: A segmentation model based on the AnYue Hemp Duck Dataset2025-03-27DynSegNet:Dynamic Architecture Adjustment for Adversarial Learning in Segmenting Hemorrhagic Lesions from Fundus Images2025-02-13Hybridization of Attention UNet with Repeated Atrous Spatial Pyramid Pooling for Improved Brain Tumour Segmentation2025-01-22Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning2025-01-19AI Driven Water Segmentation with deep learning models for Enhanced Flood Monitoring2025-01-14Mapping Africa Settlements: High Resolution Urban and Rural Map by Deep Learning and Satellite Imagery2024-11-05Automated Surgical Skill Assessment in Endoscopic Pituitary Surgery using Real-time Instrument Tracking on a High-fidelity Bench-top Phantom2024-09-25A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation2024-09-24Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism2024-09-13WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking2024-09-05Deep Convolutional Neural Networks Meet Variational Shape Compactness Priors for Image Segmentation2024-05-23Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation2024-04-02One model to use them all: Training a segmentation model with complementary datasets2024-02-29CAFCT-Net: A CNN-Transformer Hybrid Network with Contextual and Attentional Feature Fusion for Liver Tumor Segmentation2024-01-30MixMobileNet: A Mixed Mobile Network for Edge Vision Applications2024-01-26Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge2024-01-22M3FPolypSegNet: Segmentation Network with Multi-frequency Feature Fusion for Polyp Localization in Colonoscopy Images2023-10-09An easy zero-shot learning combination: Texture Sensitive Semantic Segmentation IceHrNet and Advanced Style Transfer Learning Strategy2023-09-30