Semantic Segmentation
The idea of semantic segmentation is to recognize and understand what is in an image at the pixel-level.
State-of-the-art leaderboards
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Subtasks
Latest papers without code
Dual Graph Convolutional Network for Semantic Segmentation
Exploiting long-range contextual information is key for pixel-wise prediction tasks such as semantic segmentation.
Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images
Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue.
PILOT: Physics-Informed Learned Optimal Trajectories for Accelerated MRI
Magnetic Resonance Imaging (MRI) has long been considered to be among "the gold standards" of diagnostic medical imaging.
An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image
A CNN segmentation model was trained based on the augmented training data by leave-one-out strategy.
SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation
Deep learning has largely reduced the need for manual feature selection in image segmentation.
FEATURE SELECTION IMAGE CLASSIFICATION MEDICAL IMAGE SEGMENTATION SEMANTIC SEGMENTATION
Image Segmentation using Multi-Threshold technique by Histogram Sampling
We observed a maximum speed-up of 35. 58x and a minimum speed-up of 10. 21x on popular image processing benchmarks.
BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding
For understanding generic documents, information like font sizes, column layout, and generally the positioning of words may carry semantic information that is crucial for solving a downstream document intelligence task.
INSTANCE SEGMENTATION LANGUAGE MODELLING SEMANTIC SEGMENTATION
Semantic Foreground Inpainting from Weak Supervision
Our approach is inherently more efficient than the previous two-stage state-of-the-art method, and outperforms it by a margin of 3% IoU for the inpainted foreground regions on Cityscapes.
Virtual organelle self-coding for fluorescence imaging via adversarial learning
A detailed comparative analysis is also conducted on the performance of the cGAN network between predicting fluorescence channels based on phase contrast or based on another fluorescence channel using human breast cancer MDA-MB-231 cell line as a test case.
Chargrid-OCR: End-to-end trainable Optical Character Recognition through Semantic Segmentation and Object Detection
We present an end-to-end trainable approach for optical character recognition (OCR) on printed documents.
OBJECT DETECTION OPTICAL CHARACTER RECOGNITION SEMANTIC SEGMENTATION