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
Harvesting Information from Captions for Weakly Supervised Semantic Segmentation
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They do not require additional curation as it is the case for the clean class tags used by current weakly supervised approaches and they provide textual context for the classes present in an image.
Online Normalization for Training Neural Networks
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Online Normalization is a new technique for normalizing the hidden activations of a neural network.
IMAGE CLASSIFICATION LANGUAGE MODELLING SEMANTIC SEGMENTATION
DARNet: Deep Active Ray Network for Building Segmentation
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In this paper, we propose a Deep Active Ray Network (DARNet) for automatic building segmentation.
Efficient Ladder-style DenseNets for Semantic Segmentation of Large Images
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Recent progress of deep image classification models has provided great potential to improve state-of-the-art performance in related computer vision tasks.
Budget-aware Semi-Supervised Semantic and Instance Segmentation
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Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention.
Video Instance Segmentation
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The goal of this new task is simultaneous detection, segmentation and tracking of instances in videos.
INSTANCE SEGMENTATION SEMANTIC SEGMENTATION VIDEO UNDERSTANDING
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
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We also revisit existing approaches for fast convergence, and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted setting.
ARCHITECTURE SEARCH IMAGE CLASSIFICATION INSTANCE SEGMENTATION OBJECT DETECTION SEMANTIC SEGMENTATION VIDEO CLASSIFICATION
Style transfer based data augmentation in material microscopic image processing
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We validate the presented approach by using real image data from experiment and simulated image data from Monte Carlo Potts Models, which simulate the growth of polycrystal.
EdgeSegNet: A Compact Network for Semantic Segmentation
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In this study, we introduce EdgeSegNet, a compact deep convolutional neural network for the task of semantic segmentation.
T-Net: Encoder-Decoder in Encoder-Decoder architecture for the main vessel segmentation in coronary angiography
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As a result, all features from the low-level to the high-level extracted from the encoder are delivered from the beginning of the decoder to predict a more accurate mask.