Semantic Segmentation
Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category.
Some example benchmarks for this task are Cityscapes, PASCAL VOC and ADE20K. Models are usually evaluated with the Mean Intersection-Over-Union (Mean IoU) and Pixel Accuracy metrics.
( Image credit: CSAILVision )
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Subtasks
Latest papers without code
The Edge of Depth: Explicit Constraints between Segmentation and Depth
In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images.
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation
To satisfy the stringent requirements on computational resources in the field of real-time semantic segmentation, most approaches focus on the hand-crafted design of light-weight segmentation networks.
Attention-based Assisted Excitation for Salient Object Segmentation
In this paper, object-based attention in human visual cortex inspires us to introduce a mechanism for modification of activations in feature maps of CNNs.
Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation
The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
DATA AUGMENTATION IMAGE GENERATION MEDICAL IMAGE SEGMENTATION SEMANTIC SEGMENTATION
BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation
Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously.
FGN: Fully Guided Network for Few-Shot Instance Segmentation
Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for training.
FEW-SHOT LEARNING INSTANCE SEGMENTATION SEMANTIC SEGMENTATION
Segmenting Transparent Objects in the Wild
To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10, 428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets.
Attention-based Multi-modal Fusion Network for Semantic Scene Completion
This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images.
EOLO: Embedded Object Segmentation only Look Once
In this paper, we introduce an anchor-free and single-shot instance segmentation method, which is conceptually simple with 3 independent branches, fully convolutional and can be used by easily embedding it into mobile and embedded devices.
Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset
In this work we explore the performance of DCNNs on semantic segmentation using spaceborne polarimetric synthetic aperture radar (PolSAR) datasets.