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Weakly-Supervised Semantic Segmentation

The semantic segmentation task is to assign a label from a label set to each pixel in an image. In the case of fully supervised setting, the dataset consists of images and their corresponding pixel-level class-specific annotations (expensive pixel-level annotations). However, in the weakly-supervised setting, the dataset consists of images and corresponding annotations that are relatively easy to obtain, such as tags/labels of objects present in the image.

( Image credit: Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing )

Papers

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Showing 171–180 of 296 papers

TitleStatusHype
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network—0
LID 2020: The Learning from Imperfect Data Challenge Results—0
Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation—0
Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation—0
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference—0
Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularization—0
Movable-Object-Aware Visual SLAM via Weakly Supervised Semantic Segmentation—0
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning—0
Multi-Label Prototype Visual Spatial Search for Weakly Supervised Semantic Segmentation—0
Multi-Miner: Object-Adaptive Region Mining for Weakly-Supervised Semantic Segmentation—0
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