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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 191–200 of 296 papers

TitleStatusHype
Prompt Categories Cluster for Weakly Supervised Semantic Segmentation—0
Realizing Pixel-Level Semantic Learning in Complex Driving Scenes based on Only One Annotated Pixel per Class—0
Rethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise—0
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation—0
Semantic Component Analysis—0
Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network—0
Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation—0
Semi-supervised Semantic Segmentation via Strong-weak Dual-branch Network—0
SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation—0
Small Objects Matters in Weakly-supervised Semantic Segmentation—0
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