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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

Showing 5160 of 296 papers

TitleStatusHype
ISIM: Iterative Self-Improved Model for Weakly Supervised SegmentationCode1
Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentationCode1
SemFormer: Semantic Guided Activation Transformer for Weakly Supervised Semantic SegmentationCode1
Dual Progressive Transformations for Weakly Supervised Semantic SegmentationCode1
Expansion and Shrinkage of Localization for Weakly-Supervised Semantic SegmentationCode1
Mining Discriminative Food Regions for Accurate Food RecognitionCode1
Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology ImagesCode1
RecurSeed and EdgePredictMix: Pseudo-Label Refinement Learning for Weakly Supervised Semantic Segmentation across Single- and Multi-Stage FrameworksCode1
L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic SegmentationCode1
Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against ThresholdsCode1
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