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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 41–50 of 296 papers

TitleStatusHype
Learning without Exact Guidance: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical LabelsCode2
Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation—0
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic SegmentationCode2
Weakly Supervised Co-training with Swapping Assignments for Semantic SegmentationCode1
Scribble Hides Class: Promoting Scribble-Based Weakly-Supervised Semantic Segmentation with Its Class LabelCode1
HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of Histological Tissue Type in Whole Slide Images—0
0-1 laws for pattern occurrences in phylogenetic trees and networks—0
CoBra: Complementary Branch Fusing Class and Semantic Knowledge for Robust Weakly Supervised Semantic Segmentation—0
Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic SegmentationCode0
Semantic Prompt Learning for Weakly-Supervised Semantic SegmentationCode1
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