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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 141–150 of 296 papers

TitleStatusHype
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
Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery—0
P2Seg: Pointly-supervised Segmentation via Mutual Distillation—0
Clustering-Guided Class Activation for Weakly Supervised Semantic SegmentationCode0
PSDPM: Prototype-based Secondary Discriminative Pixels Mining for Weakly Supervised Semantic SegmentationCode0
Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation—0
Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation ModelsCode0
Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation—0
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