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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 151–160 of 296 papers

TitleStatusHype
Multi-class Token Transformer for Weakly Supervised Semantic SegmentationCode1
Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic SegmentationCode1
Cross Language Image Matching for Weakly Supervised Semantic SegmentationCode2
Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with TransformersCode2
Class Re-Activation Maps for Weakly-Supervised Semantic SegmentationCode1
PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds—0
Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid PoolingCode0
Modeling the Background for Incremental and Weakly-Supervised Semantic SegmentationCode1
Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods—0
MuSCLe: A Multi-Strategy Contrastive Learning Framework for Weakly Supervised Semantic Segmentation—0
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