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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 110 of 296 papers

TitleStatusHype
Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic SegmentationCode2
Modeling the Label Distributions for Weakly-Supervised Semantic SegmentationCode2
DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic SegmentationCode2
From SAM to CAMs: Exploring Segment Anything Model for Weakly Supervised Semantic SegmentationCode2
Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with TransformersCode2
Learning without Exact Guidance: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical LabelsCode2
CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic SegmentationCode2
Cross Language Image Matching for Weakly Supervised Semantic SegmentationCode2
Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic SegmentationCode2
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic SegmentationCode2
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