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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 21–30 of 296 papers

TitleStatusHype
Conditional Diffusion Models for Weakly Supervised Medical Image SegmentationCode1
Context Decoupling Augmentation for Weakly Supervised Semantic SegmentationCode1
Affinity Attention Graph Neural Network for Weakly Supervised Semantic SegmentationCode1
Class Tokens Infusion for Weakly Supervised Semantic SegmentationCode1
DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic SegmentationCode1
Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic SegmentationCode1
Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic SegmentationCode1
Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic SegmentationCode1
Class-incremental Continual Learning for Instance Segmentation with Image-level Weak SupervisionCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
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