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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 111–120 of 296 papers

TitleStatusHype
Hierarchical Semantic Contrast for Weakly Supervised Semantic SegmentationCode1
Coupling Global Context and Local Contents for Weakly-Supervised Semantic SegmentationCode0
Convolutional Simplex Projection Network (CSPN) for Weakly Supervised Semantic SegmentationCode0
A Multimodal Approach Combining Structural and Cross-domain Textual Guidance for Weakly Supervised OCT SegmentationCode0
Constrained-CNN losses for weakly supervised segmentationCode0
HisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency RegularizationCode0
HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide ImagesCode0
High-fidelity Pseudo-labels for Boosting Weakly-Supervised SegmentationCode0
PSDPM: Prototype-based Secondary Discriminative Pixels Mining for Weakly Supervised Semantic SegmentationCode0
COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical imagesCode0
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