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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 251–275 of 296 papers

TitleStatusHype
Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation—0
Gated CRF Loss for Weakly Supervised Semantic Image Segmentation—0
Movable-Object-Aware Visual SLAM via Weakly Supervised Semantic Segmentation—0
Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation—0
Harvesting Information from Captions for Weakly Supervised Semantic Segmentation—0
Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic SegmentationCode0
Weakly Supervised Semantic Segmentation of Satellite Images—0
Fully Using Classifiers for Weakly Supervised Semantic Segmentation with Modified Cues—0
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference—0
The effect of scene context on weakly supervised semantic segmentation—0
CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic SegmentationCode0
Learning Segmentation Masks with the Independence Prior—0
Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
Towards Closing the Gap in Weakly Supervised Semantic Segmentation with DCNNs: Combining Local and Global Models—0
Convolutional Simplex Projection Network (CSPN) for Weakly Supervised Semantic SegmentationCode0
Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features—0
Bootstrapping the Performance of Webly Supervised Semantic SegmentationCode0
Weakly-Supervised Semantic Segmentation Network With Deep Seeded Region GrowingCode0
Constrained-CNN losses for weakly supervised segmentationCode0
Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic SegmentationCode0
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation—0
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning—0
Learning random-walk label propagation for weakly-supervised semantic segmentation—0
Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation—0
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