Weakly supervised Semantic Segmentation
137 papers with code • 3 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Weakly supervised Semantic Segmentation
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nuScenes
SemanticKITTI
SemanticPOSS
CheXlocalize
Most implemented papers
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
kangningthu/adele
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CVPR 2022
We discover a phenomenon that has been previously reported in the context of classification: the networks tend to first fit the clean pixel-level labels during an "early-learning" phase, before eventually memorizing the false annotations.
Cross Language Image Matching for Weakly Supervised Semantic Segmentation
cvi-szu/clims
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As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects.
RecurSeed and EdgePredictMix: Pseudo-Label Refinement Learning for Weakly Supervised Semantic Segmentation across Single- and Multi-Stage Frameworks
shjo-april/recurseed_and_edgepredictmix
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Although weakly supervised semantic segmentation using only image-level labels (WSSS-IL) is potentially useful, its low performance and implementation complexity still limit its application.
ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation using Object Border Fitting for Medical Images
Weakly Supervised Semantic Segmentation (WSSS) relying only on image-level supervision is a promising approach to deal with the need for Segmentation networks, especially for generating a large number of pixel-wise masks in a given dataset.
Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic Segmentation
First, modifying only the class token of the text prompt results in a greater impact on the Class Activation Map (CAM), compared to arguably more complex strategies that optimize the context.
Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation
In addition, our method also achieves state-of-the-art weakly supervised semantic segmentation performance on the PASCAL VOC 2012 and MS COCO 2014 datasets.
Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation
When activating class objects, we argue that the false activation stems from the bias to the ambiguous regions during the feature extraction.
STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation
shimoda-uec/ssdd
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Then, a better network called Enhanced-DCNN is learned with supervision from the predicted segmentation masks of simple images based on the Initial-DCNN as well as the image-level annotations.
Spatio-temporal video autoencoder with differentiable memory
At each time step, the system receives as input a video frame, predicts the optical flow based on the current observation and the LSTM memory state as a dense transformation map, and applies it to the current frame to generate the next frame.
Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation
arslan-chaudhry/dcsp_segmentation
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We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task.
