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Found 4 papers, 2 papers with code
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management
As the model training on information from users is likely to invade personal privacy, many methods have been proposed to block the learning and memorizing of the sensitive data in raw texts.
Group-Wise Learning for Weakly Supervised Semantic Segmentation
The framework explicitly encodes semantic dependencies in a group of images to discover rich semantic context for estimating more reliable pseudo ground-truths, which are subsequently employed to train more effective segmentation models.
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation
To address this, we introduce a novel approach for more accurate and efficient spatio-temporal segmentation.
Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation
We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths, which can be used for training more accurate segmentation models.
Ranked #37 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
(using extra training data)
