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Found 2 papers, 1 papers with code
Learning to Detour: Shortcut Mitigating Augmentation for Weakly Supervised Semantic Segmentation
In this paper, we propose shortcut mitigating augmentation (SMA) for WSSS, which generates synthetic representations of object-background combinations not seen in the training data to reduce the use of shortcut features.
GPTs Are Multilingual Annotators for Sequence Generation Tasks
However, the conventional approach of data annotation through crowdsourcing is both time-consuming and expensive.
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