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Found 4 papers, 3 papers with code
Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets
Medical image segmentation presents the challenge of segmenting various-size targets, demanding the model to effectively capture both local and global information.
Rethinking Multi-domain Generalization with A General Learning Objective
Multi-domain generalization (mDG) is universally aimed to minimize the discrepancy between training and testing distributions to enhance marginal-to-label distribution mapping.
Semantic-aware Data Augmentation for Text-to-image Synthesis
In particular, we propose to augment texts in the semantic space via an Implicit Textual Semantic Preserving Augmentation ($ITA$), in conjunction with a specifically designed Image Semantic Regularization Loss ($L_r$) as Generated Image Semantic Conservation, to cope well with semantic mismatch and collapse.
SSD: Towards Better Text-Image Consistency Metric in Text-to-Image Generation
Generating consistent and high-quality images from given texts is essential for visual-language understanding.
