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About this paper
Zhiqiang Shen, Mingyang Huang, Jianping Shi, Xiangyang Xue, Thomas Huang
Instance-aware Image-to-Image Translation (INIT) introduces an approach that leverages both global scene styles and individual object instance styles to address inconsistencies in complex scene transformations. This method generates more realistic, diverse, and higher-quality synthetic images, demonstrating improved performance for object detection and instance segmentation on the COCO dataset.
Key insights
- Integrating both global and instance-level stylistic control is crucial for generating realistic and coherent images, effectively mitigating inconsistencies caused by global-style-only approaches in complex scenes.
- The proposed cross-granularity cycle consistency loss effectively integrates instance-level details into the global translation, ensuring harmonious outputs without requiring instance annotations during inference.
- Synthetic data generated by an instance-aware I2I model can serve as an effective data augmentation technique, leading to improved performance in downstream computer vision tasks like object detection.
Summary by alphaXiv.
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