The content discusses a pipeline for editing visual attributes of real images while preserving original segmentation labels. A benchmark is constructed to evaluate segmentation models' robustness to different attribute variations. Results show vulnerability to object attribute changes and the importance of considering local attributes for improved robustness. The quality of edited images is assessed through comparisons with existing benchmarks and image editing methods.
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