Looking beyond single images for contrastive semantic segmentation learning

Looking beyond single images for contrastive semantic segmentation learning

We present an approach to contrastive representation learning for semantic segmentation. Our approach leverages the representational power of existing feature extractors to find corresponding regions across images. These cross-image correspondences are used as auxiliary labels to guide the pixel-lev...

書誌詳細
主要な著者:	Zhang, F, Torr, P, Ranftl, R, Richter, S
フォーマット:	Conference item
言語:	English
出版事項:	Neural Information Processing Systems Foundation 2021

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