Better image segmentation of details by using multiple image crops with CropFormer

Better image segmentation of details by using multiple image crops with CropFormer

Fine-Grained Entity Segmentation

arXiv paper abstract https://arxiv.org/abs/2211.05776

arXiv PDF paper https://arxiv.org/pdf/2211.05776.pdf

GitHub https://github.com/dvlab-research/Entity

Project page http://luqi.info/entityv2.github.io

In dense image segmentation tasks (e.g., semantic, panoptic), existing methods can hardly generalize well to unseen image domains, predefined classes, and image resolution & quality variations.

... construct a large-scale entity segmentation dataset to explore fine-grained entity segmentation, with a strong focus on open-world and high-quality dense segmentation.

The dataset contains images spanning diverse image domains and resolutions, along with high-quality mask annotations for training and testing.

... propose CropFormer for high-quality segmentation, which can improve mask prediction using high-res image crops that provide more fine-grained image details than the full image.

CropFormer is the first query-based Transformer architecture that can effectively ensemble mask predictions from multiple image crops, by learning queries that can associate the same entities across the full image and its crop.

... achieve a significant AP gain of 1.9 on the challenging fine-grained entity segmentation task ...

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