论文标题
高质量实体细分
High-Quality Entity Segmentation
论文作者
论文摘要
密集的图像分割任务，例如语义，全景）对于图像编辑很有用，但是现有方法几乎无法在有不受限制的图像域，类，图像分辨率和质量变化的内部环境中很好地概括。在这些观察结果的推动下，我们构建了一个新的实体细分数据集，强烈着重于野外高质量的密集分段。该数据集包含涵盖各种图像域和实体的图像，以及大量的高分辨率图像以及用于训练和测试的高质量掩盖注释。鉴于数据集的高质量和 - 分辨率性质，我们提出了旨在解决高分辨率图像上实例级分割的难题。它通过融合高分辨率图像作物来改善面具预测，从而提供更细粒度的图像细节和完整的图像。 CropFormer是第一个基于查询的变压器体系结构，可以通过学习有效地将整个图像及其作物的相同实体的查询来有效地融合来自多个图像视图的掩码预测。借助农作物形式，我们在具有挑战性的实体细分任务上获得了$ 1.9 $的大量AP增益。此外，农作物形成者始终提高传统分割任务和数据集的准确性。数据集和代码将在http://luqi.info/entityv2.github.io/上发布。
Dense image segmentation tasks e.g., semantic, panoptic) are useful for image editing, but existing methods can hardly generalize well in an in-the-wild setting where there are unrestricted image domains, classes, and image resolution and quality variations. Motivated by these observations, we construct a new entity segmentation dataset, with a strong focus on high-quality dense segmentation in the wild. The dataset contains images spanning diverse image domains and entities, along with plentiful high-resolution images and high-quality mask annotations for training and testing. Given the high-quality and -resolution nature of the dataset, we propose CropFormer which is designed to tackle the intractability of instance-level segmentation on high-resolution images. It improves mask prediction by fusing high-res image crops that provide more fine-grained image details and the full image. CropFormer is the first query-based Transformer architecture that can effectively fuse mask predictions from multiple image views, by learning queries that effectively associate the same entities across the full image and its crop. With CropFormer, we achieve a significant AP gain of $1.9$ on the challenging entity segmentation task. Furthermore, CropFormer consistently improves the accuracy of traditional segmentation tasks and datasets. The dataset and code will be released at http://luqi.info/entityv2.github.io/.
