Multi-dataset training provides a viable solution for exploiting
heterogeneous large-scale datasets without extra annotation cost. In this work,
we propose a scalable multi-dataset detector (ScaleDet) that can scale up its
generalization across datasets when increasing the number of training datasets.
Unlike existing multi-dataset learners that mostly rely on manual relabelling
efforts or sophisticated optimizations to unify labels across datasets, we
introduce a simple yet scalable formulation to derive a unified semantic label
space for multi-dataset training. ScaleDet is trained by visual-textual
alignment to learn the label assignment with label semantic similarities across
datasets. Once trained, ScaleDet can generalize well on any given upstream and
downstream datasets with seen and unseen classes. We conduct extensive
experiments using LVIS, COCO, Objects365, OpenImages as upstream datasets, and
13 datasets from Object Detection in the Wild (ODinW) as downstream datasets.
Our results show that ScaleDet achieves compelling strong model performance
with an mAP of 50.7 on LVIS, 58.8 on COCO, 46.8 on Objects365, 76.2 on
OpenImages, and 71.8 on ODinW, surpassing state-of-the-art detectors with the
same backbone.

本文提出了一种可扩展的多数据集检测器 (ScaleDet)，使用语义标签相似性通过视觉 - 文字对齐训练来学习标签分配，从而在多个数据集上实现了强大的模型性能，超越了相同骨干网络的最新检测器。

ScaleDet: 一种可扩展的多数据集对象检测器

ScaleDet: A Scalable Multi-Dataset Object Detector

In spite of the compelling achievements that deep neural networks (DNNs) have
made in medical image computing, these deep models often suffer from degraded
performance when being applied to new test datasets with domain shift. In this
paper, we present a novel unsupervised domain adaptation approach for
segmentation tasks by designing semantic-aware generative adversarial networks
(GANs). Specifically, we transform the test image into the appearance of source
domain, with the semantic structural information being well preserved, which is
achieved by imposing a nested adversarial learning in semantic label space. In
this way, the segmentation DNN learned from the source domain is able to be
directly generalized to the transformed test image, eliminating the need of
training a new model for every new target dataset. Our domain adaptation
procedure is unsupervised, without using any target domain labels. The
adversarial learning of our network is guided by a GAN loss for mapping data
distributions, a cycle-consistency loss for retaining pixel-level content, and
a semantic-aware loss for enhancing structural information. We validated our
method on two different chest X-ray public datasets for left/right lung
segmentation. Experimental results show that the segmentation performance of
our unsupervised approach is highly competitive with the upper bound of
supervised transfer learning.

通过设计语义感知的生成对抗网络，将测试图像转换成源域的外观，保留语义结构信息，并实现语义标签空间内的嵌套对抗学习。通过生成对抗网络映射数据分布、循环一致性保留像素级内容以及语义感知增强结构信息，实现对两个不同的胸部 X 光公共数据集进行左 / 右肺分割的无监督领域自适应，创造性地解决了深度学习在 domain shift 上性能下降的问题，并与有监督的迁移学习性能相媲美。