Object detectors frequently encounter significant performance degradation
when confronted with domain gaps between collected data (source domain) and
data from real-world applications (target domain). To address this task,
numerous unsupervised domain adaptive detectors have been proposed, leveraging
carefully designed feature alignment techniques. However, these techniques
primarily align instance-level features in a class-agnostic manner, overlooking
the differences between extracted features from different categories, which
results in only limited improvement. Furthermore, the scope of current
alignment modules is often restricted to a limited batch of images, failing to
learn the entire dataset-level cues, thereby severely constraining the
detector's generalization ability to the target domain. To this end, we
introduce a strong DETR-based detector named Domain Adaptive detection
TRansformer (DATR) for unsupervised domain adaptation of object detection.
Firstly, we propose the Class-wise Prototypes Alignment (CPA) module, which
effectively aligns cross-domain features in a class-aware manner by bridging
the gap between object detection task and domain adaptation task. Then, the
designed Dataset-level Alignment Scheme (DAS) explicitly guides the detector to
achieve global representation and enhance inter-class distinguishability of
instance-level features across the entire dataset, which spans both domains, by
leveraging contrastive learning. Moreover, DATR incorporates a mean-teacher
based self-training framework, utilizing pseudo-labels generated by the teacher
model to further mitigate domain bias. Extensive experimental results
demonstrate superior performance and generalization capabilities of our
proposed DATR in multiple domain adaptation scenarios. Code is released at
this https URL

我们提出了一种强大的基于 DetR 的检测器，名为 Domain Adaptive Detection Transformer (DATR)，用于无监督域自适应目标检测，通过有效地对跨域特征进行类别感知的对齐，明确引导检测器实现全局表示，并通过使用教师模型生成的伪标签来减轻领域偏差，展示了我们的 DATR 在多个域适应场景中具有卓越的性能和泛化能力。

DATR: 无监督领域自适应检测 Transformer 模型：基于数据集层面适应与原型对齐

DATR: Unsupervised Domain Adaptive Detection Transformer with  Dataset-Level Adaptation and Prototypical Alignment

Current state-of-the-art methods for image captioning employ region-based
features, as they provide object-level information that is essential to
describe the content of images; they are usually extracted by an object
detector such as Faster R-CNN. However, they have several issues, such as lack
of contextual information, the risk of inaccurate detection, and the high
computational cost. The first two could be resolved by additionally using
grid-based features. However, how to extract and fuse these two types of
features is uncharted. This paper proposes a Transformer-only neural
architecture, dubbed GRIT (Grid- and Region-based Image captioning
Transformer), that effectively utilizes the two visual features to generate
better captions. GRIT replaces the CNN-based detector employed in previous
methods with a DETR-based one, making it computationally faster. Moreover, its
monolithic design consisting only of Transformers enables end-to-end training
of the model. This innovative design and the integration of the dual visual
features bring about significant performance improvement. The experimental
results on several image captioning benchmarks show that GRIT outperforms
previous methods in inference accuracy and speed.

本文提出了一个名为 GRIT 的纯 Transformer 神经架构，用于图像字幕生成，它可以有效地利用区域和网格这两种视觉特征来生成更好的字幕。在几项图像字幕基准测试中的实验结果表明，GRIT 在推理准确性和速度方面优于先前的方法。