Addressing the challenge of domain shift between datasets is vital in
maintaining model performance. In the context of cross-domain object detection,
the teacher-student framework, a widely-used semi-supervised model, has shown
significant accuracy improvements. However, existing methods often overlook
class differences, treating all classes equally, resulting in suboptimal
results. Furthermore, the integration of instance-level alignment with a
one-stage detector, essential due to the absence of a Region Proposal Network
(RPN), remains unexplored in this framework. In response to these shortcomings,
we introduce a novel teacher-student model named Versatile Teacher (VT). VT
differs from previous works by considering class-specific detection difficulty
and employing a two-step pseudo-label selection mechanism, referred to as
Class-aware Pseudo-label Adaptive Selection (CAPS), to generate more reliable
pseudo labels. These labels are leveraged as saliency matrices to guide the
discriminator for targeted instance-level alignment. Our method demonstrates
promising results on three benchmark datasets, and extends the alignment
methods for widely-used one-stage detectors, presenting significant potential
for practical applications. Code is available at
this https URL

在跨领域目标检测中，本文引入了一种名为 Versatile Teacher (VT) 的新型教师 - 学生模型，通过考虑类别特定的检测难度并采用两步赋值机制，生成更可靠的伪标签，将其作为显著性矩阵来引导区分器进行有针对性的实例级对齐，取得了有希望的实验结果，并扩展了广泛使用的一阶检测器的对齐方法，具有重要的实际应用潜力。

多功能教师：一种面向跨领域适应的课程感知教师 - 学生框架

Versatile Teacher: A Class-aware Teacher-student Framework for  Cross-domain Adaptation

Recent advances in adaptive object detection have achieved compelling results
in virtue of adversarial feature adaptation to mitigate the distributional
shifts along the detection pipeline. Whilst adversarial adaptation
significantly enhances the transferability of feature representations, the
feature discriminability of object detectors remains less investigated.
Moreover, transferability and discriminability may come at a contradiction in
adversarial adaptation given the complex combinations of objects and the
differentiated scene layouts between domains. In this paper, we propose a
Hierarchical Transferability Calibration Network (HTCN) that hierarchically
(local-region/image/instance) calibrates the transferability of feature
representations for harmonizing transferability and discriminability. The
proposed model consists of three components: (1) Importance Weighted
Adversarial Training with input Interpolation (IWAT-I), which strengthens the
global discriminability by re-weighting the interpolated image-level features;
(2) Context-aware Instance-Level Alignment (CILA) module, which enhances the
local discriminability by capturing the underlying complementary effect between
the instance-level feature and the global context information for the
instance-level feature alignment; (3) local feature masks that calibrate the
local transferability to provide semantic guidance for the following
discriminative pattern alignment. Experimental results show that HTCN
significantly outperforms the state-of-the-art methods on benchmark datasets.

本文提出了一种基于 Hierarchical Transferability Calibration Network 的自适应目标检测方法，其中采用各种技术（如输入插值、自适应训练、上下文感知实例层对齐）来提高特征可辨别性和可转移性，从而使得该方法在目标检测领域表现出色。