# Diverse Feature-Level Guidance Adjustments for Unsupervised Domain Adapative Object Detection
## 1 Introduction  
Object detection aims to recognize and locate foreground objects within complex backgrounds. However, conventional detectors exhibit a noticeable performance decline when they are tested on real-world data. The root of this issue lies in the domain gap between training sets and real-world data. Conventional detectors are typically trained on labeled data, with researchers assuming that the training and validation sets follow an independent and identically distributed (i.i.d.) distribution. However, labeled samples are frequently absent when the detector is used in practical settings. Moreover, the distribution of training and validation sets may differ significantly in practical situations. This is because there are substantial differences in aspects such as illumination, shooting perspectives, and picture styles. **Unsupervised Domain Adapative Object Detection** (UDAOD) has emerged as a solution to tackle this problem, as it aims to minimize the data distribution inconsistency across different domains. Researchers have introduced numerous UDAOD methods to effectively transfer models from labeled data to unlabeled data. These methods encompass abundant adaptation strategies, including data alignment and feature alignment strategies (image-level and instance-level [1], as shown in Figure 1) combined with an adversarial training [2] technique, among others. These methods are typically based on one-stage or two-stage detectors. One-stage detectors [3; 4; 5; 6; 7] introduce image-level feature alignment with single stage detection frameworks. However, due to the absence of instance-level feature alignment, these methods suffer from incorrect awareness of foreground samples within complex backgrounds [5]. Conversely, two-stage object detection frameworks [1; 8; 9; 10; 11; 12; 13; 14; 15; 16] utilize instance-level alignment to promote foreground perception through the Region Proposal Network (RPN) [17]. While an instance-level alignment strategy significantly improves object detection accuracy, it introduces certain issues. On the one hand, it hampers the discriminator's ability to distinguish features, since the pooled proposal features from the RPN commonly lose contextual and texture details. On the other hand, while using the RPN and an instance-level alignment strategy enhances domain invariant [12] proposal features, it also results in quantities of background candidate boxes. To solve these problems, we introduce **Diverse Feature-level Guidance Adjustments** (DFGAs) for two-stage detection frameworks. To aid with the lack of context information [18] alignment, **Pixel-wise Multi-scale Alignment** (PMA) is proposed to adapt features on diverse hierarchical levels among different domains. Specifically, since global features contain comprehensive information, a novel similarity measurement loss function, termed PMA loss, is introduced to facilitate information exchange between the source and target domains. As for overly focusing on background candidates, the **Adaptative Threshold Confidence Adjustment** (ATCA) strategy is proposed as a category classifier. It encourages the model to focus on foreground information. Due to the necessity of foreground sample awareness, ATCA introduces adaptative thresholds to enhance the cross-domain alignment of foreground candidates. Specifically, it calculates the corresponding foreground-background sample division threshold based on the classifier's output. These samples are adaptively partitioned into allocation intervals (foreground, background, and ignored samples) depending on their corresponding thresholds. This approach enables  
Figure 1: Both image-level and instance-level alignment approaches are commonly used in feature alignment methods. We use “+”and “\(-\)” to represent foreground and background samples, respectively. Additionally, the input requirements for these two alignment approaches differ. In image-level alignment, the entire set of image features is utilized to capture comprehensive information.