论文标题
具有双重标签预测的自适应对象检测
Adaptive Object Detection with Dual Multi-Label Prediction
论文作者
论文摘要
在本文中，我们通过将多标签对象识别作为双重辅助任务来提出一种新颖的端到端无监督的深层域适应模型，以用于自适应对象检测。该模型利用多标签预测来揭示每个图像中的对象类别信息，然后使用预测结果来执行条件对抗性全局特征对齐，以便可以解决图像特征的多模式结构以在全局特征级别弥合域差异，同时保留特征的可区分性。此外，我们介绍了一种预测一致性正则化机制来协助对象检测，该机制将多标签预测结果用作辅助正规化信息，以确保对象识别任务与对象检测任务之间的一致对象类别发现。实验是在一些基准数据集上进行的，结果表明该模型的表现优于最新比较方法。
In this paper, we propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection by exploiting multi-label object recognition as a dual auxiliary task. The model exploits multi-label prediction to reveal the object category information in each image and then uses the prediction results to perform conditional adversarial global feature alignment, such that the multi-modal structure of image features can be tackled to bridge the domain divergence at the global feature level while preserving the discriminability of the features. Moreover, we introduce a prediction consistency regularization mechanism to assist object detection, which uses the multi-label prediction results as an auxiliary regularization information to ensure consistent object category discoveries between the object recognition task and the object detection task. Experiments are conducted on a few benchmark datasets and the results show the proposed model outperforms the state-of-the-art comparison methods.
