论文标题

从域概括的外部和内在的监督中学习

Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization

论文作者

Wang, Shujun, Yu, Lequan, Li, Caizi, Fu, Chi-Wing, Heng, Pheng-Ann

论文摘要

神经网络跨领域的概括能力对于实际应用至关重要。我们认为,广义对象识别系统应该很好地理解不同图像之间的关系以及图像本身之间的关系。为此,我们提出了一个新的域概括框架,该框架学习了如何从多源域中的图像中从外部关系监督和内在的自我划分中同时跨域进行概括。具体来说,我们使用多任务学习范式嵌入功能嵌入来制定框架。除了执行共同的监督识别任务外,我们还无缝整合了一项动量公制学习任务,并进行了一项自我监管的辅助任务,以共同利用外部监督和内在的监督。此外,我们开发了一种有效的动量度量学习方案,并使用K-HARD负面开采来增强网络以捕获域概括的图像关系。我们证明了方法对两个标准对象识别基准VLC和PAC的有效性,并表明我们的方法达到了最新的性能。

The generalization capability of neural networks across domains is crucial for real-world applications. We argue that a generalized object recognition system should well understand the relationships among different images and also the images themselves at the same time. To this end, we present a new domain generalization framework that learns how to generalize across domains simultaneously from extrinsic relationship supervision and intrinsic self-supervision for images from multi-source domains. To be specific, we formulate our framework with feature embedding using a multi-task learning paradigm. Besides conducting the common supervised recognition task, we seamlessly integrate a momentum metric learning task and a self-supervised auxiliary task to collectively utilize the extrinsic supervision and intrinsic supervision. Also, we develop an effective momentum metric learning scheme with K-hard negative mining to boost the network to capture image relationship for domain generalization. We demonstrate the effectiveness of our approach on two standard object recognition benchmarks VLCS and PACS, and show that our methods achieve state-of-the-art performance.

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