Adapting Object Detectors with Conditional Domain Normalization

By Peng Su, Kun Wang, Xingyu Zeng, Shixiang Tang, Dapeng Chen, Xiaogang Wang. 2020

100 citations recorded by the source corpus.

Real-world object detectors are often challenged by the domain gaps between different datasets. In this work, we present the Conditional Domain Normalization (CDN) to bridge the domain gap. CDN is designed to encode different domain inputs into a shared latent space, where the features from different domains carry the same domain attribute. To achieve this, we first disentangle the domain-specific attribute out of the semantic features from one domain via a domain embedding module, which learns a domain-vector to characterize the corresponding domain attribute information. Then this domain-vector is used to encode the features from another domain through a conditional normalization, resulting in different domains' features carrying the same domain attribute. We incorporate CDN into various convolution stages of an object detector to adaptively address the domain shifts of different level's representation. In contrast to existing adaptation works that conduct domain confusion learning on semantic features to remove domain-specific factors, CDN aligns different domain distributions by modulating the semantic features of one domain conditioned on the learned domain-vector of another domain. Extensive experiments show that CDN outperforms existing methods remarkably on both real-to-real and synthetic-to-real adaptation benchmarks, including 2D image detection and 3D point cloud detection.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Layerwise Optimization by Gradient Decomposition for Continual Learning (2021, positive): “Layerwise gradient update strategy is also applied in meta-learning for rapid loss convergence on transfer learning [13, 14, 48] and few-shot learning [49, 28].”
Unbiased Mean Teacher for Cross-domain Object Detection (2020, methodology): “Besides the baselines compared previously, we further include DA-Faster [6], PF [40], SCDA [56], MAF [17], WD [52], MTOR [1], iFAN [57] and CDN [44] for comparison.”
Gradient Regularized Contrastive Learning for Continual Domain Adaptation (2020, background): “Existing DA methods (Ganin et al. 2016; Long et al. 2017; Tzeng et al. 2017; Saito et al. 2018; Su et al. 2020; Peng et al. 2019b) cannot retrain such discriminative ability on source features because they adopt multitask learning, i.”
Cross-Domain Adaptive Teacher for Object Detection (2021, methodology): “Researchers have proposed various methods, such as domain classifier and adversarial learning [10], to address the task of cross-domain adaptation in object detection [2, 3, 14, 32, 39, 42, 44].”