Unsupervised domain adaptive object detection aims to learn a robust detector on the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different. In this paper, we propose a gradient detach based Stacked Complementary Losses (SCL) method that uses detection objective (cross entropy and smooth l1 regression) as the primary objective, and cuts in several auxiliary losses in different network stages to utilize information from the complement data (target images) that can be effective in adapting model parameters to both source and target domains. A gradient detach operation is applied between detection and context sub-networks during training to force networks to learn discriminative representations. We argue that the conventional training with primary objective mainly leverages the information from the source-domain for maximizing likelihood and ignores the complement data in shallow layers of networks, which leads to an insufficient integration within different domains. Thus, our proposed method is a more syncretic adaptation learning process. We conduct comprehensive experiments on seven datasets, the results demonstrate that our method performs favorably better than the state-of-the-art methods by a large margin. For instance, from Cityscapes to FoggyCityscapes, we achieve 37.9% mAP, outperforming the previous art Strong-Weak by 3.6%. In real world scenarios, generic object detection always faces severe challenges from variations in viewpoint, background, object appearance, illumination, occlusion conditions, scene change, etc. These unavoidable factors make object detection in domain-shift circumstance becoming a challenging and new rising research topic in the recent years. Also, domain change is a widely-recognized, intractable problem that urgently needs to break through in reality of detection tasks, like video surveillance, autonomous driving, etc. (see Figure 2 ). Revisiting Domain-Shift Object Detection. Common approaches for tackling domain-shift object detection are mainly in two directions: (i) training supervised model then fine-tuning on the target domain; or (ii) unsupervised cross-domain representation learning. The former requires additional instance-level annotations on target data, which is fairly laborious, expensive and time-consuming. So most approaches focus on the latter one but still have some challenges. The first challenge is that the representations of source and target domain data should be embedded into a common space for matching the object, such as the hidden feature space (Saito et al., 2019; Chen et al., 2018) , input space Cai et al., 2019) or both of them (Kim et al., 2019b) . The second is that a feature alignment/matching operation or mechanism for source/target domains should be further defined, such as subspace alignment (Raj et al., 2015) , H-divergence and adversarial learning (Chen et al., 2018) , MRL (Kim et al., 2019b) , Strong-Weak alignment (Saito et al., 2019) , etc. In general, our SCL is also a learning-based alignment method across domains with an end-to-end framework. (a) Non-adapted (b) CVPR'18 (Chen et al., 2018) (c) CVPR'19 (Saito et al., 2019) (d) SCL (Ours) (e) Non-adapted (f) CVPR'18 (Chen et al., 2018) (g) CVPR'19 (Saito et al., 2019) (h) SCL (Ours) Figure 1: Visualization of features from PASCAL to Clipart (first row) and from Cityscapes to FoggyCityscapes (second row) by t-SNE (Maaten & Hinton, 2008) . Red indicates the source examples and blue is the target one. If source and target features locate in the same position, it is shown as light blue. All models are re-trained with a unified setting to ensure fair comparisons. It can be observed that our feature embedding results are consistently much better than previous approaches on either dissimilar domains (PASCAL and Clipart) or similar domains (Cityscapes and FoggyCityscapes). Our Key Ideas. The goal of this paper is to introduce a simple design that is specific to convolutional neural network optimization and improves its training on tasks that adapt on discrepant domains. Unsupervised domain adaptation for recognition has been widely studied by a large body of previous literature (Ganin et al., 2016; Long et al., 2016; Tzeng et al., 2017; Panareda Busto & Gall, 2017; Hoffman et al., 2018; Murez et al., 2018; Zhao et al., 2019; Wu et al., 2019) , our method more or less draws merits from them, like aligning source and target distributions with adversarial learning (domain-invariant alignment). However, object detection is a technically different problem from classification, since we would like to focus more on the object of interests (local regions). In this paper, we have addressed unsupervised domain adaptive object detection through stacked complementary losses. One of our key contributions is gradient detach training, enabled by suppressing gradients flowing back to the detection backbone. In addition, we proposed to use multiple complementary losses for better optimization. We conduct extensive experiments and ablation studies to verify the effectiveness of each component that we proposed. Our experimental results outperform the state-of-the-art approaches by a large margin on a variety of benchmarks. Our future work will focus on exploring the domain-shift detection from scratch, i.e., without the pre-trained models like DSOD (Shen et al., 2017) , to avoid involving bias from the pre-trained dataset.
We introduce a new gradient detach based complementary objective training strategy for domain adaptive object detection.
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