Progressive Domain Adaptation for Object Detection

By Han-Kai Hsu, Wei-Chih Hung, Hung-Yu Tseng, Chun-Han Yao, Yi-Hsuan Tsai, Maneesh Kumar Singh, Ming-Hsuan Yang. 2019

369 citations recorded by the source corpus.

Recent deep learning methods for object detection rely on a large amount of bounding box annotations. Collecting these annotations is laborious and costly, yet supervised models do not generalize well when testing on images from a different distribution. Domain adaptation provides a solution by adapting existing labels to the target testing data. However, a large gap between domains could make adaptation a challenging task, which leads to unstable training processes and sub-optimal results. In this paper, we propose to bridge the domain gap with an intermediate domain and progressively solve easier adaptation subtasks. This intermediate domain is constructed by translating the source images to mimic the ones in the target domain. To tackle the domain-shift problem, we adopt adversarial learning to align distributions at the feature level. In addition, a weighted task loss is applied to deal with unbalanced image quality in the intermediate domain. Experimental results show that our method performs favorably against the state-of-the-art method in terms of the performance on the target domain.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Representation & Self-Supervised Learning

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Citations in the corpus

Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100 (2020, background): “The most obvious domain gaps stem from changes in locations [49], viewpoints [50], labels [51] and participants [52].”
Exploring Categorical Regularization for Domain Adaptive Object Detection (2020, background): “Recently, DA Faster R-CNN has rapidly evolved into a successful series [28, 41, 12, 14].”
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (2020, background): “…been proposed to apply adversarial training to align the source and target distributions on the feature-level (Tzeng et al., 2017; Chen et al., 2018; Hsu et al., 2020) or on the pixel-level (Tsai et al., 2018; Hoffman et al., 2018; Bous-malis et al., 2017; Chen et al., 2019b; Lee et al., 2018).”
Multiple Class Novelty Detection Under Data Distribution Shift (2020, background): “However, most of the work on this topic has been done for the task of classiﬁcation [52,49,11,14], segmentation [50,55,15], detection [16,9,19] etc. and to the best of our knowledge no work is available in the literature that addresses the distribution shift problem for novelty detection.”
Regularizing Generative Adversarial Networks under Limited Data (2021, neutral): “The GAN models are the corner-stone techniques for numerous vision applications, such as data augmentation [12, 13], domain adaptation [19, 20], image extrapolation [10, 66], image-to-image translation [22, 37, 86], and visual content creation [1, 4, 21, 23, 70, 72].”