Abstract
A domain adaptive object detector aims to adapt itself to unseen domains that may contain variations of object appearance, viewpoints or backgrounds. Most existing methods adopt feature alignment either on the image level or instance level. However, image-level alignment on global features may tangle foreground/background pixels at the same time, while instance-level alignment using proposals may suffer from the background noise. Different from existing solutions, we propose a domain adaptation framework that accounts for each pixel via predicting pixel-wise objectness and centerness. Specifically, the proposed method carries out center-aware alignment by paying more attention to foreground pixels, hence achieving better adaptation across domains. We demonstrate our method on numerous adaptation settings with extensive experimental results and show favorable performance against existing state-of-the-art algorithms. Source codes and models are available at https://github.com/chengchunhsu/EveryPixelMatters.
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Notes
- 1.
In this paper, we use image-level alignment and global alignment interchangeably.
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Acknowledgment
This work was supported in part by the Ministry of Science and Technology (MOST) under grants MOST 107-2628-E-009-007-MY3, MOST 109-2634-F-007-013, and MOST 109-2221-E-009-113-MY3, and by Qualcomm through a Taiwan University Research Collaboration Project. M.-H. Yang is supported in part by NSF CAREER Grant 1149783.
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Hsu, CC., Tsai, YH., Lin, YY., Yang, MH. (2020). Every Pixel Matters: Center-Aware Feature Alignment for Domain Adaptive Object Detector. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12354. Springer, Cham. https://doi.org/10.1007/978-3-030-58545-7_42
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