Adaptive Object Detection with Dual Multi-Label Prediction
- URL: http://arxiv.org/abs/2003.12943v2
- Date: Tue, 11 Aug 2020 00:25:27 GMT
- Title: Adaptive Object Detection with Dual Multi-Label Prediction
- Authors: Zhen Zhao, Yuhong Guo, Haifeng Shen, Jieping Ye
- Abstract summary: We propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection.
The model exploits multi-label prediction to reveal the object category information in each image.
We introduce a prediction consistency regularization mechanism to assist object detection.
- Score: 78.69064917947624
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose a novel end-to-end unsupervised deep domain
adaptation model for adaptive object detection by exploiting multi-label object
recognition as a dual auxiliary task. The model exploits multi-label prediction
to reveal the object category information in each image and then uses the
prediction results to perform conditional adversarial global feature alignment,
such that the multi-modal structure of image features can be tackled to bridge
the domain divergence at the global feature level while preserving the
discriminability of the features. Moreover, we introduce a prediction
consistency regularization mechanism to assist object detection, which uses the
multi-label prediction results as an auxiliary regularization information to
ensure consistent object category discoveries between the object recognition
task and the object detection task. Experiments are conducted on a few
benchmark datasets and the results show the proposed model outperforms the
state-of-the-art comparison methods.
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