Harmonizing Transferability and Discriminability for Adapting Object
Detectors
- URL: http://arxiv.org/abs/2003.06297v1
- Date: Fri, 13 Mar 2020 13:47:48 GMT
- Title: Harmonizing Transferability and Discriminability for Adapting Object
Detectors
- Authors: Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, Qi Dou
- Abstract summary: We propose a Hierarchical Transferability Network (HTCN) that calibrates the transferability of feature representations for harmonizing discriminability.
Experimental results show that HTCN significantly outperforms the state-of-the-art methods on benchmark datasets.
- Score: 48.78231850215302
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in adaptive object detection have achieved compelling results
in virtue of adversarial feature adaptation to mitigate the distributional
shifts along the detection pipeline. Whilst adversarial adaptation
significantly enhances the transferability of feature representations, the
feature discriminability of object detectors remains less investigated.
Moreover, transferability and discriminability may come at a contradiction in
adversarial adaptation given the complex combinations of objects and the
differentiated scene layouts between domains. In this paper, we propose a
Hierarchical Transferability Calibration Network (HTCN) that hierarchically
(local-region/image/instance) calibrates the transferability of feature
representations for harmonizing transferability and discriminability. The
proposed model consists of three components: (1) Importance Weighted
Adversarial Training with input Interpolation (IWAT-I), which strengthens the
global discriminability by re-weighting the interpolated image-level features;
(2) Context-aware Instance-Level Alignment (CILA) module, which enhances the
local discriminability by capturing the underlying complementary effect between
the instance-level feature and the global context information for the
instance-level feature alignment; (3) local feature masks that calibrate the
local transferability to provide semantic guidance for the following
discriminative pattern alignment. Experimental results show that HTCN
significantly outperforms the state-of-the-art methods on benchmark datasets.
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