Unified Representation Learning for Cross Model Compatibility
- URL: http://arxiv.org/abs/2008.04821v1
- Date: Tue, 11 Aug 2020 16:14:53 GMT
- Title: Unified Representation Learning for Cross Model Compatibility
- Authors: Chien-Yi Wang, Ya-Liang Chang, Shang-Ta Yang, Dong Chen, Shang-Hong
Lai
- Abstract summary: We propose a unified representation learning framework to address the Cross Model Compatibility problem in the context of visual search applications.
Cross compatibility between different embedding models enables the visual search systems to correctly recognize and retrieve identities without re-encoding user images.
- Score: 19.808287296481208
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a unified representation learning framework to address the Cross
Model Compatibility (CMC) problem in the context of visual search applications.
Cross compatibility between different embedding models enables the visual
search systems to correctly recognize and retrieve identities without
re-encoding user images, which are usually not available due to privacy
concerns. While there are existing approaches to address CMC in face
identification, they fail to work in a more challenging setting where the
distributions of embedding models shift drastically. The proposed solution
improves CMC performance by introducing a light-weight Residual Bottleneck
Transformation (RBT) module and a new training scheme to optimize the embedding
spaces. Extensive experiments demonstrate that our proposed solution
outperforms previous approaches by a large margin for various challenging
visual search scenarios of face recognition and person re-identification.
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