Xuanhong Chen, Ziang Liu, Ting Qiu, Bingbing Ni, Naiyuan Liu, Xiwei Hu, Yuhan Li

[Project Website] [Paper]

Prevailing image-translation frameworks mostly seek to process images via the end-to-end style, which has achieved convincing results. Nonetheless, these methods lack interpretability and are not scalable on different image-translation tasks (e.g., style transfer, HDR, etc.). In this paper, we propose an interpretable knowledge-based image-translation framework, which realizes the image-translation through knowledge retrieval and transfer. In details, the framework constructs a plug-and-play and model-agnostic general purpose knowledge library, remembering task-specific styles, tones, texture patterns, etc. Furthermore, we present a fast ANN searching approach, Bandpass Hierarchical K-Means (BHKM), to cope with the difficulty of searching in the enormous knowledge library. Extensive experiments well demonstrate the effectiveness and feasibility of our framework in different image-translation tasks. In particular, backtracking experiments verify the interpretability of our method.

If you find this paper useful in your research, please consider citing:

@misc{chen2020image,
      title={Image Translation via Fine-grained Knowledge Transfer}, 
      author={Xuanhong Chen and Ziang Liu and Ting Qiu and Bingbing Ni and Naiyuan Liu and Xiwei Hu and Yuhan Li},
      year={2020},
      eprint={2012.11193},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Please contact Ziang Liu([email protected]), Xuanhong Chen([email protected]), Ting Qiu([email protected]) for questions about the details.

Learn about our other projects [RainNet], [Sketch Generation].