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Jui-Ting Chien

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AAAI Conference 2019 Conference Paper

Unsupervised Meta-Learning of Figure-Ground Segmentation via Imitating Visual Effects

Ding-Jie Chen
Jui-Ting Chien
Hwann-Tzong Chen
Tyng-Luh Liu

This paper presents a “learning to learn” approach to figureground image segmentation. By exploring webly-abundant images of specific visual effects, our method can effectively learn the visual-effect internal representations in an unsupervised manner and uses this knowledge to differentiate the figure from the ground in an image. Specifically, we formulate the meta-learning process as a compositional image editing task that learns to imitate a certain visual effect and derive the corresponding internal representation. Such a generative process can help instantiate the underlying figure-ground notion and enables the system to accomplish the intended image segmentation. Whereas existing generative methods are mostly tailored to image synthesis or style transfer, our approach offers a flexible learning mechanism to model a general concept of figure-ground segmentation from unorganized images that have no explicit pixel-level annotations. We validate our approach via extensive experiments on six datasets to demonstrate that the proposed model can be end-to-end trained without ground-truth pixel labeling yet outperforms the existing methods of unsupervised segmentation tasks.

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AAAI Conference 2018 Conference Paper

Tap and Shoot Segmentation

Ding-Jie Chen
Jui-Ting Chien
Hwann-Tzong Chen
Long-Wen Chang

We present a new segmentation method that leverages latent photographic information available at the moment of taking pictures. Photography on a portable device is often done by tapping to focus before shooting the picture. This tap-andshoot interaction for photography not only speciﬁes the region of interest but also yields useful focus/defocus cues for image segmentation. However, most of the previous interactive segmentation methods address the problem of image segmentation in a post-processing scenario without considering the action of taking pictures. We propose a learning-based approach to this new tap-and-shoot scenario of interactive segmentation. The experimental results on various datasets show that, by training a deep convolutional network to integrate the selection and focus/defocus cues, our method can achieve higher segmentation accuracy in comparison with existing interactive segmentation methods.

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