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Award ID contains: 1812850

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  1. ; ; ; (, IEEE Conference on Computer Vision and Pattern Recognition (CVPR))
    null (Ed.)
    Accepted Manuscript Full Text Available
  2. ; ; ; (, IEEE International Conference on Robotics and Automation (ICRA))
    Accepted Manuscript Full Text Available
  3. ; ; ; (, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
    We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation. We build upon FineGAN, an unconditional generative model, to learn the desired disentanglement and image generator, and leverage adversarial joint image-code distribution matching to learn the latent factor encoders. MixNMatch requires bounding boxes during training to model background, but requires no other supervision. Through extensive experiments, we demonstrate MixNMatch's ability to accurately disentangle, encode, and combine multiple factors for mix-and-match image generation, including sketch2color, cartoon2img, and img2gif applications. Our code/models/demo can be found at https://github.com/Yuheng-Li/MixNMatch 
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    Full Text Available 
  4. ; ; ; (, Advances in neural information processing systems)
    We propose a novel unsupervised generative model that learns to disentangle object identity from other low-level aspects in class-imbalanced data. We first investigate the issues surrounding the assumptions about uniformity made by InfoGAN [10], and demonstrate its ineffectiveness to properly disentangle object identity in imbalanced data. Our key idea is to make the discovery of the discrete latent factor of variation invariant to identity-preserving transformations in real images, and use that as a signal to learn the appropriate latent distribution representing object identity. Experiments on both artificial (MNIST, 3D cars, 3D chairs, ShapeNet) and real-world (YouTube-Faces) imbalanced datasets demonstrate the effectiveness of our method in disentangling object identity as a latent factor of variation. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; (, IEEE Transactions on Pattern Analysis and Machine Intelligence)
    null (Ed.)
    Full Text Available