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StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks
This paper proposes Stacked Generative Adversarial Networks (StackGAN) to generate 256 photo-realistic images conditioned on text descriptions and introduces a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold.
Self-Attention Generative Adversarial Networks
The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset.
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.
A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI
A novel region-based method for image segmentation, which is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction).
Realistic Animation of Liquids
This approach unifies existing computer graphics techniques for simulating fluids and extends them by incorporating more complex behavior based on the Navier–Stokes equations which couple momentum and mass conservation to completely describe fluid motion.
Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit.
We present the detailed planning and execution of the Insight Toolkit (ITK), an application programmers interface (API) for the segmentation and registration of medical image data. This public
Learning with structured sparsity
This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By
Query Specific Fusion for Image Retrieval
A graph-based query specific fusion approach where multiple retrieval sets are merged and reranked by conducting a link analysis on a fused graph is proposed, capable of adaptively integrating the strengths of the retrieval methods using local or holistic features for different queries without any supervision.
Semantic Graph Convolutional Networks for 3D Human Pose Regression
The proposed Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data that learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph.
Handling Noise in Single Image Deblurring Using Directional Filters
This work proposes a new method for handling noise in blind image deconvolution based on new theoretical and practical insights, and observes that applying a directional low-pass filter to the input image greatly reduces the noise level, while preserving the blur information in the orthogonal direction to the filter.