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Found 10 papers, 0 papers with code
Visual Semantic Parsing: From Images to Abstract Meaning Representation
The success of scene graphs for visual scene understanding has brought attention to the benefits of abstracting a visual input (e. g., image) into a structured representation, where entities (people and objects) are nodes connected by edges specifying their relations.
SAViR-T: Spatially Attentive Visual Reasoning with Transformers
We present a novel computational model, "SAViR-T", for the family of visual reasoning problems embodied in the Raven's Progressive Matrices (RPM).
DAReN: A Collaborative Approach Towards Reasoning And Disentangling
Computational learning approaches to solving visual reasoning tests, such as Raven's Progressive Matrices (RPM), critically depend on the ability to identify the visual concepts used in the test (i. e., the representation) as well as the latent rules based on those concepts (i. e., the reasoning).
Adversarial Problems for Generative Networks
We are interested in the design of generative networks.
Image De-Quantization Using Generative Models as Priors
Image quantization is used in several applications aiming in reducing the number of available colors in an image and therefore its size.
Image Restoration from Parametric Transformations using Generative Models
When images are statistically described by a generative model we can use this information to develop optimum techniques for various image restoration problems as inpainting, super-resolution, image coloring, generative model inversion, etc.
Training Neural Networks for Likelihood/Density Ratio Estimation
In classical approaches the two densities are assumed known or to belong to some known parametric family.
Optimizing Shallow Networks for Binary Classification
Data driven classification that relies on neural networks is based on optimization criteria that involve some form of distance between the output of the network and the desired label.
Kernel-Based Training of Generative Networks
Generative adversarial networks (GANs) are designed with the help of min-max optimization problems that are solved with stochastic gradient-type algorithms which are known to be non-robust.
