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Found 14 papers, 3 papers with code
An in-depth look at Euclidean disk embeddings for structure preserving parsing
We address this gap for two structure generation tasks, namely dependency and semantic parsing.
Probabilistic Directed Distance Fields for Ray-Based Shape Representations
One fundamental operation applied to such representations is differentiable rendering, as it enables inverse graphics approaches in learning frameworks.
Efficient Flow-Guided Multi-frame De-fencing
Taking photographs ''in-the-wild'' is often hindered by fence obstructions that stand between the camera user and the scene of interest, and which are hard or impossible to avoid.
Uncertainty-based Cross-Modal Retrieval with Probabilistic Representations
Probabilistic embeddings have proven useful for capturing polysemous word meanings, as well as ambiguity in image matching.
Approximate Policy Iteration with Bisimulation Metrics
Based on these results, we design an API($\alpha$) procedure that employs conservative policy updates and enjoys better performance bounds than the naive API approach.
Representing 3D Shapes with Probabilistic Directed Distance Fields
On the other hand, implicit representations (occupancy, distance, or radiance fields) preserve greater fidelity, but suffer from complex or inefficient rendering processes, limiting scalability.
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks
Under such a class of discriminator (or critic) functions, we present Gradient Normalization (GraN), a novel input-dependent normalization method, which guarantees a piecewise K-Lipschitz constraint in the input space.
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning
We introduce an RL-based framework for learning to accomplish tasks in mobile apps.
Dependency parsing with structure preserving embeddings
We do so by casting dependency parsing as a tree embedding problem where we incorporate geometric properties of dependency trees in the form of training losses within a graph-based parser.
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models
In this work, we improve on a prior generative model of geometric disentanglement for 3D shapes, wherein the space of object geometry is factorized into rigid orientation, non-rigid pose, and intrinsic shape.
Probabilistic Multimodal Representation Learning
Learning multimodal representations is a requirement for many tasks such as image--caption retrieval.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Geometric Disentanglement for Generative Latent Shape Models
Representing 3D shape is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics.
Scene Categorization from Contours: Medial Axis Based Salience Measures
That is, the medial axis based salience weights appear to add useful information that is not available when CNNs are trained to use contours alone.
