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Naive Bayes Super-Resolution Forest
TLDR
A bimodal tree for clustering, which successfully exploits the antipodal invariance of the coarse-to-high-res mapping of natural image patches and provides scalability to finer partitions of the underlying coarse patch space, is presented. Expand
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
TLDR
RoboTHOR offers a framework of simulated environments paired with physical counterparts to systematically explore and overcome the challenges of simulation-to-real transfer, and a platform where researchers across the globe can remotely test their embodied models in the physical world. Expand
Non-Parametric Blur Map Regression for Depth of Field Extension
TLDR
This paper presents a blind deblurring pipeline able to restore real camera systems by slightly extending their DOF and recovering sharpness in regions slightly out of focus by relying first on the estimation of the spatially varying defocus blur. Expand
PSyCo: Manifold Span Reduction for Super Resolution
TLDR
A novel regression-based SR algorithm that benefits from an extended knowledge of the structure of both manifolds, and proposes a transform that collapses the 16 variations induced from the dihedral group of transforms and antipodality into a single primitive. Expand
Image-based multi-view scene analysis using 'conexels'
Multi-camera environments allow constructing volumetric models of the scene to improve the analysis performance of computer vision algorithms (e.g. disambiguating occlusion). When representingExpand
AllenAct: A Framework for Embodied AI Research
TLDR
AllenAct is introduced, a modular and flexible learning framework designed with a focus on the unique requirements of Embodied AI research that provides first-class support for a growing collection of embodied environments, tasks and algorithms. Expand
Half hypersphere confinement for piecewise linear regression
TLDR
The usage of antipodally invariant metrics are proposed and introduced and the Half Hypersphere Confinement (HHC), a fast alternative to Multidimensional Scaling (MDS) that allows to map antipodal invariant distances in the Euclidean space with very little approximation error is introduced. Expand
Fast Super-Resolution via Dense Local Training and Inverse Regressor Search
TLDR
This paper proposes a novel inverse-search approach for regression-based Super-Resolution by applying spherical hashing to both image and regressors, which reduces the inverse search into computing a trained function. Expand
Robust single-image super-resolution using cross-scale self-similarity
TLDR
A noise-aware single-image super-resolution algorithm, which automatically cancels additive noise while adding detail learned from lower-resolution scales, and adapts the recent and efficient in-place cross-scale self-similarity prior for both learning fine detail examples and reducing image noise. Expand
Robust super-resolution for interactive video navigation
TLDR
The presented method tackles the problem of generating super-resolved versions of input video frames, thus allowing the user to visualize the captured visual contents at any desired scale with minimal degradation, with the advantages of real-time processing and robustness against spatial aliasing. Expand
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