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Found 10 papers, 5 papers with code
GGNN: Graph-based GPU Nearest Neighbor Search
Approximate nearest neighbor (ANN) search in high dimensions is an integral part of several computer vision systems and gains importance in deep learning with explicit memory representations.
Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)
Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid.
Separating Reflection and Transmission Images in the Wild
The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms.
Learning Blind Motion Deblurring
As handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime.
Efficient Large-scale Approximate Nearest Neighbor Search on the GPU
We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization.
Backpropagation Training for Fisher Vectors within Neural Networks
Fisher-Vectors (FV) encode higher-order statistics of a set of multiple local descriptors like SIFT features.
Will People Like Your Image? Learning the Aesthetic Space
Rating how aesthetically pleasing an image appears is a highly complex matter and depends on a large number of different visual factors.
Learning Robust Video Synchronization without Annotations
Aligning video sequences is a fundamental yet still unsolved component for a broad range of applications in computer graphics and vision.
Transfer Learning for Material Classification using Convolutional Networks
Specifically, transfer learning from the task of object recognition is exploited to more effectively train good features for material classification.
End-to-End Learning for Image Burst Deblurring
We present a neural network model approach for multi-frame blind deconvolution.
