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Found 6 papers, 4 papers with code
Any-Width Networks
To address these constraints, we propose the Any-Width Network (AWN), an adjustable-width CNN architecture and associated training routine that allow for fine-grained control over speed and accuracy during inference.
Revisiting the Threat Space for Vision-based Keystroke Inference Attacks
This indicates that these models are able to learn rich, meaningful representations from our synthetic data and that training on the synthetic data can help overcome the issue of having small, real-life datasets for vision-based key stroke inference attacks.
Mapped Convolutions
We present a versatile formulation of the convolution operation that we term a "mapped convolution."
Deep Blending for Free-Viewpoint Image-Based-Rendering
We present a new deep learning approach to blending for IBR, in which we use held-out real image data to learn blending weights to combine input photo contributions.
Augmenting Crowd-Sourced 3D Reconstructions Using Semantic Detections
Image-based 3D reconstruction for Internet photo collections has become a robust technology to produce impressive virtual representations of real-world scenes.
Rolling Shutter and Radial Distortion Are Features for High Frame Rate Multi-Camera Tracking
In this paper, we introduce a novel multi-camera tracking approach that for the first time jointly leverages the information introduced by rolling shutter and radial distortion as a feature to achieve superior performance with respect to high-frequency camera pose estimation.
