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Found 17 papers, 5 papers with code
The Need for Medically Aware Video Compression in Gastroenterology
Compression is essential to storing and transmitting medical videos, but the effect of compression on downstream medical tasks is often ignored.
LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks
We consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions.
Neural Video Compression using GANs for Detail Synthesis and Propagation
Our approach significantly outperforms previous neural and non-neural video compression methods in a user study, setting a new state-of-the-art in visual quality for neural methods.
End-to-end Learning of Compressible Features
We propose a learned method that jointly optimizes for compressibility along with the task objective for learning the features.
Computationally Efficient Neural Image Compression
Image compression using neural networks have reached or exceeded non-neural methods (such as JPEG, WebP, BPG).
Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference
We show results that are within 1. 6% of the reported, non-quantized performance on MobileNet using only 40 entries in our table.
Integer Networks for Data Compression with Latent-Variable Models
We consider the problem of using variational latent-variable models for data compression.
Neural Image Decompression: Learning to Render Better Image Previews
A rapidly increasing portion of Internet traffic is dominated by requests from mobile devices with limited- and metered-bandwidth constraints.
No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference
For successful deployment of deep neural networks on highly--resource-constrained devices (hearing aids, earbuds, wearables), we must simplify the types of operations and the memory/power resources used during inference.
Towards a Semantic Perceptual Image Metric
We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification.
Spatially adaptive image compression using a tiled deep network
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images.
Variational image compression with a scale hyperprior
We describe an end-to-end trainable model for image compression based on variational autoencoders.
Target-Quality Image Compression with Recurrent, Convolutional Neural Networks
Our methods introduce a multi-pass training method to combine the training goals of high-quality reconstructions in areas around stop-code masking as well as in highly-detailed areas.
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM.
Full Resolution Image Compression with Recurrent Neural Networks
As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.
Im2Calories: Towards an Automated Mobile Vision Food Diary
We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.
What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision
We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task.
