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Found 21 papers, 12 papers with code
High-Fidelity Image Compression with Score-based Generative Models
Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult.
Multi-Realism Image Compression with a Conditional Generator
By optimizing the rate-distortion-realism trade-off, generative compression approaches produce detailed, realistic images, even at low bit rates, instead of the blurry reconstructions produced by rate-distortion optimized models.
VCT: A Video Compression Transformer
The resulting video compression transformer outperforms previous methods on standard video compression data sets.
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.
High-Fidelity Generative Image Compression
We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system.
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
While it is well known that autoregressive models come with a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and, together, exploit the probabilistic structure in the latents better than all previous learned models.
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.
Image-Dependent Local Entropy Models for Learned Image Compression
The leading approach for image compression with artificial neural networks (ANNs) is to learn a nonlinear transform and a fixed entropy model that are optimized for rate-distortion performance.
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.
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1. 58M action labels with multiple labels per person occurring frequently.
Ranked #6 on
Action Detection
on UCF101-24
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.
YouTube-8M: A Large-Scale Video Classification Benchmark
Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow.
Ranked #1 on
Action Recognition In Videos
on ActivityNet
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.
Variable Rate Image Compression with Recurrent Neural Networks
A large fraction of Internet traffic is now driven by requests from mobile devices with relatively small screens and often stringent bandwidth requirements.
Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
We present a method for learning an embedding that places images of humans in similar poses nearby.
Efficient Large Scale Video Classification
We present two methods that build on this work, and scale it up to work with millions of videos and hundreds of thousands of classes while maintaining a low computational cost.
Beyond Short Snippets: Deep Networks for Video Classification
Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval.
Ranked #5 on
Action Recognition
on Sports-1M
Large-Scale Video Classification with Convolutional Neural Networks
We further study the generalization performance of our best model by retraining the top layers on the UCF-101 Action Recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63. 3% up from 43. 9%).
Ranked #9 on
Action Recognition
on Sports-1M
