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Found 10 papers, 1 papers with code
Low-Latency Neural Stereo Streaming
The rise of new video modalities like virtual reality or autonomous driving has increased the demand for efficient multi-view video compression methods, both in terms of rate-distortion (R-D) performance and in terms of delay and runtime.
Bitstream Organization for Parallel Entropy Coding on Neural Network-based Video Codecs
Video compression systems must support increasing bandwidth and data throughput at low cost and power, and can be limited by entropy coding bottlenecks.
MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device
This work presents the first neural video codec that decodes 1080p YUV420 video in real time on a mobile device.
Optimized learned entropy coding parameters for practical neural-based image and video compression
Neural-based image and video codecs are significantly more power-efficient when weights and activations are quantized to low-precision integers.
Boosting neural video codecs by exploiting hierarchical redundancy
In video compression, coding efficiency is improved by reusing pixels from previously decoded frames via motion and residual compensation.
MobileCodec: Neural Inter-frame Video Compression on Mobile Devices
Realizing the potential of neural video codecs on mobile devices is a big technological challenge due to the computational complexity of deep networks and the power-constrained mobile hardware.
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
We show that an adversary who can poison a training dataset can cause models trained on this dataset to leak significant private details of training points belonging to other parties.
Deep Homography Estimation for Dynamic Scenes
We then develop a multi-scale neural network and show that when properly trained using our new dataset, this neural network can already handle dynamic scenes to some extent.
Interactive Boundary Prediction for Object Selection
Instead of relying on pre-defined low-level image features, our method adaptively predicts object boundaries according to image content and user interactions.
Scaling Properties of Human Brain Functional Networks
We test for the power-law, exponential, log-normal and generalized Pareto distributions.
