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Found 9 papers, 2 papers with code
Base Layer Efficiency in Scalable Human-Machine Coding
A basic premise in scalable human-machine coding is that the base layer is intended for automated machine analysis and is therefore more compressible than the same content would be for human viewing.
Rate-Distortion Theory in Coding for Machines and its Application
To meet this growing demand, several methods have been developed for image and video coding for machines.
VVC+M: Plug and Play Scalable Image Coding for Humans and Machines
We focus on the case of images, proposing to utilize the pre-existing residual coding capabilities of video codecs such as VVC to create a scalable codec from any image compression for machines (ICM) scheme.
Conditional and Residual Methods in Scalable Coding for Humans and Machines
We present methods for conditional and residual coding in the context of scalable coding for humans and machines.
Rate-Distortion in Image Coding for Machines
In our experiments we show the trade-off between the human and machine sides of such a scalable model, and discuss the benefit of using deeper layers for training in that regard.
Does Video Compression Impact Tracking Accuracy?
Everyone "knows" that compressing a video will degrade the accuracy of object tracking.
PowerGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks
Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter.
Generative Adversarial Network
Non-Intrusive Load Monitoring
Towards Automated Swimming Analytics Using Deep Neural Networks
Methods for creating a system to automate the collection of swimming analytics on a pool-wide scale are considered in this paper.
Wavenilm: A causal neural network for power disaggregation from the complex power signal
Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement.
