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Found 8 papers, 1 papers with code
Scalable Human-Machine Point Cloud Compression
In this paper, we present a scalable codec for point-cloud data that is specialized for the machine task of classification, while also providing a mechanism for human viewing.
Learned Point Cloud Compression for Classification
Our codec demonstrates the potential of specialized codecs for machine analysis of point clouds, and provides a basis for extension to more complex tasks and datasets in the future.
Mobile-Cloud Inference for Collaborative Intelligence
Partial inference is performed on the mobile in order to reduce the dimensionality of the input data and arrive at a compact feature tensor, which is a latent space representation of the input signal.
Learned Disentangled Latent Representations for Scalable Image Coding for Humans and Machines
As an increasing amount of image and video content will be analyzed by machines, there is demand for a new codec paradigm that is capable of compressing visual input primarily for the purpose of computer vision inference, while secondarily supporting input reconstruction.
Frequency-aware Learned Image Compression for Quality Scalability
Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches.
Joint Image Compression and Denoising via Latent-Space Scalability
In this paper, we present a learning-based image compression framework where image denoising and compression are performed jointly.
Analysis of Latent-Space Motion for Collaborative Intelligence
When the input to a deep neural network (DNN) is a video signal, a sequence of feature tensors is produced at the intermediate layers of the model.
Shared Mobile-Cloud Inference for Collaborative Intelligence
Partial inference is performed on the mobile in order to reduce the dimensionality of the input data and arrive at a compact feature tensor, which is a latent space representation of the input signal.
