Search Results for author:
Found 5 papers, 3 papers with code
Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information
We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario).
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.
Neural Distributed Image Compression with Cross-Attention Feature Alignment
In the proposed method, the decoder employs a cross-attention module to align the feature maps obtained from the received latent representation of the input image and a latent representation of the side information.
Neural Distributed Image Compression using Common Information
The received latent representation and the locally generated common information are passed through a decoder network to obtain an enhanced reconstruction of the input image.
Deep Joint Transmission-Recognition for Multi-View Cameras
We propose joint transmission-recognition schemes for efficient inference at the wireless edge.
