Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing

1 code implementation • 8 Oct 2019 • Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos

The core of our framework comprises a spatial feature learning module, which utilizes residual-graph convolutional neural networks (RG-CNN), for end-to-end learning of appearance-based features directly from graphs.

Action Recognition

Graph-Based Object Classification for Neuromorphic Vision Sensing

1 code implementation • ICCV 2019 • Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos

Neuromorphic vision sensing (NVS)\ devices represent visual information as sequences of asynchronous discrete events (a. k. a., ``spikes'') in response to changes in scene reflectance.

Classification General Classification +1

Deep Video Precoding

no code implementations • 2 Aug 2019 • Eirina Bourtsoulatze, Aaron Chadha, Ilya Fadeev, Vasileios Giotsas, Yiannis Andreopoulos

We propose to use deep neural networks as precoders for current and future video codecs and adaptive video streaming systems.

Open-Ended Question Answering

Deep Joint Source-Channel Coding for Wireless Image Transmission

1 code implementation • 4 Sep 2018 • Eirina Bourtsoulatze, David Burth Kurka, Deniz Gunduz

We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols.