157 citations · 166 across the 5 of their papers we have counts for
7 papers
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing
Yin Bi, Aaron Chadha, Alhabib Abbas +2
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.…
Graph-Based Object Classification for Neuromorphic Vision Sensing
Yin Bi, Aaron Chadha, Alhabib Abbas +2
Neuromorphic vision sensing (NVS)\ devices represent visual information as sequences of asynchronous discrete events (a.k.a., ``spikes'') in response to changes in scene reflectanc…
Deep Video Precoding
Eirina Bourtsoulatze, Aaron Chadha, Ilya Fadeev +2
Several groups are currently investigating how deep learning may advance the state-of-the-art in image and video coding. An open question is how to make deep neural networks work i…
Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
Mohammad Jubran, Alhabib Abbas, Aaron Chadha +1
Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutiona…
Improved Techniques for Adversarial Discriminative Domain Adaptation
Aaron Chadha, Yiannis Andreopoulos
Adversarial discriminative domain adaptation (ADDA) is an efficient framework for unsupervised domain adaptation in image classification, where the source and target domains are as…
Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor
Aaron Chadha, Alhabib Abbas, Yiannis Andreopoulos
We investigate video classification via a two-stream convolutional neural network (CNN) design that directly ingests information extracted from compressed video bitstreams. Our app…