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20162021
most citedBiased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations

15 citations · 15 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.CV2019

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.…

cs.CV2019

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…

cs.CV2018

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…

cs.CV2018

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…

cs.CV2017

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…