6 papers
Contextual Interference Reduction by Selective Fine-Tuning of Neural Networks
Mahdi Biparva, John Tsotsos
Feature disentanglement of the foreground target objects and the background surrounding context has not been yet fully accomplished. The lack of network interpretability prevents a…
Two-Stream Networks for Lane-Change Prediction of Surrounding Vehicles
David Fernández-Llorca, Mahdi Biparva, Rubén Izquierdo-Gonzalo +1
In highway scenarios, an alert human driver will typically anticipate early cut-in and cut-out maneuvers of surrounding vehicles using only visual cues. An automated system must an…
Compact Neural Representation Using Attentive Network Pruning
Mahdi Biparva, John Tsotsos
Deep neural networks have evolved to become power demanding and consequently difficult to apply to small-size mobile platforms. Network parameter reduction methods have been introd…
Selective Segmentation Networks Using Top-Down Attention
Mahdi Biparva, John Tsotsos
Convolutional neural networks model the transformation of the input sensory data at the bottom of a network hierarchy to the semantic information at the top of the visual hierarchy…
Priming Neural Networks
Amir Rosenfeld, Mahdi Biparva, John K. Tsotsos
Visual priming is known to affect the human visual system to allow detection of scene elements, even those that may have been near unnoticeable before, such as the presence of camo…
STNet: Selective Tuning of Convolutional Networks for Object Localization
Mahdi Biparva, John Tsotsos
Visual attention modeling has recently gained momentum in developing visual hierarchies provided by Convolutional Neural Networks. Despite recent successes of feedforward processin…