activity
20162020
most citedFollowing Instructions by Imagining and Reaching Visual Goals

5 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

cs.NE20201 cited

A Deep 2-Dimensional Dynamical Spiking Neuronal Network for Temporal Encoding trained with STDP

Matthew Evanusa, Cornelia Fermuller, Yiannis Aloimonos

The brain is known to be a highly complex, asynchronous dynamical system that is highly tailored to encode temporal information. However, recent deep learning approaches to not tak…

cs.LG20205 cited

Following Instructions by Imagining and Reaching Visual Goals

John Kanu, Eadom Dessalene, Xiaomin Lin +2

While traditional methods for instruction-following typically assume prior linguistic and perceptual knowledge, many recent works in reinforcement learning (RL) have proposed learn…

cs.RO2019

Computational Tactile Flow for Anthropomorphic Grippers

Kanishka Ganguly, Behzad Sadrfaridpour, Cornelia Fermüller +1

Grasping objects requires tight integration between visual and tactile feedback. However, there is an inherent difference in the scale at which both these input modalities operate.…

cs.CV2016

Fast Task-Specific Target Detection via Graph Based Constraints Representation and Checking

Went Luan, Yezhou Yang, Cornelia Fermuller +1

In this work, we present a fast target detection framework for real-world robotics applications. Considering that an intelligent agent attends to a task-specific object target duri…

cs.CV2016

Reliable Attribute-Based Object Recognition Using High Predictive Value Classifiers

Wentao Luan, Yezhou Yang, Cornelia Fermuller +1

We consider the problem of object recognition in 3D using an ensemble of attribute-based classifiers. We propose two new concepts to improve classification in practical situations,…

cs.CV20162 cited

Prediction of Manipulation Actions

Cornelia Fermüller, Fang Wang, Yezhou Yang +4

Looking at a person's hands one often can tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor's intentions shape his/her…