5 citations · 8 across the 10 of their papers we have counts for
12 papers
How Good is Automatic Segmentation as a Multimodal Discourse Annotation Aid?
Corbyn Terpstra, Ibrahim Khebour, Mariah Bradford +3
Collaborative problem solving (CPS) in teams is tightly coupled with the creation of shared meaning between participants in a situated, collaborative task. In this work, we assess…
Hamming Similarity and Graph Laplacians for Class Partitioning and Adversarial Image Detection
Huma Jamil, Yajing Liu, Turgay Caglar +4
Researchers typically investigate neural network representations by examining activation outputs for one or more layers of a network. Here, we investigate the potential for ReLU ac…
A Computer Vision Method for Estimating Velocity from Jumps
Soumyadip Roy, Chaitanya Roygaga, Nathaniel Blanchard +1
Athletes routinely undergo fitness evaluations to evaluate their training progress. Typically, these evaluations require a trained professional who utilizes specialized equipment l…
Dual Graphs of Polyhedral Decompositions for the Detection of Adversarial Attacks
Huma Jamil, Yajing Liu, Christina M. Cole +4
Previous work has shown that a neural network with the rectified linear unit (ReLU) activation function leads to a convex polyhedral decomposition of the input space. These decompo…
Canonical Face Embeddings
David McNeely-White, Ben Sattelberg, Nathaniel Blanchard +1
We present evidence that many common convolutional neural networks (CNNs) trained for face verification learn functions that are nearly equivalent under rotation. More specifically…
Motion Prediction Performance Analysis for Autonomous Driving Systems and the Effects of Tracking Noise
Ameni Trabelsi, Ross J. Beveridge, Nathaniel Blanchard
Autonomous driving consists of a multitude of interacting modules, where each module must contend with errors from the others. Typically, the motion prediction module depends upon…
Exploring the Interchangeability of CNN Embedding Spaces
David McNeely-White, Benjamin Sattelberg, Nathaniel Blanchard +1
CNN feature spaces can be linearly mapped and consequently are often interchangeable. This equivalence holds across variations in architectures, training datasets, and network task…
A Pose Proposal and Refinement Network for Better Object Pose Estimation
Ameni Trabelsi, Mohamed Chaabane, Nathaniel Blanchard +1
In this paper, we present a novel, end-to-end 6D object pose estimation method that operates on RGB inputs. Our approach is composed of 2 main components: the first component class…
Utilizing Network Properties to Detect Erroneous Inputs
Matt Gorbett, Nathaniel Blanchard
Neural networks are vulnerable to a wide range of erroneous inputs such as adversarial, corrupted, out-of-distribution, and misclassified examples. In this work, we train a linear…
Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction
Mohamed Chaabane, Ameni Trabelsi, Nathaniel Blanchard +1
In this paper, we present an end-to-end future-prediction model that focuses on pedestrian safety. Specifically, our model uses previous video frames, recorded from the perspective…
Getting the subtext without the text: Scalable multimodal sentiment classification from visual and acoustic modalities
Nathaniel Blanchard, Daniel Moreira, Aparna Bharati +1
In the last decade, video blogs (vlogs) have become an extremely popular method through which people express sentiment. The ubiquitousness of these videos has increased the importa…
A Neurobiological Evaluation Metric for Neural Network Model Search
Nathaniel Blanchard, Jeffery Kinnison, Brandon RichardWebster +2
Neuroscience theory posits that the brain's visual system coarsely identifies broad object categories via neural activation patterns, with similar objects producing similar neural…