32 citations · 74 across the 6 of their papers we have counts for
6 papers
OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discovery
Matthew Inkawhich, Nathan Inkawhich, Hao Yang +3
An object detector's ability to detect and flag \textit{novel} objects during open-world deployments is critical for many real-world applications. Unfortunately, much of the work i…
Tunable Hybrid Proposal Networks for the Open World
Matthew Inkawhich, Nathan Inkawhich, Hai Li +1
Current state-of-the-art object proposal networks are trained with a closed-world assumption, meaning they learn to only detect objects of the training classes. These models fail t…
The Untapped Potential of Off-the-Shelf Convolutional Neural Networks
Matthew Inkawhich, Nathan Inkawhich, Eric Davis +2
Over recent years, a myriad of novel convolutional network architectures have been developed to advance state-of-the-art performance on challenging recognition tasks. As computatio…
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
Nathan Inkawhich, Kevin J Liang, Binghui Wang +3
We consider the blackbox transfer-based targeted adversarial attack threat model in the realm of deep neural network (DNN) image classifiers. Rather than focusing on crossing decis…
Snooping Attacks on Deep Reinforcement Learning
Matthew Inkawhich, Yiran Chen, Hai Li
Adversarial attacks have exposed a significant security vulnerability in state-of-the-art machine learning models. Among these models include deep reinforcement learning agents. Th…
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers
Nathan Inkawhich, Matthew Inkawhich, Yiran Chen +1
The success of deep learning research has catapulted deep models into production systems that our society is becoming increasingly dependent on, especially in the image and video d…