1 code implementation • 12 Nov 2023 • Zhaoyuan Yang, Zhengyang Yu, Zhiwei Xu, Jaskirat Singh, Jing Zhang, Dylan Campbell, Peter Tu, Richard Hartley
We present a diffusion-based image morphing approach with perceptually-uniform sampling (IMPUS) that produces smooth, direct and realistic interpolations given an image pair.
no code implementations • 6 Jul 2023 • Peter Tu, Zhaoyuan Yang, Richard Hartley, Zhiwei Xu, Jing Zhang, Yiwei Fu, Dylan Campbell, Jaskirat Singh, Tianyu Wang
This paper begins with a description of methods for estimating image probability density functions that reflects the observation that such data is usually constrained to lie in restricted regions of the high-dimensional image space-not every pattern of pixels is an image.
no code implementations • 1 Nov 2022 • Stephanie Stacy, Alfredo Gabaldon, John Karigiannis, James Kubrich, Peter Tu
Our approach uses analogy with past experiences to construct hypothetical rationales that explain the behavior of an observed agent.
no code implementations • 26 Oct 2022 • Zhaoyuan Yang, Zhiwei Xu, Jing Zhang, Richard Hartley, Peter Tu
In this work, we formulate a novel framework for adversarial robustness using the manifold hypothesis.
no code implementations • 26 Jul 2021 • Alberto Santamaria-Pang, Jianwei Qiu, Aritra Chowdhury, James Kubricht, Peter Tu, Iyer Naresh, Nurali Virani
Third, we generate new adversarial images by projecting back the original coefficients from the low scale and the perturbed coefficients from the high scale sub-space.
no code implementations • 22 Aug 2020 • Aritra Chowdhury, Alberto Santamaria-Pang, James R. Kubricht, Jianwei Qiu, Peter Tu
We show state of the art results for segmentation of COVID-19 lung infections in CT.
1 code implementation • 22 Aug 2020 • Aritra Chowdhury, Alberto Santamaria-Pang, James R. Kubricht, Peter Tu
In this work, we demonstrate for the first time, the emer-gence of deep symbolic representations of emergent language in the frame-work of image classification.
no code implementations • 18 Jul 2020 • Alberto Santamaria-Pang, James Kubricht, Aritra Chowdhury, Chitresh Bhushan, Peter Tu
A UNet-like architecture is used to generate input to the Sender network which produces a symbolic sentence, and a Receiver network co-generates the segmentation mask based on the sentence.
no code implementations • 18 Jul 2020 • Aritra Chowdhury, James R. Kubricht, Anup Sood, Peter Tu, Alberto Santamaria-Pang
In one form of the game, a sender and a receiver observe a set of cells from 5 different cell phenotypes.