activity
20162024
most citedDOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

7 citations · 21 across the 14 of their papers we have counts for

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9 papers · 1 filter

cs.CV20241 cited

`Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning

Joshua Feinglass, Jayaraman J. Thiagarajan, Rushil Anirudh +2

Current approaches in Generalized Zero-Shot Learning (GZSL) are built upon base models which consider only a single class attribute vector representation over the entire image. Thi…

cs.CV2022

On-the-fly Object Detection using StyleGAN with CLIP Guidance

Yuzhe Lu, Shusen Liu, Jayaraman J. Thiagarajan +2

We present a fully automated framework for building object detectors on satellite imagery without requiring any human annotation or intervention. We achieve this by leveraging the…

cs.CV2020

Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization

Suhas Lohit, Rushil Anirudh, Pavan Turaga

Motion capture (mocap) and time-of-flight based sensing of human actions are becoming increasingly popular modalities to perform robust activity analysis. Applications range from a…

cs.CV20204 cited

Attribute-Guided Adversarial Training for Robustness to Natural Perturbations

Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura +3

While existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real-world sett…

cs.CV2020

Generative Patch Priors for Practical Compressive Image Recovery

Rushil Anirudh, Suhas Lohit, Pavan Turaga

In this paper, we propose the generative patch prior (GPP) that defines a generative prior for compressive image recovery, based on patch-manifold models. Unlike learned, image-lev…

cs.CV2019

MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking

Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura +1

In the past few years, Generative Adversarial Networks (GANs) have dramatically advanced our ability to represent and parameterize high-dimensional, non-linear image manifolds. As…