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20232026
most citedElasticDiffusion: Training-free Arbitrary Size Image Generation through Global-Local Content Separation

14 citations · 18 across the 8 of their papers we have counts for

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cs.CV2026

Generating Humanless Environment Walkthroughs from Egocentric Walking Tour Videos

Yujin Ham, Junho Kim, Vivek Boominathan +1

Egocentric "walking tour" videos provide a rich source of image data to develop rich and diverse visual models of environments around the world. However, the significant presence o…

cs.CV2026

The Surprising Effectiveness of Noise Pretraining for Implicit Neural Representations

Kushal Vyas, Alper Kayabasi, Daniel Kim +3

The approximation and convergence properties of implicit neural representations (INRs) are known to be highly sensitive to parameter initialization strategies. While several data-d…

cs.CV2026

EgoGroups: A Benchmark For Detecting Social Groups of People in the Wild

Jeffri Murrugarra-Llerena, Pranav Chitale, Zicheng Liu +4

Social group detection, or the identification of humans involved in reciprocal interpersonal interactions (e.g., family members, friends, and customers and merchants), is a crucial…

cs.CV2025

GViT: Representing Images as Gaussians for Visual Recognition

Jefferson Hernandez, Ruozhen He, Guha Balakrishnan +2

We introduce GVIT, a classification framework that abandons conventional pixel or patch grid input representations in favor of a compact set of learnable 2D Gaussians. Each image i…

cs.CV2024★ 3 cited

Fairness and Bias Mitigation in Computer Vision: A Survey

Sepehr Dehdashtian, Ruozhen He, Yi Li +4

Computer vision systems have witnessed rapid progress over the past two decades due to multiple advances in the field. As these systems are increasingly being deployed in high-stak…

cs.CV2023★ 14 cited

ElasticDiffusion: Training-free Arbitrary Size Image Generation through Global-Local Content Separation

Moayed Haji-Ali, Guha Balakrishnan, Vicente Ordonez

Diffusion models have revolutionized image generation in recent years, yet they are still limited to a few sizes and aspect ratios. We propose ElasticDiffusion, a novel training-fr…