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20142024
most citedDeriving reproducible biomarkers from multi-site resting-state data: An Autism-based example

742 citations · 811 across the 30 of their papers we have counts for

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23 papers · 1 filter

cs.CV2024

Assessing Sample Quality via the Latent Space of Generative Models

Jingyi Xu, Hieu Le, Dimitris Samaras

Advances in generative models increase the need for sample quality assessment. To do so, previous methods rely on a pre-trained feature extractor to embed the generated samples and…

cs.CV2024

-Brush: Controllable Large Image Synthesis with Diffusion Models in Infinite Dimensions

Minh-Quan Le, Alexandros Graikos, Srikar Yellapragada +3

Synthesizing high-resolution images from intricate, domain-specific information remains a significant challenge in generative modeling, particularly for applications in large-image…

cs.CV2024

Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following

Qiaomu Miao, Alexandros Graikos, Jingwei Zhang +3

Training gaze following models requires a large number of images with gaze target coordinates annotated by human annotators, which is a laborious and inherently ambiguous process.…

cs.CV2024

MIGS: Multi-Identity Gaussian Splatting via Tensor Decomposition

Aggelina Chatziagapi, Grigorios G. Chrysos, Dimitris Samaras

We introduce MIGS (Multi-Identity Gaussian Splatting), a novel method that learns a single neural representation for multiple identities, using only monocular videos. Recent 3D Gau…

cs.CV202420 cited

Predicting Visual Attention in Graphic Design Documents

Souradeep Chakraborty, Zijun Wei, Conor Kelton +4

We present a model for predicting visual attention during the free viewing of graphic design documents. While existing works on this topic have aimed at predicting static saliency…

cs.CV2024

Look Hear: Gaze Prediction for Speech-directed Human Attention

Sounak Mondal, Seoyoung Ahn, Zhibo Yang +4

For computer systems to effectively interact with humans using spoken language, they need to understand how the words being generated affect the users' moment-by-moment attention.…