3 citations · 5 across the 7 of their papers we have counts for
7 papers
Fine-tuning a multimodal large language model for clinician-grade autism behavioral scoring from short home videos
Mohammadmahdi Honarmand, Parnian Azizian, Aaron Kline +9
Autism spectrum disorder (ASD) affects 1 in 31 US children, yet median age at diagnosis exceeds four years. Artificial intelligence pipelines that provide quantified diagnosis usin…
FIESTA: Fisher Information-based Efficient Selective Test-time Adaptation
Mohammadmahdi Honarmand, Onur Cezmi Mutlu, Parnian Azizian +2
Robust facial expression recognition in unconstrained, "in-the-wild" environments remains challenging due to significant domain shifts between training and testing distributions. T…
Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder
Marie Huynh, Aaron Kline, Saimourya Surabhi +6
Early detection of autism, a neurodevelopmental disorder marked by social communication challenges, is crucial for timely intervention. Recent advancements have utilized naturalist…
Advancing Human Action Recognition with Foundation Models trained on Unlabeled Public Videos
Yang Qian, Yinan Sun, Ali Kargarandehkordi +7
The increasing variety and quantity of tagged multimedia content on a variety of online platforms offer a unique opportunity to advance the field of human action recognition. In th…
TempT: Temporal consistency for Test-time adaptation
Onur Cezmi Mutlu, Mohammadmahdi Honarmand, Saimourya Surabhi +1
We introduce Temporal consistency for Test-time adaptation (TempT) a novel method for test-time adaptation on videos through the use of temporal coherence of predictions across seq…
Computer Vision Estimation of Emotion Reaction Intensity in the Wild
Yang Qian, Ali Kargarandehkordi, Onur Cezmi Mutlu +4
Emotions play an essential role in human communication. Developing computer vision models for automatic recognition of emotion expression can aid in a variety of domains, including…
Mitigating Negative Transfer in Multi-Task Learning with Exponential Moving Average Loss Weighting Strategies
Anish Lakkapragada, Essam Sleiman, Saimourya Surabhi +1
Multi-Task Learning (MTL) is a growing subject of interest in deep learning, due to its ability to train models more efficiently on multiple tasks compared to using a group of conv…