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20222024
most citedGaitFormer: Learning Gait Representations with Noisy Multi-Task Learning

20 citations · 25 across the 12 of their papers we have counts for

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9 papers · 1 filter

cs.CV2024

Aligning Actions and Walking to LLM-Generated Textual Descriptions

Radu Chivereanu, Adrian Cosma, Andy Catruna +2

Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains, including data augmentation and synthetic data generation. This work explores the use of…

cs.CV2024

Gait Recognition from Highly Compressed Videos

Andrei Niculae, Andy Catruna, Adrian Cosma +2

Surveillance footage represents a valuable resource and opportunities for conducting gait analysis. However, the typical low quality and high noise levels in such footage can sever…

cs.CV2024

The Paradox of Motion: Evidence for Spurious Correlations in Skeleton-based Gait Recognition Models

Andy Cătrună, Adrian Cosma, Emilian Rădoi

Gait, an unobtrusive biometric, is valued for its capability to identify individuals at a distance, across external outfits and environmental conditions. This study challenges the…

cs.CV20241 cited

CrossGaze: A Strong Method for 3D Gaze Estimation in the Wild

Andy Cătrună, Adrian Cosma, Emilian Rădoi

Gaze estimation, the task of predicting where an individual is looking, is a critical task with direct applications in areas such as human-computer interaction and virtual reality.…

cs.CV20243 cited

Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cues

David Gimeno-Gómez, Ana-Maria Bucur, Adrian Cosma +2

Depression, a prominent contributor to global disability, affects a substantial portion of the population. Efforts to detect depression from social media texts have been prevalent,…

cs.CV202320 cited

GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning

Adrian Cosma, Emilian Radoi

Gait analysis is proven to be a reliable way to perform person identification without relying on subject cooperation. Walking is a biometric that does not significantly change in s…