Zobrazeno 1 - 10
of 317
pro vyhledávání: '"Hu, Guanyu"'
Autor:
Hu, Guanyu, Kollias, Dimitrios, Papadopoulou, Eleni, Tzouveli, Paraskevi, Wei, Jie, Yang, Xinyu
Evaluating affect analysis methods presents challenges due to inconsistencies in database partitioning and evaluation protocols, leading to unfair and biased results. Previous studies claim continuous performance improvements, but our findings challe
Externí odkaz:
http://arxiv.org/abs/2408.02164
Autor:
Hu, Guanyu, Wei, Jie, Song, Siyang, Kollias, Dimitrios, Yang, Xinyu, Sun, Zhonglin, Kaloidas, Odysseus
The objective of the Multiple Appropriate Facial Reaction Generation (MAFRG) task is to produce contextually appropriate and diverse listener facial behavioural responses based on the multimodal behavioural data of the conversational partner (i.e., t
Externí odkaz:
http://arxiv.org/abs/2407.15798
Autor:
Kollias, Dimitrios, Zafeiriou, Stefanos, Kotsia, Irene, Dhall, Abhinav, Ghosh, Shreya, Shao, Chunchang, Hu, Guanyu
This paper describes the 7th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with ECCV 2024. The 7th ABAW Competition addresses novel challenges in understanding human expressio
Externí odkaz:
http://arxiv.org/abs/2407.03835
The Gini coefficient is an universally used measurement of income inequality. Intersectoral GDP contributions reveal the economic development of different sectors of the national economy. Linking intersectoral GDP contributions to Gini coefficients w
Externí odkaz:
http://arxiv.org/abs/2405.07408
Autor:
Hu, Guanyu, Papadopoulou, Eleni, Kollias, Dimitrios, Tzouveli, Paraskevi, Wei, Jie, Yang, Xinyu
The increasing integration of machine learning algorithms in daily life underscores the critical need for fairness and equity in their deployment. As these technologies play a pivotal role in decision-making, addressing biases across diverse subpopul
Externí odkaz:
http://arxiv.org/abs/2405.06841
In this paper, we develop a novel depth-based testing procedure on spatial point processes to examine the difference in made and missed field goal attempts for NBA players. Specifically, our testing procedure can statistically detect the differences
Externí odkaz:
http://arxiv.org/abs/2403.17221
Autor:
Kollias, Dimitrios, Tzirakis, Panagiotis, Cowen, Alan, Zafeiriou, Stefanos, Kotsia, Irene, Baird, Alice, Gagne, Chris, Shao, Chunchang, Hu, Guanyu
This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with IEEE CVPR 2024. The 6th ABAW Competition addresses contemporary challenges in understanding hum
Externí odkaz:
http://arxiv.org/abs/2402.19344
Bayesian Nonparametric Clustering with Feature Selection for Spatially Resolved Transcriptomics Data
The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context. Nevertheless, th
Externí odkaz:
http://arxiv.org/abs/2312.08324
Functional data is a powerful tool for capturing and analyzing complex patterns and relationships in a variety of fields, allowing for more precise modeling, visualization, and decision-making. For example, in healthcare, functional data such as medi
Externí odkaz:
http://arxiv.org/abs/2304.12460
Technological advances have paved the way for collecting high-resolution network data in basketball, football, and other team-based sports. Such data consist of interactions among players of competing teams indexed by space and time. High-resolution
Externí odkaz:
http://arxiv.org/abs/2303.01318