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Found 11 papers, 4 papers with code
A Multi-Perspective Machine Learning Approach to Evaluate Police-Driver Interaction in Los Angeles
Interactions between the government officials and civilians affect public wellbeing and the state legitimacy that is necessary for the functioning of democratic society.
MM-AU:Towards Multimodal Understanding of Advertisement Videos
Advertisement videos (ads) play an integral part in the domain of Internet e-commerce as they amplify the reach of particular products to a broad audience or can serve as a medium to raise awareness about specific issues through concise narrative structures.
Robust Self Supervised Speech Embeddings for Child-Adult Classification in Interactions involving Children with Autism
We address the problem of detecting who spoke when in child-inclusive spoken interactions i. e., automatic child-adult speaker classification.
FedMultimodal: A Benchmark For Multimodal Federated Learning
In order to facilitate the research in multimodal FL, we introduce FedMultimodal, the first FL benchmark for multimodal learning covering five representative multimodal applications from ten commonly used datasets with a total of eight unique modalities.
Understanding Spoken Language Development of Children with ASD Using Pre-trained Speech Embeddings
This paper proposes applications of speech processing technologies in support of automated assessment of children's spoken language development by classification between child and adult speech and between speech and nonverbal vocalization in NLS, with respective F1 macro scores of 82. 6% and 67. 8%, underscoring the potential for accurate and scalable tools for ASD research and clinical use.
Contextually-rich human affect perception using multimodal scene information
The process of human affect understanding involves the ability to infer person specific emotional states from various sources including images, speech, and language.
A dataset for Audio-Visual Sound Event Detection in Movies
In this work, we present a dataset of audio events called Subtitle-Aligned Movie Sounds (SAM-S).
A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness
Speech-centric machine learning systems have revolutionized many leading domains ranging from transportation and healthcare to education and defense, profoundly changing how people live, work, and interact with each other.
MovieCLIP: Visual Scene Recognition in Movies
Longform media such as movies have complex narrative structures, with events spanning a rich variety of ambient visual scenes.
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings
To assess the information leakage of SER systems trained using FL, we propose an attribute inference attack framework that infers sensitive attribute information of the clients from shared gradients or model parameters, corresponding to the FedSGD and the FedAvg training algorithms, respectively.
Robust Character Labeling in Movie Videos: Data Resources and Self-supervised Feature Adaptation
Our work in this paper focuses on two key aspects of this problem: the lack of domain-specific training or benchmark datasets, and adapting face embeddings learned on web images to long-form content, specifically movies.
