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Found 20 papers, 5 papers with code
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social settings (e. g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels.
Uncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-Formations From Surveillance Videos
We present a novel approach for jointly estimating tar- gets' head, body orientations and conversational groups called F-formations from a distant social scene (e. g., a cocktail party captured by surveillance cameras).
Evaluating Crowdsourcing Participants in the Absence of Ground-Truth
Given a supervised/semi-supervised learning scenario where multiple annotators are available, we consider the problem of identification of adversarial or unreliable annotators.
An EEG-based Image Annotation System
The success of deep learning in computer vision has greatly increased the need for annotated image datasets.
AVEID: Automatic Video System for Measuring Engagement In Dementia
Engagement in dementia is typically measured using behavior observational scales (BOS) that are tedious and involve intensive manual labor to annotate, and are therefore not easily scalable.
Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements
Contrary to the popular notion that ad affect hinges on the narrative and the clever use of linguistic and social cues, we find that actively attended objects and the coarse scene structure better encode affective information as compared to individual scene objects or conspicuous background elements.
Recognition of Advertisement Emotions with Application to Computational Advertising
Advertisements (ads) often contain strong affective content to capture viewer attention and convey an effective message to the audience.
Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
MDS is computed as an aggregate of dissimilarity scores between audio and visual segments in a video.
The eyes know it: FakeET -- An Eye-tracking Database to Understand Deepfake Perception
We present \textbf{FakeET}-- an eye-tracking database to understand human visual perception of \emph{deepfake} videos.
Characterizing Hirability via Personality and Behavior
Modeling hirability as a discrete/continuous variable with the \emph{big-five} personality traits as predictors, we utilize (a) apparent personality annotations, and (b) personality estimates obtained via audio, visual and textual cues for hirability prediction (HP).
ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency Prediction
We also explore a variation of ViNet architecture by augmenting audio features into the decoder.
FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios
This paper proposes a new DeepFake detector FakeBuster for detecting impostors during video conferencing and manipulated faces on social media.
Head Matters: Explainable Human-centered Trait Prediction from Head Motion Dynamics
We demonstrate the utility of elementary head-motion units termed kinemes for behavioral analytics to predict personality and interview traits.
Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals
Employing traditional machine learning and deep learning methods, we explore (a) dimensional and categorical emotion recognition, and (b) PD vs HC classification from emotional EEG signals.
Outlier-based Autism Detection using Longitudinal Structural MRI
Among other findings, metrics employed for model training as well as reconstruction loss computation impact detection performance, and the coronal modality is found to best encode structural information for ASD detection.
Explainable Human-centered Traits from Head Motion and Facial Expression Dynamics
We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits.
A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference
Whilst a majority of affective computing research focuses on inferring emotions, examining mood or understanding the \textit{mood-emotion interplay} has received significantly less attention.
Explainable Depression Detection via Head Motion Patterns
While depression has been studied via multimodal non-verbal behavioural cues, head motion behaviour has not received much attention as a biomarker.
Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning
Whilst deep learning techniques have achieved excellent emotion prediction, they still require large amounts of labelled training data, which are (a) onerous and tedious to compile, and (b) prone to errors and biases.
MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily Behavior Recognition in Group Settings
Bodily behavioral language is an important social cue, and its automated analysis helps in enhancing the understanding of artificial intelligence systems.
