Unsupervised discovery of character dictionaries in animation movies

Automatic content analysis of animation movies can enable an objective understanding of character (actor) representations and their portrayals. It can also help illuminate potential markers of unconscious biases and their impact. However, multimedia analysis of movie content has predominantly focused on live-action features. A dearth of multimedia research in this field is because of the complexity and heterogeneity in the design of animated characters-an extremely challenging problem to be generalized by a single method or model. In this paper, we address the problem of automatically discovering characters in animation movies as a first step toward automatic character labeling in these media. Movie-specific character dictionaries can act as a powerful first step for subsequent content analysis at scale. We propose an unsupervised approach which requires no prior information about the characters in a movie. We first use a deep neural network-based object detector that is trained on natural images to identify a set of initial character candidates. These candidates are further pruned using saliency constraints and visual object tracking. A character dictionary per movie is then generated from exemplars obtained by clustering these candidates. We are able to identify both anthropomorphic and nonanthropomorphic characters in a dataset of 46 animation movies with varying composition and character design. Our results indicate high precision and recall of the automatically detected characters compared to human-annotated ground truth, demonstrating the generalizability of our approach

A multimodal mixture-of-experts model for dynamic emotion prediction in movies

This paper addresses the problem of continuous emotion prediction in movies from multimodal cues. The rich emotion content in movies is inherently multimodal, where emotion is evoked through both audio (music, speech) and video modalities. To capture such affective information, we put forth a set of audio and video features that includes several novel features such as, Video Compressibility and Histogram of Facial Area (HFA). We propose a Mixture of Experts (MoE)-based fusion model that dynamically combines information from the audio and video modalities for predicting the emotion evoked in movies. A learning module, based on hard Expectation-Maximization (EM) algorithm, is presented for the MoE model. Experiments on a database of popular movies demonstrate that our MoE-based fusion method outperforms popular fusion strategies (e.g. early and late fusion) in the context of dynamic emotion prediction

A computational study of expressive facial dynamics in children with autism

Several studies have established that facial expressions of children with autism are often perceived as atypical, awkward or less engaging by typical adult observers. Despite this clear deficit in the quality of facial expression production, very little is understood about its underlying mechanisms and characteristics. This paper takes a computational approach to studying details of facial expressions of children with high functioning autism (HFA). The objective is to uncover those characteristics of facial expressions, notably distinct from those in typically developing children, and which are otherwise difficult to detect by visual inspection. We use motion capture data obtained from subjects with HFA and typically developing subjects while they produced various facial expressions. This data is analyzed to investigate how the overall and local facial dynamics of children with HFA differ from their typically developing peers. Our major observations include reduced complexity in the dynamic facial behavior of the HFA group arising primarily from the eye region

GPT-FL: Generative Pre-trained Model-Assisted Federated Learning

In this work, we propose GPT-FL, a generative pre-trained model-assisted federated learning (FL) framework. At its core, GPT-FL leverages generative pre-trained models to generate diversified synthetic data. These generated data are used to train a downstream model on the server, which is then fine-tuned with private client data under the standard FL framework. We show that GPT-FL consistently outperforms state-of-the-art FL methods in terms of model test accuracy, communication efficiency, and client sampling efficiency. Through comprehensive ablation analysis, we discover that the downstream model generated by synthetic data plays a crucial role in controlling the direction of gradient diversity during FL training, which enhances convergence speed and contributes to the notable accuracy boost observed with GPT-FL. Also, regardless of whether the target data falls within or outside the domain of the pre-trained generative model, GPT-FL consistently achieves significant performance gains, surpassing the results obtained by models trained solely with FL or synthetic data

Motion-capture patterns of dynamic facial expressions in children and adolescents with and without ASD

Research shows that neurotypical individuals struggle to interpret the emotional facial expressions of people with Autism Spectrum Disorder (ASD). The current study uses motion-capture to objectively quantify differences between the movement patterns of emotional facial expressions of individuals with and without ASD. Participants volitionally mimicked emotional expressions while wearing facial markers. Recorded marker movement was grouped by expression valence and intensity. We used Growth Curve Analysis to test whether movement patterns were predictable by expression type and participant group. Results show significant interactions between expression type and group, and little effect of emotion valence on ASD expressions. Together, results support perceptions that expressions of individuals with ASD are different from -- and more ambiguous than -- those of neurotypical individuals’

A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images

Real-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open dataset providing raw multi-coil RT-MRI data from an optimized speech production experimental setup. Such datasets could enable new and improved methods for dynamic image reconstruction, artifact correction, feature extraction, and direct extraction of linguistically-relevant biomarkers. The present dataset offers a unique corpus of 2D sagittal-view RT-MRI videos along with synchronized audio for 75 subjects performing linguistically motivated speech tasks, alongside the corresponding first-ever public domain raw RT-MRI data. The dataset also includes 3D volumetric vocal tract MRI during sustained speech sounds and high-resolution static anatomical T2-weighted upper airway MRI for each subject.Comment: 27 pages, 6 figures, 5 tables, submitted to Nature Scientific Dat