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Found 13 papers, 4 papers with code
Naturalistic Head Motion Generation from Speech
Synthesizing natural head motion to accompany speech for an embodied conversational agent is necessary for providing a rich interactive experience.
Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis
To this end, we present VideoSham, a dataset consisting of 826 videos (413 real and 413 manipulated).
3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos
We present 3MASSIV, a multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from short-video social media platform - Moj.
Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality
We use an LSTM-based learning model for emotion perception.
Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification
We present a new approach, that we call AdaGTCN, for identifying human reader intent from Electroencephalogram~(EEG) and Eye movement~(EM) data in order to help differentiate between normal reading and task-oriented reading.
MCQA: Multimodal Co-attention Based Network for Question Answering
We present MCQA, a learning-based algorithm for multimodal question answering.
EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle
We report an AP of 65. 83 across 4 categories on GroupWalk, which is also an improvement over prior methods.
Ranked #2 on
Emotion Recognition in Context
on CAER
Emotion Recognition in Context
Multimodal Emotion Recognition
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues
Additionally, we extract and compare affective cues corresponding to perceived emotion from the two modalities within a video to infer whether the input video is "real" or "fake".
Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs
In practice, our approach reduces the average prediction error by more than 54% over prior algorithms and achieves a weighted average accuracy of 91. 2% for behavior prediction.
Ranked #1 on
Trajectory Prediction
on ApolloScape
Robotics
Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping
For the annotated data, we also train a classifier to map the latent embeddings to emotion labels.
M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues
Our approach combines cues from multiple co-occurring modalities (such as face, text, and speech) and also is more robust than other methods to sensor noise in any of the individual modalities.
STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits
We use hundreds of annotated real-world gait videos and augment them with thousands of annotated synthetic gaits generated using a novel generative network called STEP-Gen, built on an ST-GCN based Conditional Variational Autoencoder (CVAE).
Game of Sketches: Deep Recurrent Models of Pictionary-style Word Guessing
Similarly, performance on multi-disciplinary tasks such as Visual Question Answering (VQA) is considered a marker for gauging progress in Computer Vision.
