most citedRobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs

1 citations · 2 across the 2 of their papers we have counts for

collaborators

11 papers

cs.CV2020

EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle

Trisha Mittal, Pooja Guhan, Uttaran Bhattacharya +3

We present EmotiCon, a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. Motivated by Frege's Context Principle from psychology…

cs.CV2020

Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues

Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra +2

We present a learning-based method for detecting real and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the…

cs.RO2020

CMetric: A Driving Behavior Measure Using Centrality Functions

Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal +2

We present a new measure, CMetric, to classify driver behaviors using centrality functions. Our formulation combines concepts from computational graph theory and social traffic psy…

cs.CV2019

The Liar's Walk: Detecting Deception with Gait and Gesture

Tanmay Randhavane, Uttaran Bhattacharya, Kyra Kapsaskis +3

We present a data-driven deep neural algorithm for detecting deceptive walking behavior using nonverbal cues like gaits and gestures. We conducted an elaborate user study, where we…

cs.RO2019

Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs

Rohan Chandra, Tianrui Guan, Srujan Panuganti +4

We present a novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning. We predict both the low-level infor…

eess.SP20191 cited

M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues

Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra +2

We present M3ER, a learning-based method for emotion recognition from multiple input modalities. Our approach combines cues from multiple co-occurring modalities (such as face, tex…