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20142023
most citedTraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions

233 citations · 2.4k across the 215 of their papers we have counts for

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Showing 2019Show all

38 papers · 1 filter

cs.CV2019★ 11 cited

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★ 3 cited

Reinforcement Learning-based Visual Navigation with Information-Theoretic Regularization

Qiaoyun Wu, Kai Xu, Jun Wang +3

To enhance the cross-target and cross-scene generalization of target-driven visual navigation based on deep reinforcement learning (RL), we introduce an information-theoretic regul…

cs.RO2019★ 1 cited

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.SP2019★ 1 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…

cs.CV2019★ 44 cited

Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping

Uttaran Bhattacharya, Christian Roncal, Trisha Mittal +5

We present an autoencoder-based semi-supervised approach to classify perceived human emotions from walking styles obtained from videos or motion-captured data and represented as se…

cs.SD2019★ 38 cited

Scene-Aware Audio Rendering via Deep Acoustic Analysis

Zhenyu Tang, Nicholas J. Bryan, Dingzeyu Li +2

We present a new method to capture the acoustic characteristics of real-world rooms using commodity devices, and use the captured characteristics to generate similar sounding sourc…