activity
20172023
most citedTraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions

233 citations · 450 across the 31 of their papers we have counts for

collaborators
Showing 2020Show all

8 papers · 1 filter

cs.CV2020

SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments

Divya Kothandaraman, Rohan Chandra, Dinesh Manocha

We present a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or fog. This includes a new algorithm for source-free domain adaptation (S…

cs.RO2020★ 7 cited

StylePredict: Machine Theory of Mind for Human Driver Behavior From Trajectories

Rohan Chandra, Aniket Bera, Dinesh Manocha

Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic environment composed of human drivers and do not adapt to local conditions and socio-cultural n…

cs.RO2020★ 2 cited

B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving

Angelos Mavrogiannis, Rohan Chandra, Dinesh Manocha

We address the problem of ego-vehicle navigation in dense simulated traffic environments populated by road agents with varying driver behaviors. Navigation in such environments is…

cs.CV2020

BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments

Divya Kothandaraman, Rohan Chandra, Dinesh Manocha

We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments. Our method is designed for unstructured real-world scenarios wit…

cs.CV2020★ 2 cited

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★ 13 cited

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…