Search Results for author:
Found 16 papers, 10 papers with code
METEOR:A Dense, Heterogeneous, and Unstructured Traffic Dataset With Rare Behaviors
We present a new traffic dataset, METEOR, which captures traffic patterns and multi-agent driving behaviors in unstructured scenarios.
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales based on multi-scale feature pyramids.
Ranked #1 on
3D Object Detection
on KITTI Cars Hard val
GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments
We interface GANav with a deep reinforcement learning-based navigation algorithm and highlight its benefits in terms of navigation in real-world unstructured terrains.
Ranked #1 on
Semantic Segmentation
on RELLIS-3D Dataset
SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments
We present a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or fog.
B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving
We address the problem of ego-vehicle navigation in dense simulated traffic environments populated by road agents with varying driver behaviors.
Robotics
BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments
We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments.
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".
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 #1 on
Emotion Recognition in Context
on EMOTIC
Emotion Recognition in Context
Multimodal Emotion Recognition
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).
RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs
RobustTP is an approach that first computes trajectories using a combination of a non-linear motion model and a deep learning-based instance segmentation algorithm.
Robotics
RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments
We present a realtime tracking algorithm, RoadTrack, to track heterogeneous road-agents in dense traffic videos.
Robotics
TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions
We evaluate the performance of our prediction algorithm, TraPHic, on the standard datasets and also introduce a new dense, heterogeneous traffic dataset corresponding to urban Asian videos and agent trajectories.
Ranked #1 on
Trajectory Prediction
on NGSIM
Robotics
Texture Synthesis with Recurrent Variational Auto-Encoder
A novel loss function, FLTBNK, is used for training the texture synthesizer.
