Search Results for author:
Found 9 papers, 7 papers with code
IEA: Inner Ensemble Average within a convolutional neural network
Ensemble learning is a method of combining multiple trained models to improve model accuracy.
Physics Informed Data Driven model for Flood Prediction: Application of Deep Learning in prediction of urban flood development
Flash floods in urban areas occur with increasing frequency.
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans.
Ranked #3 on
Trajectory Prediction
on ETH
Inner Ensemble Networks: Average Ensemble as an Effective Regularizer
Also, we show empirically and theoretically that IENs lead to a greater variance reduction in comparison with other similar approaches such as dropout and maxout.
Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs
We propose Skeleton-Graph, a deep spatio-temporal graph CNN model that predicts the future 3D skeleton poses in a single pass from the 2D ones.
Ranked #1 on
Trajectory Prediction
on PROX
Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation
AMD is a metric that quantifies how close the whole generated samples are to the ground truth.
Ranked #1 on
Trajectory Prediction
on Stanford Drone
(ADE (in world coordinates) metric)
HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data
The problem of human activity recognition from mobile sensor data applies to multiple domains, such as health monitoring, personal fitness, daily life logging, and senior care.
FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings
Yet, there is a question about the driver's ability to follow the planned lead vehicle route.
A Review of Deep Learning for Video Captioning
Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work in the fields of computer vision, natural language processing (NLP), linguistics, and human-computer interaction.
