Search Results for author:
Found 7 papers, 2 papers with code
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks
This problem is compounded by the presence of social interactions between humans and their physical interactions with the scene.
Ranked #17 on
Trajectory Prediction
on ETH/UCY
SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks
Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people.
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints
Whereas, the social attention component aggregates information across the different agent interactions and extracts the most important trajectory information from the surrounding neighbors.
Ranked #4 on
Trajectory Prediction
on Stanford Drone
(ADE (8/12) @K=5 metric)
Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks
We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded.
Real-Time Localization and Tracking of Multiple Radio-Tagged Animals with an Autonomous UAV
Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods.
Systems and Control Robotics
Joint Learning of Set Cardinality and State Distribution
We present a novel approach for learning to predict sets using deep learning.
DeepSetNet: Predicting Sets with Deep Neural Networks
This paper addresses the task of set prediction using deep learning.
