Search Results for author:
Found 7 papers, 5 papers with code
SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction
This paper addresses motion forecasting in multi-agent environments, pivotal for ensuring safety of autonomous vehicles.
SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving
Self-supervised learning (SSL) is an emerging technique that has been successfully employed to train convolutional neural networks (CNNs) and graph neural networks (GNNs) for more transferable, generalizable, and robust representation learning.
Ranked #59 on
Motion Forecasting
on Argoverse CVPR 2020
Visual Representation Learning with Self-Supervised Attention for Low-Label High-data Regime
For few-shot image classification we train SSL-ViTs without any supervision, on external data, and use this trained embedder to adapt quickly to novel classes with limited number of labels.
3D Scene Understanding at Urban Intersection using Stereo Vision and Digital Map
The driving behavior at urban intersections is very complex.
SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection
In this paper, we propose two variants of self-attention for contextual modeling in 3D object detection by augmenting convolutional features with self-attention features.
Ranked #1 on
3D Object Detection
on KITTI Cyclists Hard
Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations
We present Deformable PV-RCNN, a high-performing point-cloud based 3D object detector.
FANTrack: 3D Multi-Object Tracking with Feature Association Network
Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN.
Ranked #4 on
3D Multi-Object Tracking
on KITTI
