Search Results for author:
Found 7 papers, 5 papers with code
Better Monocular 3D Detectors with LiDAR from the Past
Accurate 3D object detection is crucial to autonomous driving.
Denoising Vision Transformers
Our two-stage approach, termed Denoising Vision Transformers (DVT), does not require re-training existing pre-trained ViTs and is immediately applicable to any Transformer-based architecture.
Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps
We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Encoder Representations from transFormers, to leverage priors in SD maps for the lane-topology prediction task.
Learning Iterative Neural Optimizers for Image Steganography
Image steganography is the process of concealing secret information in images through imperceptible changes.
Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs
In this paper, we investigate the idea of turning sensor inputs (i. e., images) captured in an adverse condition into a benign one (i. e., sunny), upon which the downstream tasks (e. g., semantic segmentation) can attain high accuracy.
Fixed Neural Network Steganography: Train the images, not the network
Recent attempts at image steganography make use of advances in deep learning to train an encoder-decoder network pair to hide and retrieve secret messages in images.
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection
Self-driving cars must detect other vehicles and pedestrians in 3D to plan safe routes and avoid collisions.
