Search Results for author:
Found 5 papers, 2 papers with code
Motion Prediction Performance Analysis for Autonomous Driving Systems and the Effects of Tracking Noise
In this work, we systematically explore the importance of the tracking module for the motion prediction task and ultimately conclude that the overall motion prediction performance is highly sensitive to the tracking module's imperfections.
A Pose Proposal and Refinement Network for Better Object Pose Estimation
Our approach is composed of 2 main components: the first component classifies the objects in the input image and proposes an initial 6D pose estimate through a multi-task, CNN-based encoder/multi-decoder module.
End-to-end Learning Improves Static Object Geo-localization in Monocular Video
We also show that the end-to-end system performance is further improved via joint-training of the constituent models.
Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction
Our end-to-end model consists of two stages: the first stage is an encoder/decoder network that learns to predict future video frames.
Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding Specificities
For this purpose, we present deepRAM, an end-to-end deep learning tool that provides an implementation of novel and previously proposed architectures; its fully automatic model selection procedure allows us to perform a fair and unbiased comparison of deep learning architectures.
Automatic Machine Learning Model Selection
Model Selection
+1
