Search Results for author:
Found 8 papers, 5 papers with code
Robust Re-Identification by Multiple Views Knowledge Distillation
To achieve robustness in Re-Identification, standard methods leverage tracking information in a Video-To-Video fashion.
Ranked #2 on
Vehicle Re-Identification
on VeRi
Warp and Learn: Novel Views Generation for Vehicles and Other Objects
An Image Completion Network (ICN) is then trained to generate a realistic image starting from this geometric guidance.
SimNet: Learning Reactive Self-driving Simulations from Real-world Observations
We train our system directly from 1, 000 hours of driving logs and measure both realism, reactivity of the simulation as the two key properties of the simulation.
One Thousand and One Hours: Self-driving Motion Prediction Dataset
Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1, 000 hours of data.
Future Urban Scenes Generation Through Vehicles Synthesis
In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene.
Multi-views Embedding for Cattle Re-identification
People re-identification task has seen enormous improvements in the latest years, mainly due to the development of better image features extraction from deep Convolutional Neural Networks (CNN) and the availability of large datasets.
Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients
In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations.
DriverGym: Democratising Reinforcement Learning for Autonomous Driving
Despite promising progress in reinforcement learning (RL), developing algorithms for autonomous driving (AD) remains challenging: one of the critical issues being the absence of an open-source platform capable of training and effectively validating the RL policies on real-world data.
