research
∙
11/25/2021
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
A classical problem in computer vision is to infer a 3D scene representa...
share
research
∙
08/19/2021
Do Vision Transformers See Like Convolutional Neural Networks?
Convolutional neural networks (CNNs) have so far been the de-facto model...
research
∙
05/04/2021
MLP-Mixer: An all-MLP Architecture for Vision
Convolutional Neural Networks (CNNs) are the go-to model for computer vi...
research
∙
04/07/2021
Differentiable Patch Selection for Image Recognition
Neural Networks require large amounts of memory and compute to process h...
research
∙
10/22/2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
While the Transformer architecture has become the de-facto standard for ...
research
∙
08/05/2020
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
We present a learning-based method for synthesizing novel views of compl...
research
∙
03/02/2020
Learning Depth via Interaction
Motivated by the astonishing capabilities of natural intelligent agents ...
research
∙
05/21/2019
Deep Drone Racing: From Simulation to Reality with Domain Randomization
Dynamically changing environments, unreliable state estimation, and oper...
research
∙
01/30/2019
Benchmarking Classic and Learned Navigation in Complex 3D Environments
Navigation research is attracting renewed interest with the advent of le...
research
∙
01/24/2019
Learning agile and dynamic motor skills for legged robots
Legged robots pose one of the greatest challenges in robotics. Dynamic a...
research
∙
01/10/2019
Motion Perception in Reinforcement Learning with Dynamic Objects
In dynamic environments, learned controllers are supposed to take motion...
research
∙
10/22/2018
Unsupervised Learning of Shape and Pose with Differentiable Point Clouds
We address the problem of learning accurate 3D shape and camera pose fro...
research
∙
10/15/2018
Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing
Autonomous micro aerial vehicles still struggle with fast and agile mane...
research
∙
09/13/2018
On Offline Evaluation of Vision-based Driving Models
Autonomous driving models should ideally be evaluated by deploying them ...
research
∙
09/12/2018
Frequency-Aware Model Predictive Control
Transferring solutions found by trajectory optimization to robotic hardw...
research
∙
07/18/2018
On Evaluation of Embodied Navigation Agents
Skillful mobile operation in three-dimensional environments is a primary...
research
∙
06/22/2018
Deep Drone Racing: Learning Agile Flight in Dynamic Environments
Autonomous agile flight brings up fundamental challenges in robotics, su...
research
∙
06/04/2018
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
Our understanding of reinforcement learning (RL) has been shaped by theo...
research
∙
04/25/2018
Driving Policy Transfer via Modularity and Abstraction
End-to-end approaches to autonomous driving have high sample complexity ...
research
∙
03/01/2018
Semi-parametric Topological Memory for Navigation
We introduce a new memory architecture for navigation in previously unse...
research
∙
01/19/2018
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
The finding that very large networks can be trained efficiently and reli...
research
∙
12/11/2017
MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
We present MINOS, a simulator designed to support the development of mul...
research
∙
11/10/2017
CARLA: An Open Urban Driving Simulator
We introduce CARLA, an open-source simulator for autonomous driving rese...
research
∙
10/06/2017
End-to-end Driving via Conditional Imitation Learning
Deep networks trained on demonstrations of human driving have learned to...
research
∙
08/13/2017
Artistic style transfer for videos and spherical images
Manually re-drawing an image in a certain artistic style takes a profess...
research
∙
03/28/2017
Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs
We present a deep convolutional decoder architecture that can generate v...
research
∙
12/07/2016
DeMoN: Depth and Motion Network for Learning Monocular Stereo
In this paper we formulate structure from motion as a learning problem. ...
research
∙
12/06/2016
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
The FlowNet demonstrated that optical flow estimation can be cast as a l...
research
∙
11/30/2016
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Generating high-resolution, photo-realistic images has been a long-stand...
research
∙
11/06/2016
Learning to Act by Predicting the Future
We present an approach to sensorimotor control in immersive environments...
research
∙
05/30/2016
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Deep neural networks (DNNs) have demonstrated state-of-the-art results o...
research
∙
02/08/2016
Generating Images with Perceptual Similarity Metrics based on Deep Networks
Image-generating machine learning models are typically trained with loss...
research
∙
12/07/2015
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Recent work has shown that optical flow estimation can be formulated as ...
research
∙
06/09/2015
Inverting Visual Representations with Convolutional Networks
Feature representations, both hand-designed and learned ones, are often ...
research
∙
04/26/2015
FlowNet: Learning Optical Flow with Convolutional Networks
Convolutional neural networks (CNNs) have recently been very successful ...
research
∙
12/21/2014
Striving for Simplicity: The All Convolutional Net
Most modern convolutional neural networks (CNNs) used for object recogni...
research
∙
11/21/2014
Learning to Generate Chairs, Tables and Cars with Convolutional Networks
We train generative 'up-convolutional' neural networks which are able to...
research
∙
06/26/2014
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Deep convolutional networks have proven to be very successful in learnin...
research
∙
05/22/2014
Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT
Latest results indicate that features learned via convolutional neural n...
research
∙
12/18/2013