Skip to search formSkip to main contentSkip to account menu

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.
View PDF on arXiv (opens in a new tab)

CARLA: An Open Urban Driving Simulator

This work introduces CARLA, an open-source simulator for autonomous driving research, and uses it to study the performance of three approaches to autonomous driving: a classic modular pipeline, an end-to-end model trained via imitation learning, and an end to-end models trained via reinforcement learning.
View PDF on arXiv (opens in a new tab)

FlowNet: Learning Optical Flow with Convolutional Networks

This paper constructs CNNs which are capable of solving the optical flow estimation problem as a supervised learning task, and proposes and compares two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations.
View on IEEE (opens in a new tab)

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

This paper proposes three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks and presents a convolutional network for real-time disparity estimation that provides state-of-the-art results.
View on IEEE (opens in a new tab)

Striving for Simplicity: The All Convolutional Net

It is found that max-pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks.
View PDF on arXiv (opens in a new tab)

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

The concept of end-to-end learning of optical flow is advanced and it work really well, and faster variants that allow optical flow computation at up to 140fps with accuracy matching the original FlowNet are presented.
View on IEEE (opens in a new tab)

MLP-Mixer: An all-MLP Architecture for Vision

It is shown that while convolutions and attention are both sufficient for good performance, neither of them are necessary, and MLP-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs), attains competitive scores on image classification benchmarks.
View PDF on arXiv (opens in a new tab)

On Evaluation of Embodied Navigation Agents

The present document summarizes the consensus recommendations of a working group to study empirical methodology in navigation research and discusses different problem statements and the role of generalization, present evaluation measures, and provides standard scenarios that can be used for benchmarking.
View PDF on arXiv (opens in a new tab)

End-to-End Driving Via Conditional Imitation Learning

This work evaluates different architectures for conditional imitation learning in vision-based driving and conducts experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area.
View on IEEE (opens in a new tab)

Object-Centric Learning with Slot Attention

An architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention is presented.
View PDF on arXiv (opens in a new tab)
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)