Search Results for author: Dian Chen

Found 12 papers, 6 papers with code

COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles

no code implementations4 May 2022 Jiaxun Cui, Hang Qiu, Dian Chen, Peter Stone, Yuke Zhu

To evaluate our model, we develop AutoCastSim, a network-augmented driving simulation framework with example accident-prone scenarios.

Autonomous Driving

Contrastive Test-Time Adaptation

no code implementations21 Apr 2022 Dian Chen, Dequan Wang, Trevor Darrell, Sayna Ebrahimi

We propose a novel way to leverage self-supervised contrastive learning to facilitate target feature learning, along with an online pseudo labeling scheme with refinement that significantly denoises pseudo labels.

Contrastive Learning Unsupervised Domain Adaptation

Multi-Frame Self-Supervised Depth with Transformers

no code implementations15 Apr 2022 Vitor Guizilini, Rares Ambrus, Dian Chen, Sergey Zakharov, Adrien Gaidon

Experiments on the KITTI and DDAD datasets show that our DepthFormer architecture establishes a new state of the art in self-supervised monocular depth estimation, and is even competitive with highly specialized supervised single-frame architectures.

Frame Monocular Depth Estimation

Learning from All Vehicles

1 code implementation22 Mar 2022 Dian Chen, Philipp Krähenbühl

In this paper, we present a system to train driving policies from experiences collected not just from the ego-vehicle, but all vehicles that it observes.

Autonomous Driving

Region-level Active Detector Learning

no code implementations20 Aug 2021 Michael Laielli, Giscard Biamby, Dian Chen, Ritwik Gupta, Adam Loeffler, Phat Dat Nguyen, Ross Luo, Trevor Darrell, Sayna Ebrahimi

Active learning for object detection is conventionally achieved by applying techniques developed for classification in a way that aggregates individual detections into image-level selection criteria.

Active Learning Object Detection

CARTL: Cooperative Adversarially-Robust Transfer Learning

1 code implementation12 Jun 2021 Dian Chen, Hongxin Hu, Qian Wang, Yinli Li, Cong Wang, Chao Shen, Qi Li

In deep learning, a typical strategy for transfer learning is to freeze the early layers of a pre-trained model and fine-tune the rest of its layers on the target domain.

Adversarial Robustness Transfer Learning

Learning to drive from a world on rails

1 code implementation ICCV 2021 Dian Chen, Vladlen Koltun, Philipp Krähenbühl

This assumption greatly simplifies the learning problem, factorizing the dynamics into a nonreactive world model and a low-dimensional and compact forward model of the ego-vehicle.

Autonomous Driving Model-based Reinforcement Learning

Minimax Active Learning

no code implementations18 Dec 2020 Sayna Ebrahimi, William Gan, Dian Chen, Giscard Biamby, Kamyar Salahi, Michael Laielli, Shizhan Zhu, Trevor Darrell

Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator.

Active Learning Image Classification +1

Learning Instance Segmentation by Interaction

1 code implementation21 Jun 2018 Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, Jitendra Malik

The agent uses its current segmentation model to infer pixels that constitute objects and refines the segmentation model by interacting with these pixels.

Instance Segmentation Semantic Segmentation

Cannot find the paper you are looking for? You can Submit a new open access paper.