Search Results for author: Dian Chen

Found 20 papers, 11 papers with code

SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms

no code implementations15 Feb 2024 Dian Chen, Paul Yang, Ing-Ray Chen, Dong Sam Ha, Jin-Hee Cho

We propose a novel energy-aware federated learning (FL)-based system, namely SusFL, for sustainable smart farming to address the challenge of inconsistent health monitoring due to fluctuating energy levels of solar sensors.

Data Poisoning Federated Learning

pix2gestalt: Amodal Segmentation by Synthesizing Wholes

1 code implementation25 Jan 2024 Ege Ozguroglu, Ruoshi Liu, Dídac Surís, Dian Chen, Achal Dave, Pavel Tokmakov, Carl Vondrick

We introduce pix2gestalt, a framework for zero-shot amodal segmentation, which learns to estimate the shape and appearance of whole objects that are only partially visible behind occlusions.

3D Reconstruction Object Recognition +1

FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects

no code implementations19 Oct 2023 Mayank Lunayach, Sergey Zakharov, Dian Chen, Rares Ambrus, Zsolt Kira, Muhammad Zubair Irshad

In this work, we address the challenging task of 3D object recognition without the reliance on real-world 3D labeled data.

3D Object Recognition 6D Pose Estimation

MotionLM: Multi-Agent Motion Forecasting as Language Modeling

no code implementations ICCV 2023 Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp

Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a language modeling task over this domain.

Autonomous Vehicles Language Modelling +2

Viewpoint Equivariance for Multi-View 3D Object Detection

1 code implementation CVPR 2023 Dian Chen, Jie Li, Vitor Guizilini, Rares Ambrus, Adrien Gaidon

We design view-conditioned queries at the output level, which enables the generation of multiple virtual frames during training to learn viewpoint equivariance by enforcing multi-view consistency.

3D Object Detection Object +2

Depth Is All You Need for Monocular 3D Detection

no code implementations5 Oct 2022 Dennis Park, Jie Li, Dian Chen, Vitor Guizilini, Adrien Gaidon

Our methods leverage commonly available LiDAR or RGB videos during training time to fine-tune the depth representation, which leads to improved 3D detectors.

Depth Prediction Monocular Depth Estimation +1

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

1 code implementation CVPR 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

1 code implementation CVPR 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 Test +1

Multi-Frame Self-Supervised Depth with Transformers

no code implementations CVPR 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.

Monocular Depth Estimation

Learning from All Vehicles

1 code implementation CVPR 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 CARLA longest6

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 +2

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 CARLA longest6 +1

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 Clustering +2

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 Segmentation +2

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