no code implementations • 15 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.
1 code implementation • 25 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.
no code implementations • 19 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.
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.
no code implementations • ICCV 2023 • Vitor Guizilini, Igor Vasiljevic, Dian Chen, Rares Ambrus, Adrien Gaidon
Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to produce metric predictions.
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.
1 code implementation • CVPR 2023 • Ziqi Pang, Jie Li, Pavel Tokmakov, Dian Chen, Sergey Zagoruyko, Yu-Xiong Wang
It emphasizes spatio-temporal continuity and integrates both past and future reasoning for tracked objects.
no code implementations • 5 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.
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.
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.
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.
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.
Ranked #5 on Autonomous Driving on CARLA Leaderboard
no code implementations • 20 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.
1 code implementation • 12 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.
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.
Ranked #13 on Autonomous Driving on CARLA Leaderboard
no code implementations • 18 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.
9 code implementations • 27 Dec 2019 • Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl
We first train an agent that has access to privileged information.
Ranked #16 on Autonomous Driving on CARLA Leaderboard
1 code implementation • 21 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.
1 code implementation • ICLR 2018 • Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell
In our framework, the role of the expert is only to communicate the goals (i. e., what to imitate) during inference.
no code implementations • 6 Mar 2017 • Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine
Manipulation of deformable objects, such as ropes and cloth, is an important but challenging problem in robotics.