Zhangjie Cao

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HUAWEI Technologies Co., Ltd.

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MIT

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Stanford University

Staff at Electric power bureau of Hangzhou, ChinaSouthwest Jiaotong University

Featured Co-authors

Philip S. Yu
245 publications
Stefano Ermon
138 publications
Jitendra Malik
121 publications
Weinan Zhang
121 publications
Yong Yu
93 publications
Qiang Yang
89 publications
Leonidas Guibas
79 publications
Dorsa Sadigh
72 publications
Chao Huang
60 publications
Jianmin Wang
57 publications
Adrien Gaidon
57 publications

∙ 09/16/2022

Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations

Multimodal demonstrations provide robots with an abundance of informatio...

Yilun Hao, et al. ∙

∙ 06/08/2022

Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models

Transfer learning aims to leverage knowledge from pre-trained models to ...

Yang Shu, et al. ∙

∙ 04/15/2022

MetaSets: Meta-Learning on Point Sets for Generalizable Representations

Deep learning techniques for point clouds have achieved strong performan...

Chao Huang, et al. ∙

∙ 03/14/2022

From Big to Small: Adaptive Learning to Partial-Set Domains

Domain adaptation targets at knowledge acquisition and dissemination fro...

Zhangjie Cao, et al. ∙

∙ 03/08/2022

Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction

Multi-agent interactions are important to model for forecasting other ag...

Zhangjie Cao, et al. ∙

∙ 03/02/2022

Weakly Supervised Correspondence Learning

Correspondence learning is a fundamental problem in robotics, which aims...

Zihan Wang, et al. ∙

∙ 02/07/2022

Learning from Imperfect Demonstrations via Adversarial Confidence Transfer

Existing learning from demonstration algorithms usually assume access to...

Zhangjie Cao, et al. ∙

∙ 10/28/2021

Learning Feasibility to Imitate Demonstrators with Different Dynamics

The goal of learning from demonstrations is to learn a policy for an age...

Zhangjie Cao, et al. ∙

∙ 10/27/2021

Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality

Most existing imitation learning approaches assume the demonstrations ar...

Songyuan Zhang, et al. ∙

∙ 10/14/2021

Omni-Training for Data-Efficient Deep Learning

Learning a generalizable deep model from a few examples in a short time ...

Yang Shu, et al. ∙

∙ 07/10/2021

Multi-Agent Imitation Learning with Copulas

Multi-agent imitation learning aims to train multiple agents to perform ...

Hongwei Wang, et al. ∙

∙ 06/29/2021

Zoo-Tuning: Adaptive Transfer from a Zoo of Models

With the development of deep networks on various large-scale datasets, a...

Yang Shu, et al. ∙

∙ 04/08/2021

Open Domain Generalization with Domain-Augmented Meta-Learning

Leveraging datasets available to learn a model with high generalization ...

Yang Shu, et al. ∙

∙ 03/10/2021

Learning from Imperfect Demonstrations from Agents with Varying Dynamics

Imitation learning enables robots to learn from demonstrations. Previous...

Zhangjie Cao, et al. ∙

∙ 02/10/2021

Transfer Reinforcement Learning across Homotopy Classes

The ability for robots to transfer their learned knowledge to new tasks ...

Zhangjie Cao, et al. ∙

∙ 07/01/2020

Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving

Autonomous driving has achieved significant progress in recent years, bu...

Zhangjie Cao, et al. ∙

∙ 06/07/2020

Robust Learning Through Cross-Task Consistency

Visual perception entails solving a wide set of tasks, e.g., object dete...

Amir Zamir, et al. ∙

∙ 11/21/2019

Improving Unsupervised Domain Adaptation with Variational Information Bottleneck

Domain adaptation aims to leverage the supervision signal of source doma...

Yuxuan Song, et al. ∙

∙ 06/27/2019

Few-Shot Video Classification via Temporal Alignment

There is a growing interest in learning a model which could recognize no...

Kaidi Cao, et al. ∙

∙ 05/30/2019

AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows

Given unpaired data from multiple domains, a key challenge is to efficie...

Aditya Grover, et al. ∙

∙ 03/28/2019

Learning to Transfer Examples for Partial Domain Adaptation

Domain adaptation is critical for learning in new and unseen environment...

Zhangjie Cao, et al. ∙

∙ 09/04/2018

Multi-Adversarial Domain Adaptation

Recent advances in deep domain adaptation reveal that adversarial learni...

Zhongyi Pei, et al. ∙

∙ 09/04/2018

Deep Priority Hashing

Deep hashing enables image retrieval by end-to-end learning of deep repr...

Zhangjie Cao, et al. ∙

∙ 08/10/2018

Partial Adversarial Domain Adaptation

Domain adversarial learning aligns the feature distributions across the ...

Zhangjie Cao, et al. ∙

∙ 12/13/2017

Transfer Adversarial Hashing for Hamming Space Retrieval

Hashing is widely applied to large-scale image retrieval due to the stor...

Zhangjie Cao, et al. ∙

∙ 12/12/2017

3D Object Classification via Spherical Projections

In this paper, we introduce a new method for classifying 3D objects. Our...

Zhangjie Cao, et al. ∙

∙ 02/02/2017

HashNet: Deep Learning to Hash by Continuation

Learning to hash has been widely applied to approximate nearest neighbor...

Zhangjie Cao, et al. ∙

∙ 08/15/2016

Transitive Hashing Network for Heterogeneous Multimedia Retrieval

Hashing has been widely applied to large-scale multimedia retrieval due ...

Zhangjie Cao, et al. ∙