Search Results for author: Zihan Ding

Found 17 papers, 10 papers with code

Learning a Universal Human Prior for Dexterous Manipulation from Human Preference

no code implementations10 Apr 2023 Zihan Ding, Yuanpei Chen, Allen Z. Ren, Shixiang Shane Gu, Hao Dong, Chi Jin

Generating human-like behavior on robots is a great challenge especially in dexterous manipulation tasks with robotic hands.

Robot Manipulation

Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection

1 code implementation CVPR 2023 Luting Wang, Yi Liu, Penghui Du, Zihan Ding, Yue Liao, Qiaosong Qi, Biaolong Chen, Si Liu

When extracting object knowledge from PVLMs, the former adaptively transforms object proposals and adopts object-aware mask attention to obtain precise and complete knowledge of objects.

Open Vocabulary Object Detection

PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding

1 code implementation11 Aug 2022 Zihan Ding, Zi-han Ding, Tianrui Hui, Junshi Huang, Xiaoming Wei, Xiaolin Wei, Si Liu

To alleviate these drawbacks, we propose a one-stage end-to-end Pixel-Phrase Matching Network (PPMN), which directly matches each phrase to its corresponding pixels instead of region proposals and outputs panoptic segmentation by simple combination.

Panoptic Segmentation Semantic correspondence

A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

2 code implementations18 Jul 2022 Zihan Ding, DiJia Su, Qinghua Liu, Chi Jin

This paper proposes new, end-to-end deep reinforcement learning algorithms for learning two-player zero-sum Markov games.

Atari Games Q-Learning

Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game

1 code implementation3 Jun 2022 Zhiyuan Yao, Zihan Ding

A fully distributed MARL algorithm is proposed to approximate the Nash equilibrium of the game.

Fairness Management +1

Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center

no code implementations27 Jan 2022 Zhiyuan Yao, Zihan Ding, Thomas Clausen

This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods.

Multi-agent Reinforcement Learning reinforcement-learning +1

Reinforced Workload Distribution Fairness

no code implementations29 Oct 2021 Zhiyuan Yao, Zihan Ding, Thomas Heide Clausen

Network load balancers are central components in data centers, that distributes workloads across multiple servers and thereby contribute to offering scalable services.

Fairness Reinforcement Learning (RL)

Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation

no code implementations CVPR 2021 Tianrui Hui, Shaofei Huang, Si Liu, Zihan Ding, Guanbin Li, Wenguan Wang, Jizhong Han, Fei Wang

Though 3D convolutions are amenable to recognizing which actor is performing the queried actions, it also inevitably introduces misaligned spatial information from adjacent frames, which confuses features of the target frame and yields inaccurate segmentation.

feature selection Referring Expression Segmentation

Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

1 code implementation19 Apr 2021 Jie Ren, Yewen Li, Zihan Ding, Wei Pan, Hao Dong

However, grasping distinguishable skills for some tasks with non-unique optima can be essential for further improving its learning efficiency and performance, which may lead to a multimodal policy represented as a mixture-of-experts (MOE).

reinforcement-learning Reinforcement Learning (RL)

DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration

no code implementations22 Feb 2021 Ya-Yen Tsai, Hui Xu, Zihan Ding, Chong Zhang, Edward Johns, Bidan Huang

One of the main challenges of transferring the policy learned in a simulated environment to real world, is the discrepancy between the dynamics of the two environments.


CDT: Cascading Decision Trees for Explainable Reinforcement Learning

1 code implementation15 Nov 2020 Zihan Ding, Pablo Hernandez-Leal, Gavin Weiguang Ding, Changjian Li, Ruitong Huang

As a second contribution our study reveals limitations of explaining black-box policies via imitation learning with tree-based explainable models, due to its inherent instability.

Explainable Models Imitation Learning +3

Efficient Reinforcement Learning Development with RLzoo

1 code implementation18 Sep 2020 Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Guo Li, Quancheng Guo, Luo Mai, Hao Dong

RLzoo provides developers with (i) high-level yet flexible APIs for prototyping DRL agents, and further customising the agents for best performance, (ii) a model zoo where users can import a wide range of DRL agents and easily compare their performance, and (iii) an algorithm that can automatically construct DRL agents with custom components (which are critical to improve agent's performance in custom applications).

reinforcement-learning Reinforcement Learning (RL)

Crossing The Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamics

no code implementations15 Aug 2020 Eugene Valassakis, Zihan Ding, Edward Johns

Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem.

TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation

1 code implementation28 Jan 2019 Zihan Ding, Xiao-Yang Liu, Miao Yin, Linghe Kong

Secondly, we propose TGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions.

Dictionary Learning Image Generation +1

Deep Reinforcement Learning for Intelligent Transportation Systems

no code implementations3 Dec 2018 Xiao-Yang Liu, Zihan Ding, Sem Borst, Anwar Walid

Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe.

Management reinforcement-learning +1

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