Search Results for author: Zhiyuan Xu

Found 28 papers, 5 papers with code

SWBT: Similarity Weighted Behavior Transformer with the Imperfect Demonstration for Robotic Manipulation

no code implementations17 Jan 2024 Kun Wu, Ning Liu, Zhen Zhao, Di Qiu, Jinming Li, Zhengping Che, Zhiyuan Xu, Qinru Qiu, Jian Tang

Imitation learning (IL), aiming to learn optimal control policies from expert demonstrations, has been an effective method for robot manipulation tasks.

Imitation Learning Robot Manipulation

An Efficient Generalizable Framework for Visuomotor Policies via Control-aware Augmentation and Privilege-guided Distillation

no code implementations17 Jan 2024 Yinuo Zhao, Kun Wu, Tianjiao Yi, Zhiyuan Xu, Xiaozhu Ju, Zhengping Che, Qinru Qiu, Chi Harold Liu, Jian Tang

Visuomotor policies, which learn control mechanisms directly from high-dimensional visual observations, confront challenges in adapting to new environments with intricate visual variations.

Data Augmentation Reinforcement Learning (RL) +1

Language-Conditioned Robotic Manipulation with Fast and Slow Thinking

no code implementations8 Jan 2024 Minjie Zhu, Yichen Zhu, Jinming Li, Junjie Wen, Zhiyuan Xu, Zhengping Che, Chaomin Shen, Yaxin Peng, Dong Liu, Feifei Feng, Jian Tang

The language-conditioned robotic manipulation aims to transfer natural language instructions into executable actions, from simple pick-and-place to tasks requiring intent recognition and visual reasoning.

Decision Making Intent Recognition +2

Cross-Modal Reasoning with Event Correlation for Video Question Answering

no code implementations20 Dec 2023 Chengxiang Yin, Zhengping Che, Kun Wu, Zhiyuan Xu, Qinru Qiu, Jian Tang

Video Question Answering (VideoQA) is a very attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i. e., the spatio-temporal video content and the word sequence in question.

Question Answering Video Question Answering

Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering

no code implementations20 Dec 2023 Chengxiang Yin, Zhengping Che, Kun Wu, Zhiyuan Xu, Jian Tang

Visual Question Answering (VQA) has emerged as one of the most challenging tasks in artificial intelligence due to its multi-modal nature.

Question Answering Visual Question Answering

Optimization Techniques for a Physical Model of Human Vocalisation

no code implementations26 Sep 2023 Mateo Cámara, Zhiyuan Xu, Yisu Zong, José Luis Blanco, Joshua D. Reiss

We present a non-supervised approach to optimize and evaluate the synthesis of non-speech audio effects from a speech production model.

Benchmarking

DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field

no code implementations4 Aug 2023 Haowen Wang, Zhipeng Fan, Zhen Zhao, Zhengping Che, Zhiyuan Xu, Dong Liu, Feifei Feng, Yakun Huang, XIUQUAN QIAO, Jian Tang

We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene.

Object Pose Estimation

RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion

no code implementations6 Jun 2023 Haowen Wang, Zhengping Che, Mingyuan Wang, Zhiyuan Xu, XIUQUAN QIAO, Mengshi Qi, Feifei Feng, Jian Tang

The raw depth image captured by indoor depth sensors usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transparent objects and the limited distance range.

Depth Completion Transparent objects

CP$^3$: Channel Pruning Plug-in for Point-based Networks

no code implementations23 Mar 2023 Yaomin Huang, Ning Liu, Zhengping Che, Zhiyuan Xu, Chaomin Shen, Yaxin Peng, Guixu Zhang, Xinmei Liu, Feifei Feng, Jian Tang

CP$^3$ is elaborately designed to leverage the characteristics of point clouds and PNNs in order to enable 2D channel pruning methods for PNNs.

CP3: Channel Pruning Plug-In for Point-Based Networks

no code implementations CVPR 2023 Yaomin Huang, Ning Liu, Zhengping Che, Zhiyuan Xu, Chaomin Shen, Yaxin Peng, Guixu Zhang, Xinmei Liu, Feifei Feng, Jian Tang

Directly implementing the 2D CNN channel pruning methods to PNNs undermine the performance of PNNs because of the different representations of 2D images and 3D point clouds as well as the network architecture disparity.

ScaleKD: Distilling Scale-Aware Knowledge in Small Object Detector

no code implementations CVPR 2023 Yichen Zhu, Qiqi Zhou, Ning Liu, Zhiyuan Xu, Zhicai Ou, Xiaofeng Mou, Jian Tang

Unlike existing works that struggle to balance the trade-off between inference speed and SOD performance, in this paper, we propose a novel Scale-aware Knowledge Distillation (ScaleKD), which transfers knowledge of a complex teacher model to a compact student model.

Knowledge Distillation object-detection +2

Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation

1 code implementation21 Nov 2022 Zhen Tian, Ting Bai, Zibin Zhang, Zhiyuan Xu, Kangyi Lin, Ji-Rong Wen, Wayne Xin Zhao

Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference.

Click-Through Rate Prediction Knowledge Distillation +1

Label-Guided Auxiliary Training Improves 3D Object Detector

1 code implementation24 Jul 2022 Yaomin Huang, Xinmei Liu, Yichen Zhu, Zhiyuan Xu, Chaomin Shen, Zhengping Che, Guixu Zhang, Yaxin Peng, Feifei Feng, Jian Tang

Detecting 3D objects from point clouds is a practical yet challenging task that has attracted increasing attention recently.

3D Object Detection Object +1

Continual Few-Shot Learning with Adversarial Class Storage

no code implementations10 Jul 2022 Kun Wu, Chengxiang Yin, Jian Tang, Zhiyuan Xu, Yanzhi Wang, Dejun Yang

In this paper, we define a new problem called continual few-shot learning, in which tasks arrive sequentially and each task is associated with a few training samples.

continual few-shot learning Few-Shot Learning +1

RGB-Depth Fusion GAN for Indoor Depth Completion

no code implementations CVPR 2022 Haowen Wang, Mingyuan Wang, Zhengping Che, Zhiyuan Xu, XIUQUAN QIAO, Mengshi Qi, Feifei Feng, Jian Tang

In this paper, we design a novel two-branch end-to-end fusion network, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map.

Depth Completion Transparent objects

CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving

1 code implementation17 Feb 2022 Yinuo Zhao, Kun Wu, Zhiyuan Xu, Zhengping Che, Qi Lu, Jian Tang, Chi Harold Liu

Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors.

reinforcement-learning Reinforcement Learning (RL)

Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning

no code implementations ICCV 2021 Chengxiang Yin, Kun Wu, Zhengping Che, Bo Jiang, Zhiyuan Xu, Jian Tang

Deep learning has made tremendous success in computer vision, natural language processing and even visual-semantic learning, which requires a huge amount of labeled training data.

Graph Attention Image Captioning +2

Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control

1 code implementation NeurIPS 2020 Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang, Jieping Ye

While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks.

Continuous Control reinforcement-learning +2

AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates

no code implementations6 Jul 2019 Ning Liu, Xiaolong Ma, Zhiyuan Xu, Yanzhi Wang, Jian Tang, Jieping Ye

This work proposes AutoCompress, an automatic structured pruning framework with the following key performance improvements: (i) effectively incorporate the combination of structured pruning schemes in the automatic process; (ii) adopt the state-of-art ADMM-based structured weight pruning as the core algorithm, and propose an innovative additional purification step for further weight reduction without accuracy loss; and (iii) develop effective heuristic search method enhanced by experience-based guided search, replacing the prior deep reinforcement learning technique which has underlying incompatibility with the target pruning problem.

Model Compression

Adversarial Meta-Learning

no code implementations8 Jun 2018 Chengxiang Yin, Jian Tang, Zhiyuan Xu, Yanzhi Wang

Meta-learning enables a model to learn from very limited data to undertake a new task.

Meta-Learning

Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning

no code implementations2 Mar 2018 Teng Li, Zhiyuan Xu, Jian Tang, Yanzhi Wang

Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in DSDPSs; and present design, implementation and evaluation of a novel and highly effective DRL-based control framework, which minimizes average end-to-end tuple processing time by jointly learning the system environment via collecting very limited runtime statistics data and making decisions under the guidance of powerful Deep Neural Networks.

reinforcement-learning Reinforcement Learning (RL) +1

Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data

no code implementations28 Jan 2018 Ning Liu, Ying Liu, Brent Logan, Zhiyuan Xu, Jian Tang, Yanzhi Wang

This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data.

reinforcement-learning Reinforcement Learning (RL)

Experience-driven Networking: A Deep Reinforcement Learning based Approach

no code implementations17 Jan 2018 Zhiyuan Xu, Jian Tang, Jingsong Meng, Weiyi Zhang, Yanzhi Wang, Chi Harold Liu, Dejun Yang

Modern communication networks have become very complicated and highly dynamic, which makes them hard to model, predict and control.

Continuous Control reinforcement-learning +1

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

no code implementations13 Mar 2017 Ning Liu, Zhe Li, Zhiyuan Xu, Jielong Xu, Sheng Lin, Qinru Qiu, Jian Tang, Yanzhi Wang

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system.

Cloud Computing Decision Making +3

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