Search Results for author: Zhizheng Zhang

Found 42 papers, 13 papers with code

Reinforced UI Instruction Grounding: Towards a Generic UI Task Automation API

no code implementations7 Oct 2023 Zhizheng Zhang, Wenxuan Xie, Xiaoyi Zhang, Yan Lu

In this work, we build a multimodal model to ground natural language instructions in given UI screenshots as a generic UI task automation executor.

document understanding Reinforcement Learning (RL)

When and Why Momentum Accelerates SGD:An Empirical Study

no code implementations15 Jun 2023 Jingwen Fu, Bohan Wang, Huishuai Zhang, Zhizheng Zhang, Wei Chen, Nanning Zheng

In the comparison of SGDM and SGD with the same effective learning rate and the same batch size, we observe a consistent pattern: when $\eta_{ef}$ is small, SGDM and SGD experience almost the same empirical training losses; when $\eta_{ef}$ surpasses a certain threshold, SGDM begins to perform better than SGD.

Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators

no code implementations2 Jun 2023 Zhizheng Zhang, Xiaoyi Zhang, Wenxuan Xie, Yan Lu

In specific, we present Responsible Task Automation (ResponsibleTA) as a fundamental framework to facilitate responsible collaboration between LLM-based coordinators and executors for task automation with three empowered capabilities: 1) predicting the feasibility of the commands for executors; 2) verifying the completeness of executors; 3) enhancing the security (e. g., the protection of users' privacy).

Prompt Engineering

MRVM-NeRF: Mask-Based Pretraining for Neural Radiance Fields

no code implementations11 Apr 2023 Ganlin Yang, Guoqiang Wei, Zhizheng Zhang, Yan Lu, Dong Liu

Most Neural Radiance Fields (NeRFs) have poor generalization ability, limiting their application when representing multiple scenes by a single model.

Unifying Layout Generation with a Decoupled Diffusion Model

no code implementations CVPR 2023 Mude Hui, Zhizheng Zhang, Xiaoyi Zhang, Wenxuan Xie, Yuwang Wang, Yan Lu

Since different attributes have their individual semantics and characteristics, we propose to decouple the diffusion processes for them to improve the diversity of training samples and learn the reverse process jointly to exploit global-scope contexts for facilitating generation.

Versatile Neural Processes for Learning Implicit Neural Representations

1 code implementation21 Jan 2023 Zongyu Guo, Cuiling Lan, Zhizheng Zhang, Yan Lu, Zhibo Chen

In this paper, we propose an efficient NP framework dubbed Versatile Neural Processes (VNP), which largely increases the capability of approximating functions.

Template-guided Hierarchical Feature Restoration for Anomaly Detection

no code implementations ICCV 2023 Hewei Guo, Liping Ren, Jingjing Fu, Yuwang Wang, Zhizheng Zhang, Cuiling Lan, Haoqian Wang, Xinwen Hou

Targeting for detecting anomalies of various sizes for complicated normal patterns, we propose a Template-guided Hierarchical Feature Restoration method, which introduces two key techniques, bottleneck compression and template-guided compensation, for anomaly-free feature restoration.

Anomaly Detection

Image Coding for Machines with Omnipotent Feature Learning

no code implementations5 Jul 2022 Ruoyu Feng, Xin Jin, Zongyu Guo, Runsen Feng, Yixin Gao, Tianyu He, Zhizheng Zhang, Simeng Sun, Zhibo Chen

Learning a kind of feature that is both general (for AI tasks) and compact (for compression) is pivotal for its success.

Self-Supervised Learning

Deep Frequency Filtering for Domain Generalization

no code implementations CVPR 2023 Shiqi Lin, Zhizheng Zhang, Zhipeng Huang, Yan Lu, Cuiling Lan, Peng Chu, Quanzeng You, Jiang Wang, Zicheng Liu, Amey Parulkar, Viraj Navkal, Zhibo Chen

Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge.

Domain Generalization Retrieval

Active Token Mixer

2 code implementations11 Mar 2022 Guoqiang Wei, Zhizheng Zhang, Cuiling Lan, Yan Lu, Zhibo Chen

In this work, we propose an innovative token-mixer, dubbed Active Token Mixer (ATM), to actively incorporate flexible contextual information distributed across different channels from other tokens into the given query token.

Image Classification Instance Segmentation +2

Mask-based Latent Reconstruction for Reinforcement Learning

1 code implementation28 Jan 2022 Tao Yu, Zhizheng Zhang, Cuiling Lan, Yan Lu, Zhibo Chen

For deep reinforcement learning (RL) from pixels, learning effective state representations is crucial for achieving high performance.

reinforcement-learning Reinforcement Learning (RL) +1

Confounder Identification-free Causal Visual Feature Learning

no code implementations26 Nov 2021 Xin Li, Zhizheng Zhang, Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Xin Jin, Zhibo Chen

In this paper, we propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders.

Domain Generalization Meta-Learning

Dual Aspect Self-Attention based on Transformer for Remaining Useful Life Prediction

1 code implementation30 Jun 2021 Zhizheng Zhang, Wen Song, Qiqiang Li

While deep learning has achieved great success in RUL prediction, existing methods have difficulties in processing long sequences and extracting information from the sensor and time step aspects.

ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation

1 code implementation NeurIPS 2021 Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, Zhibo Chen

Unsupervised domain adaptive classifcation intends to improve the classifcation performance on unlabeled target domain.

Unsupervised Domain Adaptation

Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification

no code implementations25 Mar 2021 Zhizheng Zhang, Cuiling Lan, Wenjun Zeng, Quanzeng You, Zicheng Liu, Kecheng Zheng, Zhibo Chen

Each recomposed feature, obtained based on the domain-invariant feature (which enables a reliable inheritance of identity) and an enhancement from a domain specific feature (which enables the approximation of real distributions), is thus an "ideal" augmentation.

Disentanglement Domain Adaptive Person Re-Identification +1

Learned Block-based Hybrid Image Compression

no code implementations17 Dec 2020 Yaojun Wu, Xin Li, Zhizheng Zhang, Xin Jin, Zhibo Chen

Recent works on learned image compression perform encoding and decoding processes in a full-resolution manner, resulting in two problems when deployed for practical applications.

Blocking Image Compression +2

Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification

1 code implementation16 Dec 2020 Kecheng Zheng, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, Zheng-Jun Zha

Based on this finding, we propose to exploit the uncertainty (measured by consistency levels) to evaluate the reliability of the pseudo-label of a sample and incorporate the uncertainty to re-weight its contribution within various ReID losses, including the identity (ID) classification loss per sample, the triplet loss, and the contrastive loss.

Clustering Domain Adaptive Person Re-Identification +3

Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution

no code implementations11 Dec 2020 Xin Li, Xin Jin, Tao Yu, Yingxue Pang, Simeng Sun, Zhizheng Zhang, Zhibo Chen

Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i. e., bicubic down-sampling), typically suffer from poor performance when applied into real-world low-resolution (LR) images due to the complicated realistic degradations.

Image Super-Resolution

Causal Contextual Prediction for Learned Image Compression

no code implementations19 Nov 2020 Zongyu Guo, Zhizheng Zhang, Runsen Feng, Zhibo Chen

In this paper, we propose the concept of separate entropy coding to leverage a serial decoding process for causal contextual entropy prediction in the latent space.

Image Compression MS-SSIM +1

Uncertainty-Aware Few-Shot Image Classification

no code implementations9 Oct 2020 Zhizheng Zhang, Cuiling Lan, Wenjun Zeng, Zhibo Chen, Shih-Fu Chang

In this work, we propose Uncertainty-Aware Few-Shot framework for image classification by modeling uncertainty of the similarities of query-support pairs and performing uncertainty-aware optimization.

Classification Few-Shot Image Classification +3

Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification

no code implementations8 Jun 2020 Zhizheng Zhang, Cuiling Lan, Wen-Jun Zeng, Zhibo Chen, Shih-Fu Chang

There is a lack of loss design which enables the joint optimization of multiple instances (of multiple classes) within per-query optimization for person ReID.

Classification General Classification +3

Multi-scale Grouped Dense Network for VVC Intra Coding

no code implementations16 May 2020 Xin Li, Simeng Sun, Zhizheng Zhang, Zhibo Chen

Versatile Video Coding (H. 266/VVC) standard achieves better image quality when keeping the same bits than any other conventional image codec, such as BPG, JPEG, and etc.

Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification

no code implementations CVPR 2020 Zhizheng Zhang, Cuiling Lan, Wen-Jun Zeng, Zhibo Chen

In this paper, we propose an attentive feature aggregation module, namely Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to delicately aggregate spatio-temporal features into a discriminative video-level feature representation.

Video-Based Person Re-Identification

Region Normalization for Image Inpainting

1 code implementation23 Nov 2019 Tao Yu, Zongyu Guo, Xin Jin, Shilin Wu, Zhibo Chen, Weiping Li, Zhizheng Zhang, Sen Liu

In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation.

Image Inpainting

On the Strong Equivalences of LPMLN Programs

no code implementations18 Sep 2019 Bin Wang, Jun Shen, Shutao Zhang, Zhizheng Zhang

Firstly, we present the notions of p-strong and w-strong equivalences between LPMLN programs.

On the Strong Equivalences for LPMLN Programs

no code implementations9 Sep 2019 Bin Wang, Jun Shen, Shutao Zhang, Zhizheng Zhang

In this paper, we study the strong equivalence for LPMLN programs, which is an important tool for program rewriting and theoretical investigations in the field of logic programming.

Logic in Computer Science D.1.6

A Coarse-to-Fine Framework for Learned Color Enhancement with Non-Local Attention

no code implementations8 Jun 2019 Chaowei Shan, Zhizheng Zhang, Zhibo Chen

For current learned methods in this field, global harmonious perception and local details are hard to be well-considered in a single model simultaneously.

Relation-Aware Global Attention for Person Re-identification

1 code implementation CVPR 2020 Zhizheng Zhang, Cuiling Lan, Wen-Jun Zeng, Xin Jin, Zhibo Chen

For person re-identification (re-id), attention mechanisms have become attractive as they aim at strengthening discriminative features and suppressing irrelevant ones, which matches well the key of re-id, i. e., discriminative feature learning.

Clustering Image Classification +2

Asynchronous Episodic Deep Deterministic Policy Gradient: Towards Continuous Control in Computationally Complex Environments

1 code implementation3 Mar 2019 Zhizheng Zhang, Jiale Chen, Zhibo Chen, Weiping Li

Not limited to the control tasks in computationally complex environments, AE-DDPG also achieves higher rewards and 2- to 4-fold improvement in sample efficiency on average compared to other variants of DDPG in MuJoCo environments.

Continuous Control Reinforcement Learning (RL)

ESmodels: An Epistemic Specification Solver

no code implementations14 May 2014 Zhizheng Zhang, Kaikai Zhao

(To appear in Theory and Practice of Logic Programming (TPLP)) ESmodels is designed and implemented as an experiment platform to investigate the semantics, language, related reasoning algorithms, and possible applications of epistemic specifications. We first give the epistemic specification language of ESmodels and its semantics.

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