Search Results for author: Jie Gui

Found 33 papers, 13 papers with code

Underwater Organism Color Enhancement via Color Code Decomposition, Adaptation and Interpolation

1 code implementation29 Sep 2024 Xiaofeng Cong, Jing Zhang, Yeying Jin, JunMing Hou, Yu Zhao, Jie Gui, James Tin-Yau Kwok, Yuan Yan Tang

ColorCode offers three key features: 1) color enhancement, producing an enhanced image with a fixed color; 2) color adaptation, enabling controllable adjustments of long-wavelength color components using guidance images; and 3) color interpolation, allowing for the smooth generation of multiple colors through continuous sampling of the color code.

Image Enhancement

Improving Fast Adversarial Training via Self-Knowledge Guidance

no code implementations26 Sep 2024 Chengze Jiang, Junkai Wang, Minjing Dong, Jie Gui, Xinli Shi, Yuan Cao, Yuan Yan Tang, James Tin-Yau Kwok

Based on the analysis, we mainly attribute the observed misalignment and disparity to the imbalanced optimization in FAT, which motivates us to optimize different training data adaptively to enhance robustness.

Adversarial Robustness Attribute

CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition

no code implementations23 Sep 2024 Yifan Wang, Jie Gui, Yuan Yan Tang, James Tin-Yau Kwok

BWR-ROIAlign can directly plug into the model to introduce the above features for DCNN-based finger vein recognition systems.

Finger Vein Recognition

Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment

no code implementations22 Sep 2024 Jidong Kuang, Hongsong Wang, Chaolei Han, Jie Gui

The DA module maps the skeleton features to the semantic space through a specially designed visual projector, followed by the SDE, which is based on cross-attention to enhance the connection between skeleton and text, thereby reducing the gap between modalities.

Action Recognition Metric Learning +2

Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective

no code implementations22 Jul 2024 Jie Gui, Chengze Jiang, Minjing Dong, Kun Tong, Xinli Shi, Yuan Yan Tang, DaCheng Tao

However, FAT suffers from catastrophic overfitting, which leads to a performance drop compared with multi-step adversarial training.

CoLA

A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning

no code implementations30 May 2024 Xiaofeng Cong, Yu Zhao, Jie Gui, JunMing Hou, DaCheng Tao

Underwater image enhancement (UIE) presents a significant challenge within computer vision research.

Disentanglement UIE

A Comprehensive Survey and Taxonomy on Point Cloud Registration Based on Deep Learning

1 code implementation22 Apr 2024 Yu-Xin Zhang, Jie Gui, Xiaofeng Cong, Xin Gong, Wenbing Tao

Point cloud registration (PCR) involves determining a rigid transformation that aligns one point cloud to another.

Point Cloud Registration

No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation

no code implementations31 Mar 2023 Biwei Cao, Lulu Hua, Jiuxin Cao, Jie Gui, Bo Liu, James Tin-Yau Kwok

Different from popular methods which take full advantage of the propagation topology structure, in this paper, we propose a novel framework for fake news detection from perspectives of semantic, emotion and data enhancement, which excavates the emotional evolution patterns of news participants during the propagation process, and a dual deep interaction channel network of semantic and emotion is designed to obtain a more comprehensive and fine-grained news representation with the consideration of comments.

Data Augmentation Fake News Detection

Fooling the Image Dehazing Models by First Order Gradient

1 code implementation30 Mar 2023 Jie Gui, Xiaofeng Cong, Chengwei Peng, Yuan Yan Tang, James Tin-Yau Kwok

In this paper, we focus on designing a group of attack methods based on first order gradient to verify the robustness of the existing dehazing algorithms.

Adversarial Attack Image Dehazing +1

A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends

1 code implementation13 Jan 2023 Jie Gui, Tuo Chen, Jing Zhang, Qiong Cao, Zhenan Sun, Hao Luo, DaCheng Tao

Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance.

Self-Supervised Learning

Fast Online Hashing with Multi-Label Projection

1 code implementation3 Dec 2022 Wenzhe Jia, Yuan Cao, Junwei Liu, Jie Gui

When a new query arrives, only the binary codes of the corresponding potential neighbors are updated.

Retrieval

Exploring the Coordination of Frequency and Attention in Masked Image Modeling

1 code implementation28 Nov 2022 Jie Gui, Tuo Chen, Minjing Dong, Zhengqi Liu, Hao Luo, James Tin-Yau Kwok, Yuan Yan Tang

To tackle this issue, we propose the Frequency \& Attention-driven Masking and Throwing Strategy (FAMT), which can extract semantic patches and reduce the number of training patches to boost model performance and training efficiency simultaneously.

Attribute Representation Learning +1

AlignVE: Visual Entailment Recognition Based on Alignment Relations

no code implementations16 Nov 2022 Biwei Cao, Jiuxin Cao, Jie Gui, Jiayun Shen, Bo Liu, Lei He, Yuan Yan Tang, James Tin-Yau Kwok

Such approaches, however, ignore the VE's unique nature of relation inference between the premise and hypothesis.

Question Answering Relation +2

Feedback Pyramid Attention Networks for Single Image Super-Resolution

no code implementations13 Jun 2021 Huapeng Wu, Jie Gui, Jun Zhang, James T. Kwok, Zhihui Wei

Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement.

Image Super-Resolution

Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution

no code implementations13 Jun 2021 Huapeng Wu, Jie Gui, Jun Zhang, James T. Kwok, Zhihui Wei

Recently, deep convolutional neural network methods have achieved an excellent performance in image superresolution (SR), but they can not be easily applied to embedded devices due to large memory cost.

Image Super-Resolution

A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning

1 code implementation7 Jun 2021 Jie Gui, Xiaofeng Cong, Yuan Cao, Wenqi Ren, Jun Zhang, Jing Zhang, Jiuxin Cao, DaCheng Tao

With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed.

Image Dehazing Single Image Dehazing

Learning Rates for Multi-task Regularization Networks

no code implementations1 Apr 2021 Jie Gui, Haizhang Zhang

Multi-task learning is an important trend of machine learning in facing the era of artificial intelligence and big data.

BIG-bench Machine Learning Multi-Task Learning

Delving into Variance Transmission and Normalization: Shift of Average Gradient Makes the Network Collapse

1 code implementation22 Mar 2021 Yuxiang Liu, Jidong Ge, Chuanyi Li, Jie Gui

We propose Parametric Weights Standardization (PWS), a fast and robust to mini-batch size module used for conv filters, to solve the shift of the average gradient.

Persuasiveness

Randomized Kernel Multi-view Discriminant Analysis

no code implementations2 Apr 2020 Xiaoyun Li, Jie Gui, Ping Li

In this paper, we propose the kernel version of multi-view discriminant analysis, called kernel multi-view discriminant analysis (KMvDA).

Object Recognition

Deep Human Answer Understanding for Natural Reverse QA

no code implementations1 Dec 2019 Rujing Yao, Linlin Hou, Lei Yang, Jie Gui, Qing Yin, Ou wu

This study focuses on a reverse question answering (QA) procedure, in which machines proactively raise questions and humans supply the answers.

Question Answering

Multi-grained Attention Networks for Single Image Super-Resolution

no code implementations26 Sep 2019 Huapeng Wu, Zhengxia Zou, Jie Gui, Wen-Jun Zeng, Jieping Ye, Jun Zhang, Hongyi Liu, Zhihui Wei

In this paper, we make a thorough investigation on the attention mechanisms in a SR model and shed light on how simple and effective improvements on these ideas improve the state-of-the-arts.

Feature Importance Image Super-Resolution

AHash: A Load-Balanced One Permutation Hash

1 code implementation25 Sep 2019 Chenxingyu Zhao, Jie Gui, Yixiao Guo, Jie Jiang, Tong Yang, Bin Cui, Gong Zhang

Unlike the densification to fill the empty bins after they undesirably occur, our design goal is to balance the load so as to reduce the empty bins in advance.

Fast Supervised Discrete Hashing

no code implementations7 Apr 2019 Jie Gui, Tongliang Liu, Zhenan Sun, DaCheng Tao, Tieniu Tan

Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm.

regression

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