Fei Li

Universitat Politècnica de Catalunya

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Liangquan Ge

Chengdu University of Technology

Rujie Liu

Fujitsu (China)

Zhixing Gu

Chengdu University of Technology

Schahram Dustdar

TU Wien

Guo‐Qiang Zeng

Wenzhou University

Sanjin Sehic

TU Wien

Qingao Qin

Chengdu University of Technology

Liangquan Ge

Chengdu University of Technology

Zeng Guoqiang

Chengdu University of Technology

Qiong Cao

Hunan Agricultural University

Cooperative Institutions

Pontifical Catholic University of Rio de Janeiro

267

Universidade Estadual de Londrina

261

A. Alikhanyan National Laboratory

260

Beijing Normal University

252

Université Libre de Bruxelles

245

Institute of High Energy Physics

235

Chinese Academy of Sciences

222

China Institute of Atomic Energy

180

China Academy of Space Technology

172

Federico Santa María Technical University

137

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The Safety Research for Face Recognition System Based on Image Feature and Digital Watermarking

Applied Mechanics and Materials (2012)

Fei Li Yuan Yuan Wang

Abstract: In order to solve the easily copied problem of images in face recognition software, an algorithm combining the image feature with digital watermark is presented in this paper. As watermark information, image feature of the adjacent blocks are embedded to the face image. And primitive face images are not needed when recovering the watermark. So face image integrity can be well confirmed, and the algorithm can detect whether the face image is the original one and identify whether the face image is attacked by malicious aim-such as tampering, replacing or illegally adding. Experimental results show that the algorithm with good invisibility and excellent robustness has no interference on face recognition rate, and it can position the specific tampered location of human face image.

Invisibility

Feature (linguistics)

Robustness

Three-dimensional face recognition

10.4028/www.scientific.net/amm.224.485

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FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing

Findings of the Association for Computational Linguistics: ACL 2022 (2023)

Zhuang Li Yuyang Chai Terry Yue Zhuo Lizhen Qu Gholamreza Haffari

Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval. However, existing scene graph parsers that convert image captions into scene graphs often suffer from two types of errors. First, the generated scene graphs fail to capture the true semantics of the captions or the corresponding images, resulting in a lack of faithfulness. Second, the generated scene graphs have high inconsistency, with the same semantics represented by different annotations.To address these challenges, we propose a novel dataset, which involves re-annotating the captions in Visual Genome (VG) using a new intermediate representation called FACTUAL-MR. FACTUAL-MR can be directly converted into faithful and consistent scene graph annotations. Our experimental results clearly demonstrate that the parser trained on our dataset outperforms existing approaches in terms of faithfulness and consistency. This improvement leads to a significant performance boost in both image caption evaluation and zero-shot image retrieval tasks. Furthermore, we introduce a novel metric for measuring scene graph similarity, which, when combined with the improved scene graph parser, achieves state-of-the-art (SOTA) results on multiple benchmark datasets for the aforementioned tasks.

Scene graph

Benchmark (surveying)

Representation

10.18653/v1/2023.findings-acl.398

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Citations (3)

Contents list

Nanoscale (2024)

Yangwen Liu Guanghuan Li Huan Li Sheng Cao Xia Chen

Permissions Request permissions Contents list Nanoscale, 2024, 16, 493 DOI: 10.1039/D4NR90006E This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content.

Acknowledgement

10.1039/d4nr90006e

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A Quantitative Spectra Analysis Framework Combining Mixup and Band Attention for Predicting Soluble Solid Content of Blueberries

Lecture notes in computer science (2023)

Zhaokui Li Jinen Zhang Wei Li Fei Li Ke Xin Bi

Interpretability

Sample (material)

10.1007/978-3-031-40292-0_30

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REP-YOLOX: An Efficient Model for Defect Detection in Gas Insulated Switchgear Equipment

2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE) (2023)

Fei Li Xuejie Yang Hui Gao Zengwei Yue Jianfeng Yu

Gas Insulated Switchgear (GIS) equipment is an important substation device in the power system and plays an indispensable role in maintaining the operation of the power system. However, GIS equipment is prone to ablation defects and some foreign objects during long-term operation, thus affecting the normal operation of the power system. Aiming at the problems of low efficiency and easy-to-miss detection in manual inspection.This paper proposes a single-stage object detection model REP-YOLOX, which is based on structural re-parameterization and attention mechanisms technology. Firstly, we combine dilated convolution and standard convolution to extract features and obtain larger receptive fields. Then, in the Neck layer of the model, the Mask Conv block module is used to enhance the feature extraction ability under occlusion. Finally, the Transformer module is used in the prediction layer of the model to weigh the global features and predict the results through the full connection layer. Experimental results show that the model can achieve 99.14% mAP50 in the GIS equipment defect detection task, which indicates it can complete GIS equipment defect detection task well.

Switchgear

Convolution (computer science)

10.1109/cacre58689.2023.10208561

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Optimal linear interpolation coding for server-based computing

Fei Li Jason Nieh

Due to its reduced administrative costs and better resource utilization, server-based computing (SBC) is becoming a popular approach for delivering computational services across a network. In SBC, all application processing is done on servers while only screen updates are sent to clients. While many SBC encoding techniques have been explored for transmitting screen updates efficiently, existing approaches do not effectively support multimedia applications. To address this problem, we propose optimal linear interpolation (OLI), a new pixel-based SBC screen update coding algorithm. With OLI, the server selects and transmits only a small sample of pixels to represent a screen update. The client recovers the complete screen update from these samples using piecewise linear interpolation to achieve the best visual quality. OLI can be used to provide lossless or lossy compression for an adaptive trade-off between network bandwidth and processing time requirements. We further propose and evaluate 2D lossless linear interpolation (2DLI), which is based on OLI but additionally provides lower encoding complexity for lossless compression. Our experimental results show that when compared with other compression methods, 2DLI provides good data compression ratio with modest computational overhead, for both servers and clients.

Lossy compression

10.1109/icc.2002.997301

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Citations (10)

Evaluation on nuclear emergency response strategies in the Asia-Pacific region

International Journal of Critical Infrastructure Protection (2021)

Fei Li Wang Jing Hui Li Qian Hu Wenxuan Dan

Emergency Response

Fukushima Nuclear Accident

Nuclear Energy Policy

10.1016/j.ijcip.2021.100447

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Citations (7)

Measurement error of spatial frequency response based on sinusoidal Siemens Star

AOPC 2020: Optical Sensing and Imaging Technology (2020)

Runjia Yang Fei Li Baoyu Hong Zhixiong Hu

In recent years, sinusoidal Siemens stars have been widely used as a method of measuring spatial frequency response (SFR). Previous researches pointed out that incorrect recognition of the Siemens star center could result in errors in SFR measurement, and these errors are more significant at high frequencies. To reduce the errors, this paper proposed two methods to correct the center of Siemens star. By calibrating various Siemens star centers with the proposed methods, it is found that both the two methods are highly consistent, with a difference of no more than 2 pixels.

Siemens

Star (game theory)

10.1117/12.2580250

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Citations (1)

Multilevel Pyramid Network for Monocular Depth Estimation Based on Feature Refinement and Adaptive Fusion

Electronics (2022)

Huihui Xu Fei Li

As a traditional computer vision task, monocular depth estimation plays an essential role in novel view 3D reconstruction and augmented reality. Convolutional neural network (CNN)-based models have achieved good performance for this task. However, in the depth map recovered by some existing deep learning-based methods, local details are still lost. To generate convincing depth maps with rich local details, this study proposes an efficient multilevel pyramid network for monocular depth estimation based on feature refinement and adaptive fusion. Specifically, a multilevel spatial feature generation scheme is developed to extract rich features from the spatial branch. Then, a feature refinement module that combines and enhances these multilevel contextual and spatial information is designed to derive detailed information. In addition, we design an adaptive fusion block for improving the capability of fully connected features. The performance evaluation results on public RGBD datasets indicate that the proposed approach can recover reasonable depth outputs with better details and outperform several depth recovery algorithms from a qualitative and quantitative perspective.

Pyramid (geometry)

Feature (linguistics)

Monocular

Depth map

10.3390/electronics11162615

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Citations (4)

An automatic vehicle detection method based on traffic videos

Qiong Cao Rujie Liu Fei Li Yuehong Wang

A vision-based vehicle detection method is presented in this paper. The proposed method is composed of two steps, i.e., hypothesis generation and hypothesis verification. An adaptive background modeling and updating method is proposed to detect foreground regions in video sequences. With the prior knowledge of the vehicle appearance, the possible vehicle locations are extracted from the foreground regions and the touched vehicles are separated. Finally, hypothesized regions are verified by comparing their appearances with vehicle model. The performance of the proposed method is verified on videos captured under versatile conditions, and good results are achieved even in heavy traffic conditions.

Foreground detection

10.1109/icip.2010.5653674

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