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Your search keyword '"Lin, Dazhen"' showing total 119 results
Start Over Author "Lin, Dazhen" Publication Type Periodicals
119 results on '"Lin, Dazhen"'

1. A Fine-Grained Image Description Generation Method Based on Joint Objectives

Author

Zhang, Yifan, Lin, Chunzhen, Cao, Donglin, and Lin, Dazhen

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

The goal of fine-grained image description generation techniques is to learn detailed information from images and simulate human-like descriptions that provide coherent and comprehensive textual details about the image content. Currently, most of these methods face two main challenges: description repetition and omission. Moreover, the existing evaluation metrics cannot clearly reflect the performance of models on these two issues. To address these challenges, we propose an innovative Fine-grained Image Description Generation model based on Joint Objectives. Furthermore, we introduce new object-based evaluation metrics to more intuitively assess the model's performance in handling description repetition and omission. This novel approach combines visual features at both the image level and object level to maximize their advantages and incorporates an object penalty mechanism to reduce description repetition. Experimental results demonstrate that our proposed method significantly improves the CIDEr evaluation metric, indicating its excellent performance in addressing description repetition and omission issues.

Published
2023

2. Comparative Evaluation of Recent Universal Adversarial Perturbations in Image Classification

Author

Weng, Juanjuan, Luo, Zhiming, Lin, Dazhen, and Li, Shaozi

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The vulnerability of Convolutional Neural Networks (CNNs) to adversarial samples has recently garnered significant attention in the machine learning community. Furthermore, recent studies have unveiled the existence of universal adversarial perturbations (UAPs) that are image-agnostic and highly transferable across different CNN models. In this survey, our primary focus revolves around the recent advancements in UAPs specifically within the image classification task. We categorize UAPs into two distinct categories, i.e., noise-based attacks and generator-based attacks, thereby providing a comprehensive overview of representative methods within each category. By presenting the computational details of these methods, we summarize various loss functions employed for learning UAPs. Furthermore, we conduct a comprehensive evaluation of different loss functions within consistent training frameworks, including noise-based and generator-based. The evaluation covers a wide range of attack settings, including black-box and white-box attacks, targeted and untargeted attacks, as well as the examination of defense mechanisms. Our quantitative evaluation results yield several important findings pertaining to the effectiveness of different loss functions, the selection of surrogate CNN models, the impact of training data and data size, and the training frameworks involved in crafting universal attackers. Finally, to further promote future research on universal adversarial attacks, we provide some visualizations of the perturbations and discuss the potential research directions., Comment: 18 pages,8 figures, 7 tables

Published
2023

3. Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature

Author

Weng, Juanjuan, Luo, Zhiming, Lin, Dazhen, Li, Shaozi, and Zhong, Zhun

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Cryptography and Security
Abstract

Recent research has shown that Deep Neural Networks (DNNs) are highly vulnerable to adversarial samples, which are highly transferable and can be used to attack other unknown black-box models. To improve the transferability of adversarial samples, several feature-based adversarial attack methods have been proposed to disrupt neuron activation in the middle layers. However, current state-of-the-art feature-based attack methods typically require additional computation costs for estimating the importance of neurons. To address this challenge, we propose a Singular Value Decomposition (SVD)-based feature-level attack method. Our approach is inspired by the discovery that eigenvectors associated with the larger singular values decomposed from the middle layer features exhibit superior generalization and attention properties. Specifically, we conduct the attack by retaining the decomposed Top-1 singular value-associated feature for computing the output logits, which are then combined with the original logits to optimize adversarial examples. Our extensive experimental results verify the effectiveness of our proposed method, which can be easily integrated into various baselines to significantly enhance the transferability of adversarial samples for disturbing normally trained CNNs and advanced defense strategies. The source code of this study is available at https://github.com/WJJLL/SVD-SSA

Published
2023

4. A Fine-Grained Image Description Generation Method Based on Joint Objectives

Author

Zhang, Yifan, Lin, Chunzhen, Cao, Donglin, Lin, Dazhen, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Wang, Tong, editor, Fan, Hongfei, editor, Liu, Dongning, editor, and Du, Bowen, editor

Published
2024
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5. Text Causal Discovery Based on Sequence Structure Information

Author

Li, Yue, Cao, Donglin, Lin, Dazhen, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Qingshan, editor, Wang, Hanzi, editor, Ma, Zhanyu, editor, Zheng, Weishi, editor, Zha, Hongbin, editor, Chen, Xilin, editor, Wang, Liang, editor, and Ji, Rongrong, editor

Published
2024
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6. Multimodal Causal Relations Enhanced CLIP for Image-to-Text Retrieval

Author

Feng, Wenjun, Lin, Dazhen, Cao, Donglin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Qingshan, editor, Wang, Hanzi, editor, Ma, Zhanyu, editor, Zheng, Weishi, editor, Zha, Hongbin, editor, Chen, Xilin, editor, Wang, Liang, editor, and Ji, Rongrong, editor

Published
2024
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14. Video Rumor Classification Based on Multi-modal Theme and Keyframe Fusion

Author

You, Jinpeng, Lin, Yanghao, Lin, Dazhen, Cao, Donglin, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Guo, Yinzhang, editor, Song, Xiaoxia, editor, Fan, Hongfei, editor, Liu, Dongning, editor, Gao, Liping, editor, and Du, Bowen, editor

Published
2023
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17. Attention and Multi-granied Feature Learning for Baggage Re-identification

Author

Wu, Huangbin, Luo, Zhiming, Cao, Donglin, Lin, Dazhen, Su, Songzhi, Li, Shaozi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Cao, Buqing, editor, Fan, Hongfei, editor, Liu, Dongning, editor, Du, Bowen, editor, and Gao, Liping, editor

Published
2022
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18. An Improved SSD-Based Gastric Cancer Detection Method

Author

Liu, Minggui, Luo, Zhiming, Cao, Donglin, Lin, Dazhen, Su, Songzhi, Li, Shaozi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Cao, Buqing, editor, Fan, Hongfei, editor, Liu, Dongning, editor, Du, Bowen, editor, and Gao, Liping, editor

Published
2022
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19. A Semi-supervised Video Object Segmentation Method Based on Adaptive Memory Module

Author

Yang, Shaohua, Luo, Zhiming, Cao, Donglin, Lin, Dazhen, Su, Songzhi, Li, Shaozi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Cao, Buqing, editor, Fan, Hongfei, editor, Liu, Dongning, editor, Du, Bowen, editor, and Gao, Liping, editor

Published
2022
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20. Sentiment Analysis of Chinese Complex Long Sentences Based on Reinforcement Learning

Author

Zhang, Chaoli, Yu, Zhenjie, Lin, Dazhen, Cao, Donglin, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Cao, Buqing, editor, Fan, Hongfei, editor, Liu, Dongning, editor, Du, Bowen, editor, and Gao, Liping, editor

Published
2022
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21. Syntactic Dependency Constraint Based Graph Convolutional Network for Aspect Level Sentiment Classification

Author

You, Jinpeng, Lin, Dazhen, Cao, Donglin, Chao, Fei, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Jansen, Thomas, editor, Jensen, Richard, editor, Mac Parthaláin, Neil, editor, and Lin, Chih-Min, editor

Published
2022
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22. Law Article Prediction via a Codex Enhanced Multi-task Learning Framework

Author

Liu, Bingjun, Luo, Zhiming, Lin, Dazhen, Cao, Donglin, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Liu, Dongning, editor, Liao, Hao, editor, Fan, Hongfei, editor, and Gao, Liping, editor

Published
2021
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31. Text Generation from Triple via Generative Adversarial Nets

Author

Chen, Xiangyan, Lin, Dazhen, Cao, Donglin, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Founding Editor, Sun, Yuqing, editor, Lu, Tun, editor, Yu, Zhengtao, editor, Fan, Hongfei, editor, and Gao, Liping, editor

Published
2019
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34. Traffic Condition Analysis Based on Users Emotion Tendency of Microblog

Author

Wang, Shuru, Cao, Donglin, Lin, Dazhen, Chao, Fei, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Chao, Fei, editor, Schockaert, Steven, editor, and Zhang, Qingfu, editor

Published
2018
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35. Sentiment Analysis Model Based on Structure Attention Mechanism

Author

Lin, Kai, Lin, Dazhen, Cao, Donglin, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Chao, Fei, editor, Schockaert, Steven, editor, and Zhang, Qingfu, editor

Published
2018
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41. Learning multi‐organ and tumor segmentation from partially labeled datasets by a conditional dynamic attention network.

Author

Li, Lei, Lian, Sheng, Lin, Dazhen, Luo, Zhiming, Wang, Beizhan, and Li, Shaozi

Subjects
DEEP learning, CLINICAL medicine, TUMORS
Abstract

Summary: Multi‐organ segmentation is a critical prerequisite for many clinical applications. Deep learning‐based approaches have recently achieved promising results on this task. However, they heavily rely on massive data with multi‐organ annotated, which is labor‐ and expert‐intensive and thus difficult to obtain. In contrast, single‐organ datasets are easier to acquire, and many well‐annotated ones are publicly available. It leads to the partially labeled issue: How to learn a unified multi‐organ segmentation model from several single‐organ datasets? Pseudo‐label‐based methods and conditional information‐based methods make up the majority of existing solutions, where the former largely depends on the accuracy of pseudo‐labels, and the latter has a limited capacity for task‐related features. In this paper, we propose the Conditional Dynamic Attention Network (CDANet). Our approach is designed with two key components: (1) multisource parameter generator, fusing the conditional and multiscale information to better distinguish among different tasks, and (2) dynamic attention module, promoting more attention to task‐related features. We have conducted extensive experiments on seven partially labeled challenging datasets. The results show that our method achieved competitive results compared with the advanced approaches, with an average Dice score of 75.08%. Additionally, the Hausdorff Distance is 26.31, which is a competitive result. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Dual-Stream Transformer With Distribution Alignment for Visible-Infrared Person Re-Identification

Author

Chai, Zehua, Ling, Yongguo, Luo, Zhiming, Lin, Dazhen, Jiang, Min, and Li, Shaozi

Abstract

Visible-infrared person re-identification(VI-ReID) aims to match the person images captured by visible and infrared cameras and suffers from severe cross-modality discrepancy and intra-modality variations. Existing approaches mainly use convolution neural network (CNN)-based architectures to extract pedestrian features, which fail to capture the long-range dependencies within an image. In addition, previous works usually attempt to bridge the modality gap by using adversarial learning to generate style-consistent images or designing different feature-level metric learning constraints. However, few works consider the cross-modality disparity from the perspective of assessing overall distance distribution discrepancy. To address these problems, we design a pure Transformer-based Visible-Infrared (TransVI) network with a conventional two-stream structure, which can explicitly capture modality-specific representations and learn multi-modality sharable knowledge. TransVI can efficiently address the lack of global dependency in CNN-based architectures due to the multi-head self-attention modules in the transformer, which allows us to capture the long-range dependencies of pedestrian images. Furthermore, we introduce the Cross-Modality Dissimilarity-based Maximum Mean Discrepancy (CMD-MMD) constraint to handle the cross-modality discrepancy at the distance distribution level. Specifically, CMD-MMD leverages intra-modality distribution separability to guide inter-modality distribution separability learning, aligning pair-wise distance distributions of intra- and inter-modality for within-class and between-class, respectively. In this way, the distance distributions of intra- and inter-modality become more similar, significantly mitigating the cross-modality discrepancy and learning more modality invariant representations. Extensive experimental results on two public VI-ReID datasets confirm that our proposed framework can achieve state-of-the-art performance.

Published
2023
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47. A Novel Image Annotation Feedback Model Based on Internet-Search

Author

Yu, JianSong, Cao, DongLin, Li, ShaoZi, Lin, DaZhen, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Fu Lee, editor, Lei, Jingsheng, editor, Gong, Zhiguo, editor, and Luo, Xiangfeng, editor

Published
2012
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50. A Collaborative Multimedia Editing System Based on Shallow Nature Language Parsing

Author

Cao, Donglin, Lin, Dazhen, Li, Shaozi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Luo, Yuhua, editor

Published
2006
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