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FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge 38

FreqBlender: Enhancing DeepFake Detection by Blending Freque...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Li, Hanzhe Zhou, Jiaran Li, Yuezun Wu, Baoyuan Li, Bin Dong, Junyu School of Computer Science and Technology Ocean University of China China School of Data Science The Chinese University of Hong Kong Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

Generating synthetic fake faces, known as pseudo-fake faces, is an effective way to improve the generalization of DeepFake detection. Existing methods typically generate these faces by blending real or fake faces in spatial domain. While these methods have shown promise, they overlook the simulation of frequency distribution in pseudo-fake faces, limiting the learning of generic forgery traces in-depth. To address this, this paper introduces FreqBlender, a new method that can generate pseudo-fake faces by blending frequency knowledge. Concretely, we investigate the major frequency components and propose a Frequency Parsing Network to adaptively partition frequency components related to forgery traces. Then we blend this frequency knowledge from fake faces into real faces to generate pseudo-fake faces. Since there is no ground truth for frequency components, we describe a dedicated training strategy by leveraging the inner correlations among different frequency knowledge to instruct the learning process. Experimental results demonstrate the effectiveness of our method in enhancing DeepFake detection, making it a potential plug-and-play strategy for other methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Material Decomposition of Dual-Energy CT Based on CBAM Attention U-Net 4

Material Decomposition of Dual-Energy CT Based on CBAM Atten...

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2022 4th International Conference on Artificial Intelligence and Computer Science, AICS 2022

作者： Zhang, Xinrui Li, Lei Yan, Bin Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering University Henan Zhengzhou China

Spectral CT has the ability of quantitative material analysis by exploring the difference in the attenuation properties under different X-ray energies, and has become an important direction for the development of CT technology. However, due to the non-linearity and inconsistency of the observation, there is noise boosting in material decomposition and serious decrease of basis material images. In this paper, we design a U-Net convolutional neural network structure with double-entry and double-out to realize the effective material decomposition of high- and low-energy images. When designing the network, we introduce the attention mechanism, and add CBAM modules combining channel attention and spatial attention to improve the overall performance of the network. We also design an ablation experiment to test the module from a vertical perspective, and compare the material decomposition performance of each network from a horizontal perspective. The results show that the proposed Attention U-Net (AT-U-Net) has advantages in reducing noise boosting and improving the quality of basis material images. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Convolutional neural networks

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CSCL-DTI: Predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: Predicting drug-target interaction through cross-v...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xuan Zhang, Xi Yu, Zu-Guo Long, Yahui Zeng, Xiangxiang Yu, Philip S. Xiangtan University College of Computer Science China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing Ministry of Education China Singapore Hunan University College of Information Science and Engineering China University of Illinois Chicago Department of Computer Science United States

ISBN: (纸本)9798350386226

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI1 outperforms six state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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Initial Results of CT Volume Metal Artifacts Reduction based on 3D U-net 2022

Initial Results of CT Volume Metal Artifacts Reduction based...

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2nd International Conference on Bioinformatics and intelligent Computing, BIC 2022

作者： Zhu, Linlin Han, Yu Xi, Xiaoqi Li, Lei Yan, Bin Henan Key Laboratory of Imaging and Intelligent Processing Pla Strategic Support Force Information Engineering University Zhengzhou45000 China

ISBN: (纸本)9781450395755

Computed tomography (CT) allows rapid acquisition of information about the internal three-dimensional (3D) structure of the scanned object. However, when the scanned object contains metal implants, it will result in metal artifacts in the reconstructed images. Metal artifacts seriously interfere with the analysis and judgment of CT images. To solve the problem of metal artifacts in CT volumes, this paper introduces 3D U-net into the metal artifact reduction of CT volume data. The 3D convolution in 3D U-net can make full use of the spatial and structural information of CT volumes, and can better maintain the internal structural information based on effectively reducing the metal artifacts. The direct processing for 3D data can improve the stability and continuity of the corrected image in all dimensions. After artifact reduction for 3D data, the information of arbitrary spatial coordinate points in the image can be obtained more accurately and effectively. To verify the performance of the algorithm, experiments were conducted using medical datasets. The results show that the method in this paper can effectively suppress metal artifacts while obtaining better stability and continuity in all dimensions. © 2022 ACM.

关键词： Computerized tomography

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Dynamic Negative Example Construction for Grammatical Error Correction Using Contrastive Learning 21

Dynamic Negative Example Construction for Grammatical Error ...

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21st China National Conference on Computational Linguistics, CCL 2022

作者： He, Junyi Zhuang, Junbin Li, Xia Guangzhou Key Laboratory of Multilingual Intelligent Processing School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9783031183140

Grammatical error correction (GEC) aims at correcting texts with different types of grammatical errors into natural and correct forms. Due to the difference of error type distribution and error density, current grammatical error correction systems may over-correct writings and produce a low precision. To address this issue, in this paper, we propose a dynamic negative example construction method for grammatical error correction using contrastive learning. The proposed method can construct sufficient negative examples with diverse grammatical errors, and can be dynamically used during model training. The constructed negative examples are beneficial for the GEC model to correct sentences precisely and suppress the model from over-correction. Experimental results show that our proposed method enhances model precision, proving the effectiveness of our method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Error correction

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Research on electricity forecast of large industrial consumers based on combination model 9

Research on electricity forecast of large industrial consume...

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9th International Conference on Energy Materials and Electrical Engineering, ICEMEE 2023

作者： Wu, Qi Wang, Dong-Tao Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9781510672765

This paper investigates the application of neural network formulas, supported by statistical basic analysis techniques and machine learning, to forecast the monthly electricity consumption of large industrial consumers in a specific province. Firstly, we utilize the SARIMA model, GM (1,1) model, and LSTM neural network model to individually forecast electricity consumption based on data collected from January 2016 to December 2022. Subsequently, we combine these three models using a weighted technique to create a comprehensive forecasting model. Finally, we evaluate the performance of the model by assessing the maximum prediction error and employing the MAPE analysis criteria. The results demonstrate that the combined model outperforms the alternative models, indicating its superior prediction accuracy. Therefore, the combined model can be effectively employed as a forecasting technique for large industrial consumers in the region to predict electricity consumption for the 12-month period of 2023. This approach presents a novel method for predicting electricity consumption among industrial users in this sector. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Data modeling Power consumption Autoregressive models Education and training Neural networks Performance modeling Power grids Statistical analysis Statistical modeling Sustainability

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Automatic Measurement of Structural Parameters of Irregular Slender Strip with Bifurcated Crack Based on Morphology 43

Automatic Measurement of Structural Parameters of Irregular ...

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43rd Chinese control Conference, CCC 2024

作者： Zeng, Fanlin Xiang, Qiulin Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin300222 China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin300222 China

ISBN: (纸本)9789887581581

In this paper, an automatic measurement method for the length and width information of irregular slender strip with bifurcated structures is proposed. This method is based on morphology, performing pixel extraction and computation, and has the characteristics of fully considering structural features in the measurement process, being unaffected by bifurcation interference, and outputting quantitative measurement results. By utilizing distance transformation, skeleton extraction, topological structure construction of nodes, generation of connection graph models, calculation of the longest path, and superimposition of distance information, the length and width of irregular structures are obtained. After practical application of this method to image measurement cases, it is evident that this method can effectively obtain key structural information of irregular slender strip bifurcated structures. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Musculoskeletal system

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PLIP: Language-Image Pre-training for Person Representation Learning 38

PLIP: Language-Image Pre-training for Person Representation ...

引用

38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Zuo, Jialong Hong, Jiahao Zhang, Feng Yu, Changqian Zhou, Hanyu Gao, Changxin Sang, Nong Wang, Jingdong National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Skywork AI United States Department of Computer Vision Baidu Inc. China

Language-image pre-training is an effective technique for learning powerful representations in general domains. However, when directly turning to person representation learning, these general pre-training methods suffer from unsatisfactory performance. The reason is that they neglect critical person-related characteristics, i.e., fine-grained attributes and identities. To address this issue, we propose a novel language-image pre-training framework for person representation learning, termed PLIP. Specifically, we elaborately design three pretext tasks: 1) Text-guided Image Colorization, aims to establish the correspondence between the person-related image regions and the fine-grained color-part textual phrases. 2) Image-guided Attributes Prediction, aims to mine fine-grained attribute information of the person body in the image;and 3) Identity-based Vision-Language Contrast, aims to correlate the cross-modal representations at the identity level rather than the instance level. Moreover, to implement our pre-train framework, we construct a large-scale person dataset with image-text pairs named SYNTH-PEDES by automatically generating textual annotations. We pre-train PLIP on SYNTH-PEDES and evaluate our models by spanning downstream person-centric tasks. PLIP not only significantly improves existing methods on all these tasks, but also shows great ability in the zero-shot and domain generalization settings. © 2024 Neural information processing systems foundation. All rights reserved.

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Scattering Characteristics-guided Self-supervised Learning for Target Classification in SAR Images 24

Scattering Characteristics-guided Self-supervised Learning f...

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7th International Conference on Image and Graphics processing, ICIGP 2024

作者： Zhong, Honghu Li, Jianhao Shi, Hao Fang, Zhonghao Chen, Liang Institute of Radar Technology Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing China Innovation Center of Beijing Institute of Technology Chongqing Chongqing401120 China

ISBN: (纸本)9798400716720

With the advancement of science and technology, the spatial resolution of spaceborne Synthetic Aperture Radar (SAR) images has achieved sub-meter precision, enabling the classification and identification of targets. Notably, the rapid and accurate classification of aircraft targets using SAR images has become a significant application requirement. Nevertheless, challenges persist in the classification of targets within aircraft remote sensing images. This paper introduces a novel method aimed at enhancing the scattering characteristics in SAR images to address issues associated with discrete imaging pixels, strong scattering characteristics, and the consequent difficulty in distinguishing aircraft features. The proposed method systematically correlates the discrete SAR image scattering characteristics, thereby facilitating the extraction of characteristic information pertaining to the edge contours of aircraft targets. Recognizing the limitations of traditional supervised classification methods, which often demand substantial manually labeled information, this study integrates self-supervised contrastive learning to mitigate labeling costs. Additionally, acknowledging the imbalanced distribution of samples across different categories and the prevalent "long tail"effect, a weighted loss function is introduced to rectify the imbalance and enhance the network's focus on the learning of underrepresented samples. The efficacy of the proposed method is evaluated using a self-established dataset. The results demonstrate a 1.48% increase in accuracy compared to the original self-supervised method, indicating an improvement in the classification performance for categories characterized by an imbalanced sample distribution. © 2024 ACM.

关键词： Image classification

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Multi-scale Traffic Camera Image Detection Network Based on Improved YOLOv8 21st

Multi-scale Traffic Camera Image Detection Network Based on...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Peng, Zhihao Qi, Xinyuan Wu, Sheng Shang, Jianga Yang, Linquan China University of Geosciences Wuhan430074 China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan430074 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789819601240

With the extensive deployment of traffic cameras across cities, utilizing these devices for vehicle detection has become essential for efficient traffic management. However, variations in vehicle sizes and distances between vehicles and cameras lead to inconsistent scaling of vehicles in the images, which presents a challenge for feature extraction. Furthermore, conventional object detection algorithms fail to fully utilize the rich feature information across multiple layers. To solve these issues, we develop a novel and advanced multi-scale traffic YOLO (MT-YOLO), based on the YOLOv8 model. Firstly, we propose the spatial multi-scale (SMS) attention mechanism to improve the extraction capability of spatial multi-scale vehicle features and aggregate these features for dynamic spatial modulation. Secondly, we develop the multi-scale dilation transposition CARAFE (MDT-CARAFE) upsampling method, which utilizes dilated convolutions to enhance the receptive field at different scales during the upsampling process. Lastly, we augment the network with a detection head and replace the original detection head with the adaptively spatial feature fusion(ASFF) detection head, which dynamically selects and fuses multi-layer feature maps, enabling the network to better capture multi-scale information. Our experiments demonstrate that the MT-YOLO model greatly improves results on the traffic camera detection dataset., achieving an mAP@0.5 of 80.9%, with a precision of 92% and recall of 93%. These results surpass the performance of leading detection algorithms. In addition, the model takes only 12.5 milliseconds to complete an inference, which makes it highly deployable and valuable for real-world applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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