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Adversarial Training and Cross-modal Feature Fusion in Multimodal Sentiment Analysis

Adversarial Training and Cross-modal Feature Fusion in Multi...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Junhuai Li Chuang Lin Huaijun Wang Yuxing Zhi Jing Chen Tao Huang School of Computer Science and Engineering Xi’an University of Technology China Intelligent Computing and Communications (ICC) Lab James Cook University Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal sentiment analysis recognizes emotions through text, audio, and visual modalities, but data incompleteness is a major challenge. Existing methods often focus on specific types of deficiencies and perform poorly when multiple types of noise are present simultaneously. To address this issue, we propose a noise-prompted adversarial training framework with a multimodal interaction model to enhance the model’s robustness to missing modalities. The model first extracts common and unique features from each modality using a BERT text encoder and a shared-private encoder. Correlation measurements are then used to calculate the similarity between modalities, and a weighting mechanism is applied to the shared features. These features are deeply fused using a Transformer, and adversarial training combined with semantic reconstruction supervision helps the model learn a unified representation of noisy and clean data. Experimental results show that this method significantly improves the performance of multimodal sentiment analysis.

关键词： Training Weight measurement Sentiment analysis Analytical models Adaptation models visualization Feature extraction Transformers Robustness Noise measurement

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A Multi-Signal Perception Network for Textile Composition Identification

A Multi-Signal Perception Network for Textile Composition Id...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Bo Peng Liren He Dong Wu Mingmin Chi Jintao Chen School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Zhongshan Fudan Joint Innovation Center Zhongshan PoolNet Technology Co. Ltd Zhongshan China Zhengzhou Zhongke Institute of Integrated Circuit and System Application Shanghai Fabric Eyes Artificial Intelligence Technology Co. Ltd Shanghai China

Textile composition identification (TCI) is an essential basic link in the textile industry. Methods based on computer vision or near-infrared (NIR) signal processing have shown potential for the nondestructive TCI task. However, these methods ignore that the integration of NIR signals and visual information may help the model learn a better representation through information complementarity. This paper propose a Multi-Signal Perception Network (MSPNet) for nondestructive textile composition identification, allowing the model to benefit from the advantages of multimodal data. Firstly, a two-way feature extraction network is used to obtain multi-modal features. After that, we propose a multimodal signal fusion module to control the aggregation granularity among multimodal data. Specifically, the target areas of the image are perceived by a target area perception module (TAP). Then a bi-gated aggregation (Bi-GFA) is designed to capture consistent semantic information from signal to image and image to signal. The quantitative and qualitative results of the proposed MSPNet are significantly improved compared to both single and multimodal approaches.

关键词： visualization Computer vision Semantics Signal processing Feature extraction Data models Task analysis

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Calibration of Evidential Mass Function for Trustworthy Information processing in Scene Graph Generation

Calibration of Evidential Mass Function for Trustworthy Info...

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ieee International conference on Fuzzy Systems (FUZZ-ieee)

作者： Lucie Kunitomo-Jacquin Ken Fukuda National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan

Scene graph generation consists of the prediction of triplets $$ to describe the content of an image. The current data sets for this task suffer from the incompleteness of the triplet annotations. On the one hand, t... 详细信息

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

Scene graph generation consists of the prediction of triplets $<$ subject, predicate, object $>$ to describe the content of an image. The current data sets for this task suffer from the incompleteness of the triplet annotations. On the one hand, the number of triplets is not exhaustive, and on the other, the granularity of predicates varies like $<$ person, $on$ , chair $>$ and $<$ per son, sitting on, chair $>$ . Most current models produce scores on the set of the possible predicates. This score is then directly used for prediction, classically by a pseudo-optimal expectation. The problem is that the set of possible predicates is regarded as a probability space despite predicates not being mutually exclusive, like $on$ and sitting on. This article reconsiders the representation information of scene graph generation and proposes an approach for predicate classification in the Belief function theory based on the Dirichlet multiclass calibration method coupled with hierarchical prior information that explicits the predicates relations. The Dirichlet calibration applied to the scores enables us to interpret the mass associated with a predicate as the probability of knowing nothing more than this predicate. Our experiments are carried out on the visual Genome dataset using scores estimated by an existing transformer-based scene graph generation model. These experiments show a decrease in log-loss after applying our method and a diminution of the evidential conflict with the ground-truth predicates. Finally, we illustrate the benefit of our evidential representation by applying a hierarchy-based evidential decision and comparing it to the baseline.

关键词： visualization Annotations Triples (Data structure) Genomics Information processing Transformers Linear programming

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Universal image Semantic Communication for Edge Network

Universal Image Semantic Communication for Edge Network

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Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), ieee International conference on

作者： Chenxin Yuan Haojiang Ye Yiming Miao School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

The increasing volume of data demands efficient communication systems for transmitting visual information across diverse application scenarios. Traditional methods based on Shannon's information theory overlook semantic content, leading to inefficient transmission of redundant data. Recent proposed deep learning-based semantic communication systems are often computationally intensive and domain-specific, limiting their practicality on edge devices and across various image types. We propose a universal image semantic communication (UISC) system - a lightweight, deep learning-based semantic communication framework for general-purpose image transmission. By processing low-frequency and high-frequency image components separately, our system reduces network complexity and enhances robustness to domain gaps. Unlike previous methods, UISC is versatile enough to handle RGB natural images, cartoons, text-embedded images, and grayscale medical images. Experimental results demonstrate that our method outperforms existing methods in both transmission accuracy and latency while consuming significantly less computational resources.

关键词： visualization Accuracy image edge detection image communication Process control Gray-scale Semantic communication Robustness X-ray imaging Testing

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The Principle, Method and Effect of imagery Practice in Swimming Teaching in Colleges and Universities 2

The Principle, Method and Effect of Imagery Practice in Swim...

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2nd International conference on Computers, Information processing and Advanced Education, CIPAE 2021

作者： Ma, Xiumei Shanghai Urban Construction Vocational College Basic Teaching Department Shanghai China

ISBN: (纸本)9781665426657

The difficulties of swimming teaching in Colleges and universities are unfamiliar water environment and unaccustomed horizontal posture, and the formation of skills depends on the body's proprioception. Appearance can get rid of the direct stimulation of things, reproduce the image of things, reduce the constraints of time and space, so as to reduce the difficulty of teaching. The method is to establish visual imagery, practice the combination of visual imagery and kinesthetic imagery, and form swimming skills mainly relying on kinesthetic imagery. imagery practice improves the quality of teaching in terms of time and skills. Based on the practice of good results, some suggestions are put forward. © 2021 ieee.

关键词： Computers visualization Education Information processing Time factors Sports

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A Secured Communication in Watermarking using Fast Walsh Hadamard Transformation in image processing 6

A Secured Communication in Watermarking using Fast Walsh Had...

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6th International conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Premkumar, K. Twinky, H. Naveen, S. Kathiresan, M. Aravind, R. Srmist Ramapuram India

ISBN: (纸本)9781665482714

E-commerce, or electronic commerce, refers to the purchasing and selling of goods and services conducted over electronic systems such as the internet and other computer networks. Consequently, there is a pressing need to bolster the security of data transmission over the internet to accommodate the expansion of e-commerce applications on the World Wide Web. Data encryption and information masking techniques were introduced and developed to secure these applications' data. The creation of digital picture watermarking schemes is the subject of this thesis. Our main emphasis in this thesis is on the advancements made possible by the Fast Walsh Hadamard Transformation (FWHT). The primary result of this study is a proposed method for implementing FWHT in digital image watermarking. If a picture is described at multiple resolutions, its decoding can proceed in stages, starting at a lower resolution and working its way up to a higher one. In order to better analyse an image, the FWHT separates it into high- and low-frequency components. The edge components are described in the high frequency portion, while the low frequency portion is further divided into high and low frequency segments. Since the human visual system is more insensitive to abrupt changes in edge contrast, low frequency components are typically employed for watermarking. Several metrics, including Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Metric, were used to compare the efficacy of various watermarking methods (SSIM). The aforesaid analysis forms the basis for applying Level-2 and FWHT to enhance the image quality characteristics. It is determined which of these FWHT produced the best outcomes by comparing the others. The outcome demonstrates that the extracted watermark is just as easy to recognize as the original. © 2022 ieee.

关键词： Mean square error

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Smart Braille Translator: Integrating Braille-to-Speech and Speech-to-Braille Using Raspberry Pi

Smart Braille Translator: Integrating Braille-to-Speech and ...

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Computing (ICOCO), ieee International conference on

作者： John Danreb R. Cabangis Frits F. Sumaculub Peter M. Valluyas Criselle J. Centeno Vivien A. Agustin Diony S. Abando Richard C. Regala College of Information Systems Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

In the Philippines, approximately half a million individuals are estimated to have visual impairments, with a significant number lacking the ability to read Braille. This study addresses various challenges in the development of a Braille translation system, such as ensuring accurate conversion of Braille characters into text and speech. The system leverages a Raspberry $\operatorname{Pi} 4$ as a microcontroller, employing a Convolutional Neural Network (CNN) for scanning Braille characters, followed by Natural Language processing (NLP) and Braille mapping for translation. Moreover, the study will integrate the principles of assistive technology theory throughout the development of the system. During data gathering, the proponents discovered that most respondents from the Philippine Blind Union community had only a basic understanding of Braille. The study ultimately achieved its three primary objectives by implementing the system successfully. Regarding Braille conversion, the system demonstrated a $\mathbf{1 0 0 \%}$ accuracy rate for simple dot patterns and $\mathbf{9 8 \%}$ for more complex patterns. Additionally, the camera scanning yielded $96 \%$ accuracy in well-lit conditions and 70% accuracy in dim lighting. The final system evaluation, based on ISO 25010:2023 standards, showed positive feedback from respondents who tested the system.

关键词： Braille Translation Accuracy image recognition visual impairment Neural networks Cameras Natural language processing Convolutional neural networks Reliability

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Invariant Adversarial Imitation Learning From visual Inputs

Invariant Adversarial Imitation Learning From Visual Inputs

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Haoran Zhang Yinghong Tian Liang Yuan Yue Lu School of Communications and Electronic Engineering East China Normal University Shanghai China College of Information Science and Technology Beijing University of Chemical Technology Beijing China

Generalization across environments is critical when using imitation learning algorithms in real-world applications. In this paper, we propose an invariant model-based adversarial imitation learning (IMAIL) method to improve generalization. IMAIL develops a variational dynamics model providing rich auxiliary objectives for efficiently learning compact state representations. The latent representations are then regularized using mutual information constraints, guaranteeing that they are insensitive to environmental changes. Based on such representations, we utilize model-based adversarial imitation learning to mimic expert behavior in the latent space. As a result, the learned policies are well generalized in unseen environments. We conduct experiments with several vision-based control tasks to demonstrate the performance of IMAIL. Experimental results show that IMAIL significantly outperforms existing baselines and successfully achieves expert-level performance in all unseen test environments.

关键词： visualization Heuristic algorithms Signal processing algorithms Signal processing Manipulators Behavioral sciences Task analysis

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Fusion-CAM: Fine-Grained and High-Faithfulness Explanations for Deep Convolutional Network via Hierarchical Fusion

Fusion-CAM: Fine-Grained and High-Faithfulness Explanations ...

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ieee Advanced Information Technology, Electronic and Automation Control conference (IAEAC)

作者： Chen Zheng Wei Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

The interpretability of the convolutional neural networks(CNNs) has become a research hotspot. A popular explanation method is based on Class Activation Mapping (CAM), which visualizes the salient regions most relevant to neural network decisions. However, many CAM methods use the feature maps produced by the final convolution layer to generate the class activation maps, which usually have a low spatial resolution and can only generate coarse-grained visual explanations that provide a rough spatial location of the target object. In this paper, we propose a novel CAM method named Fusion-CAM. It improves traditional CAM methods by combining final class activation map containing semantic information with intermediate layer class activation maps containing fine-grained details, to generate fine-grained visual explanations with high faithfulness. In order to obtain high-quality intermediate layer class activation maps, we utilize Layer-wise Relevance Propagation (LRP) to obtain the weighting components of each channel of the intermediate layer feature maps, and the intermediate layer class activation maps generated by weighted summation are less noisy and have clear fine-grained details, which help to improve the quality of the final class activation map. Qualitative and quantitative experiments show that Fusion-CAM can be easily attached to different CAM methods to improve their performance.

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Adaptive Kernelized Evidence C-Means Clustering Combining Spatial Information for Noisy image Segmentation

Adaptive Kernelized Evidence C-Means Clustering Combining Sp...

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Natural Language processing (ICNLP), International conference on

作者： Lan Rong Mi Haowen Qu Na Zhao Feng Yu Haiyan Zhang Lu School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

Although the evidence c-means clustering (ECM) has the capability to process uncertain information, it is not suitable for noisy image segmentation, because the spatial information of pixels is not considered. To solve the problem, an adaptive kernelized evidence c-means clustering combining spatial information for noisy image segmentation algorithm is proposed. Firstly, an adaptive noise distance that can be iteratively updated is constructed using the local information of the pixels. Secondly, to improve the classification performance, an adaptive kernel function is proposed to measure the distance between the pixel and the cluster center. Simultaneously, the original, local and non-local information of pixels are introduced adaptively into the objective function to enhance the robustness to noise. In the iteration, the noise cluster is automatically recovered using the recovery factor constructed by the gray and spatial information of neighborhood pixels. Finally, the credal partition is transformed into a fuzzy partition by pignistic transformation, the classification of pixel be determined by the maximum membership principle. Experiments on synthetic images and real images demonstrate that the proposed algorithm has strong noise suppression ability. visual effects and evaluation indexes verify the effectiveness of the proposed algorithm for noisy image segmentation.

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