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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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2011 ieee visual communications and image processing, VCIP 2011

2011 IEEE Visual Communications and Image Processing, VCIP 2...

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2011 ieee visual communications and image processing, VCIP 2011

ISBN: (纸本)9781457713200

The proceedings contain 141 papers. The topics discussed include: improved rate-adaptive codes for distributed video coding;adaptive raster scan for slice/frame coding;video deblurring algorithm using an adjacent unblurred frame;fast disparity estimation for multi-view plus depth video coding;real-time forward error correction for video transmission;quantitative assessment of 2D versus 3D visualization modalities;image matting based on mutual information;priority pyramid based bit allocation for multiview video coding;interactive object segmentation using iterative adjustable graph cut;unequal channel error protection of multiple description codes for wireless media streaming;image indexing using 3D model for image retrieval;compressive sensing based video scrambling for privacy protection;weighted sparse coding residual minimization for visual tracking;and a novel high efficiency fixed length coding for video compression based on symbol probability estimation.

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Adaptive Structure-Aware Connectivity-Preserving Loss for Improved Road Segmentation in Remote Sensing images

Adaptive Structure-Aware Connectivity-Preserving Loss for Im...

引用

ieee Winter Applications and Computer Vision Workshops (WACVW)

作者： Sara Shojaei Trevor Bohl Kannappan Palaniappan Filiz Bunyak Department of Electrical Engineering and Computer Science University of Missouri-Columbia MO USA

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Accurate road extraction from remote sensing images is a critical step for many applications, including navigation, environmental monitoring, and disaster response. In remote sensing images, roads appear as thin curvilinear structures and are very prone to fragmentation due to challenges such as low-contrast materials and occlusions by surrounding vegetation or buildings. Although deep learning has greatly advanced image segmentation techniques, pixel-level loss functions are insufficient for preserving connectivity and topology of thin curvilinear structures such as roads. Small pixel-level errors can disrupt the extraction of accurate road graphs, complicating subsequent characterizations and analysis tasks. In this paper, we propose a novel, adaptive, structure-aware, connectivity-preserving loss function SAC-Loss. This loss function combines global and local processing to improve structure awareness and uses a proximity-based weighting scheme with asymmetric penalties to fill gaps in the road structures while limiting spurious detections elsewhere. Experimental results demonstrate improved road segmentation in terms of visual quality and quantitative performance.

关键词： image segmentation visualization Accuracy Limiting Network topology Roads Vegetation mapping Network analyzers Topology Remote sensing

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Guess What I Think: Streamlined EEG-to-image Generation with Latent Diffusion Models

Guess What I Think: Streamlined EEG-to-Image Generation with...

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

作者： Eleonora Lopez Luigi Sigillo Federica Colonnese Massimo Panella Danilo Comminiello Dept. Information Engineering Electronics and Telecommunications (DIET) Sapienza University of Rome Italy

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode visual cues. Most of the literature has focused on fMRI-to-image tasks as fMRI is characterized by high spatial resolution. However, fMRI is an expensive neuroimaging modality and does not allow for real-time BCI. On the other hand, electroencephalography (EEG) is a low-cost, non-invasive, and portable neuroimaging technique, making it an attractive option for future real-time applications. Nevertheless, EEG presents inherent challenges due to its low spatial resolution and susceptibility to noise and artifacts, which makes generating images from EEG more difficult. In this paper, we address these problems with a streamlined framework based on the ControlNet adapter for conditioning a latent diffusion model (LDM) through EEG signals. We conduct experiments and ablation studies on popular benchmarks to demonstrate that the proposed method beats other state-of-the-art models. Unlike these methods, which often require extensive preprocessing, pretraining, different losses, and captioning models, our approach is efficient and straightforward, requiring only minimal preprocessing and a few components. The code is available at https://***/LuigiSigillo/GWIT.

关键词： Neuroimaging Adaptation models visualization Streaming media Functional magnetic resonance imaging Diffusion models Brain modeling Electroencephalography Real-time systems Spatial resolution

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Residential Real Estate image Classification for Property Valuation 28th

Residential Real Estate Image Classification for Property Va...

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Nejad, Mehrdad Ziaee Naderpour, Mohsen Behbood, Vahid Ramezani, Fahimeh Lu, Jie 15 Broadway UltimoNSW2007 Australia

ISBN: (纸本)9783031859328

Residential real estate price is one of the key components of our economic developments and has also been a major concern of the public, bank industry, government, and investors. The accurate estimation of the sale price and its changes have an important role in the decision-making of related departments and organizations. In Australia, one of the biggest investments for people is in residential real estate. Therefore, many studies and research works have been carried out to build an automated valuation model to predict sale prices of residential properties accurately as much as possible. Automatic and accurate image classification of residential real estate plays an important role in property valuation and decision making of both sellers and buyers. It can be used in real estate online websites to organize the images for each property or used as a component in a visual decision support system for predicting the property sale prices based on property images. As convolutional image classification models show valuable performance in comparison with traditional models, a convolutional classification model is developed in this paper which creates a highly reliable classification component to be used in the corresponding research areas. The performance of the proposed model is investigated through a real dataset of New Sales Wales, Australia. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Robust Low-Light Human Pose Estimation through Illumination-Texture Modulation

Robust Low-Light Human Pose Estimation through Illumination-...

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

作者： Feng Zhang Ze Li Xiatian Zhu Lei Chen Nanjing University of Posts and Telecommunications Nanjing China Surrey Institute for People-Centred Artificial Intelligence University of Surrey United Kingdom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As critical visual details become obscured, the low visibility and high ISO noise in extremely low-light images pose a significant challenge to human pose estimation. Current methods fail to provide high-quality representations due to reliance on pixel-level enhancements that compromise semantics and the inability to effectively handle extreme low-light conditions for robust feature learning. In this work, we propose a frequency-based framework for low-light human pose estimation, rooted in the "divide-and-conquer" principle. Instead of uniformly enhancing the entire image, our method focuses on task-relevant information. By applying dynamic illumination correction to the low-frequency components and low-rank denoising to the high-frequency components, we effectively enhance both the semantic and texture information essential for accurate pose estimation. As a result, this targeted enhancement method results in robust, high-quality representations, significantly improving pose estimation performance. Extensive experiments demonstrating its superiority over state-of-the-art methods in various challenging low-light scenarios.

关键词： Representation learning visualization Frequency modulation ISO Pose estimation Semantics Noise reduction Noise Lighting Speech processing

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Spatial-Frequency Information Interaction Diffusion for SAR Colorization

Spatial-Frequency Information Interaction Diffusion for SAR ...

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

作者： Xupei Zhang Hanlin Qin Jingjing Li Jinni Geng Zihan Gao Yue Yu School of Optoelectronic Engineering Xidian University Xi’an China Xi’an Key Laboratory of Infrared Technology and System Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The inherent speckle noise and grayscale characteristics of synthetic aperture radar (SAR) images pose challenges to information perception and interpretation. To address this issue, we propose a novel conditional diffusion model with spatial-frequency information interaction for SAR colorization, namely SFI4SC. Specifically, we introduce a learnable wavelet transform module to obtain the global frequency information of the input SAR image and then take it as a guiding condition to assist the diffusion model for SAR colorization. Additionally, we improved the network structure for the diffusion model by designing and introducing a spatial and frequency information interaction module to achieve multidimensional information interaction for the network inputs, further enhancing perceptual details and geometric structures of the SAR colorization results. Experimental results in the real-world remote sensing dataset show that our approach can successfully enhance visual clarity and informational content while preserving the unique characteristics of the SAR images. The code will be made publicly available.

关键词： Wavelet transforms Measurement visualization Noise Speckle Diffusion models Radar polarimetry Speech processing Synthetic aperture radar Remote sensing

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FruitMMBench: A Multi-modal Benchmark for Fruit Quality Assessment

FruitMMBench: A Multi-modal Benchmark for Fruit Quality Asse...

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

作者： Jiawei Chen Gong Huang Liu Liu Zhenbo Xu Qinghong Yang Beihang University Beijing China Beijing Univ. of Posts and Telecomm. Beijing China ShiFang Technology Inc. Hangzhou China Hangzhou International Innovation Institute Beihang University Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The rapid advancement of Large Vision-Language Models (LVLMs) has brought notable improvements in tasks like visual recognition and multi-modal understanding, demonstrating significant potential in real-world applications. However, their performances on issues related to daily life such as fruit quality assessment have never been explored due to the lack of a benchmark for fruit quality assessment. To bridge this gap, we introduce FruitMMBench, a comprehensive multi-modal benchmark designed to assess the ability of fruit quality assessment. A comprehensive metric is carefully designed by jointly considering many aspects. The resulting large-scale FruitMMBench contains 5,465 collected fruit images and dedicated quality labels including the following aspects: fruit classification, quantity recognition, maturity, surface condition, quality status, and edibility recommendation. By extensive evaluations on FruitMMBench, we find popular LVLMs struggle to provide reliable results in fruit quality assessment and their performances vary greatly. The test results reveal that the ability of current LVLMs to analyze fruit quality in real-world scenarios is still weak and needs to be paid attention to and enhanced in the future. All evaluation codes and dataset will be publicly accessible shortly.

关键词： Measurement Bridges visualization image recognition Codes Benchmark testing Signal processing Quality assessment Reliability Speech processing

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SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human Activity Recognition

SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning...

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

作者： Yijun Wang Yong Wang Chendong Xu Shuai Yao Qisong Wu Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human Activity Recognition (HAR) such as fall detection has become increasingly critical due to the aging population, necessitating effective monitoring systems to prevent serious injuries and fatalities associated with falls. This study focuses on fine-tuning the Vision Transformer (ViT) model specifically for HAR using radar-based Time-Doppler signatures. Unlike traditional image datasets, these signals present unique challenges due to their non-visual nature and the high degree of similarity among various activities. Directly fine-tuning the ViT with all parameters proves suboptimal for this application. To address this challenge, we propose a novel approach that employs Low-Rank Adaptation (LoRA) fine-tuning in the weight space to facilitate knowledge transfer from pre-trained ViT models. Additionally, to extract fine-grained features, we enhance feature representation through the integration of a serial-parallel adapter in the feature space. Our innovative joint fine-tuning method, tailored for radar-based Time-Doppler signatures, significantly improves HAR accuracy, surpassing existing state-of-the-art methodologies in this domain. Our code is released at https://***/wangyijunlyy/SelaFD.

关键词： Computer vision Accuracy Spaceborne radar Transfer learning Speech recognition Radar imaging Feature extraction Transformers Human activity recognition Speech processing

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imageFlowNet: Forecasting Multiscale image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical images

ImageFlowNet: Forecasting Multiscale Image-Level Trajectorie...

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

作者： Chen Liu Ke Xu Liangbo L. Shen Guillaume Huguet Zilong Wang Alexander Tong Danilo Bzdok Jay Stewart Jay C. Wang Lucian V. Del Priore Smita Krishnaswamy Yale University UCSF Mila Univ. de Montréal Mila McGill Yale NorCal Retina Vitreous Associates

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Advances in medical imaging technologies have enabled the collection of longitudinal images, which involve repeated scanning of the same patients over time, to monitor disease progression. However, predictive modeling of such data remains challenging due to high dimensionality, irregular sampling, and data sparsity. To address these issues, we propose imageFlowNet, a novel model designed to forecast disease trajectories from initial images while preserving spatial details. imageFlowNet first learns multiscale joint representation spaces across patients and time points, then optimizes deterministic or stochastic flow fields within these spaces using a position-parameterized neural ODE/SDE framework. The model leverages a UNet architecture to create robust multiscale representations and mitigates data scarcity by combining knowledge from all patients. We provide theoretical insights that support our formulation of ODEs, and motivate our regularizations involving high-level visual features, latent space organization, and trajectory smoothness. We validate imageFlowNet on three longitudinal medical image datasets depicting progression in geographic atrophy, multiple sclerosis, and glioblastoma, demonstrating its ability to effectively forecast disease progression and outperform existing methods. Our contributions include the development of imageFlowNet, its theoretical underpinnings, and empirical validation on real-world datasets.

关键词： visualization Multiple sclerosis Stochastic processes Organizations Predictive models Signal processing Data models Trajectory Speech processing Biomedical imaging

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