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Improved image Classification with Manifold Neural Networks

Improved Image Classification with Manifold Neural Networks

引用

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

作者： Caio F. Deberaldini Netto Zhiyang Wang Luana Ruiz Department of Applied Mathematics and Statistics Mathematical Institute for Data Science (MINDS) Data Science and Artificial Intelligence Institute (DSAI) Johns Hopkins University Baltimore USA Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Neural Networks (GNNs) have gained popularity in various learning tasks, with successful applications in fields like molecular biology, transportation systems, and electrical grids. These fields naturally use graph data, benefiting from GNNs’ message-passing framework. However, the potential of GNNs in more general data representations, especially in the image domain, remains underexplored. Leveraging the manifold hypothesis, which posits that high-dimensional data lies in a low-dimensional manifold, we explore GNNs’ potential in this context. We construct an image manifold using variational autoencoders, then sample the manifold to generate graphs where each node is an image. This approach reduces data dimensionality while preserving geometric information. We then train a GNN to predict node labels corresponding to the image labels in the classification task, and leverage convergence of GNNs to manifold neural networks to analyze GNN generalization. Experiments on MNIST and CIFAR10 datasets demonstrate that GNNs generalize effectively to unseen graphs, achieving competitive accuracy in classification tasks.

关键词： Manifolds Electric potential High dimensional data Autoencoders Transportation signal processing Graph neural networks Molecular biology Speech processing image classification

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TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

引用

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

作者： xinchen Ma Yue Lu School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Ultra-high-definition (UHD) image restoration is becoming a critical research area due to the increasing demand for high-quality visual content in various applications, including autonomous driving, remote sensing, digital entertainment, etc. However, UHD image restoration tasks present notable challenges, including the significant computational burden posed by the large image size scale, the difficulty in preserving fine details and textures, and the increased susceptibility to noise and artifacts during the restoration process. In this paper, we propose a novel triple-branched optimized heterogeneous transformer for UHD image restoration, named TriFormer. Specifically, our method embeds a high-resolution CNN branch for high-frequency features, a low-resolution pixel-wise attention branch for low-frequency features, and a channel-wise attention branch for feature fusion. We perform experiments across two UHD image restoration tasks: enhancing low-light images and deblurring. Results demonstrate that our model outperforms state-of-the-art approaches both quantitatively and qualitatively. The code will be made available at https://***/Chloe-mxxxxc/TRIFORMER.

关键词： Visualization Technological innovation Noise Graphics processing units Entertainment industry Computer architecture Transformers image restoration Speech processing Remote sensing

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From Pixels to Voice: A Simple and Efficient End-to-End Spoken image Description Approach via Vision Codec Language Models

From Pixels to Voice: A Simple and Efficient End-to-End Spok...

引用

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

作者： Chung Tran Sakriani Sakti Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Neural audio codecs provide a powerful tool for compressing audio signals into discrete codec representations. This compact discrete representation has made it possible to successfully apply a natural language processing (NLP) model to various audio and speech processing tasks, including text-to-speech (e.g., VALL-E, VALL-E x) and multimodal audio-text generation (e.g., LauraGPT, VioLA). While these models excel at handling sequential data like text and speech, their potential for processing non-sequential data, such as images, remains unexplored. In this paper, we introduce PixVoxLM, a simple and efficient end-to-end framework that combines vision-language models with neural audio codecs to tackle the image-to-Speech (I2S) problem. Experiments on the Flickr8k dataset demonstrate that PixVoxLM delivers promising results compared to existing I2S methods. Furthermore, this research is the first to explore a new capability: visual-guided speech completion in I2S model, paving the way for new practical applications in everyday communication, such as speech prompt-based instruction.

关键词： Codecs Speech coding signal processing Natural language processing Data models Acoustics Text to speech Speech processing Synthetic aperture sonar

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Spatial-Temporal Reconstruction Error for AIGC-based Forgery image Detection

Spatial-Temporal Reconstruction Error for AIGC-based Forgery...

引用

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

作者： Chengji Shen Zhenjiang Liu Kaixuan Chen Jie Lei Mingli Song Zunlei Feng State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Idlefish Network Technology Alibaba Group College of Computer Science and Technology Zhejiang University of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The remarkable success of AI-Generated Content (AIGC), especially diffusion image generation models, brings about unprecedented creative applications, but also creates fertile ground for malicious counterfeiting and crime. A highly effective family of forgery image detection methods based on diffusion reconstruction error has emerged, as images generated by diffusion are more easily reconstructed by any diffusion model. However, we find that existing methods only use reconstruction error from a single time step, failing to fully leverage the entire reconstruction process. To this end, we propose to comprehensively consider every single time step to form the Temporal Reconstruction Error (TRE) that offers a richer feature representation. Furthermore, we design temporal aggregation and spatial focusing modules from two dimensions respectively to more effectively extract discriminative information from the TRE feature. Finally, we validate the proposed method on two popular datasets, and experimental results demonstrate that the proposed approach achieves state-of-the-art performance.

关键词： Deepfakes image synthesis Focusing signal processing Feature extraction Diffusion models Forgery Data mining Speech processing image reconstruction

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The Effect of Impulse Noise on Accuracy of Clustering of Chest Radiographic images in Pneumonia

The Effect of Impulse Noise on Accuracy of Clustering of Che...

引用

Digital signal processing and its applications (DSPA), International conference on

作者： Iskander Bariev Dina Khairullina Dmitrii Tumakov Research Center for Artificial Intelligence Innopolis University Innopolis Russia Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The effect of salt-and-pepper noise on the accuracy of clustering of lung x-ray images is considered. The analysis is performed for cases of healthy lungs and lungs with signs of bacterial and viral pneumonia. Fifty images of different sizes are considered for each of the three classes. The level of salt-and-pepper noise is taken from 1 % to 5%. The number of clusters for each image is chosen to be three (elbow method). The differences between clusters of noisy and original images are studied by the IoU metric. It is concluded that images with viral pneumonia have a better match for this metric than the other two classes. In other words, noise has a lesser effect on the clustering results for viral pneumonia. Cases with a number of clusters greater than three are also analyzed.

关键词： Radiography Pneumonia Accuracy Microorganisms Lungs Noise Noise measurement Elbow x-ray imaging Noise level

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Enhancing Boundary-Handling Strategies for Convolutional Sparse Representation Model with 46 × 46 Convolution-Multiplication Properties

Enhancing Boundary-Handling Strategies for Convolutional Spa...

引用

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

作者： Yuto Tsukiashi Yoshimitsu Kuroki National Institute of Technology Kurume College Fukuoka Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Convolutional sparse representation (CSR) extends the standard form by incorporating convolutional operations and has achieved notable success in image processing applications, offering a shift-invariant representation model. Recent studies typically redesign the initial model by exploiting the convolution-multiplication property of the discrete Fourier transform to solve the involved convex optimizations efficiently, which imposes periodic boundary handling and risks introducing boundary artifacts. This paper generalizes this approach by leveraging 46 × 46 convolution-multiplication properties while requiring only extra element-wise operations, enabling model designs that combine periodic, antiperiodic, and 40 symmetric boundary handlings for each dimension and across convolutions. These newly accepted models demonstrated better representation performance than the conventional model in our experiments, indicating the benefits of selecting appropriate boundary handlings.

关键词： Convolution Sparse approximation Computational modeling image processing Memory management Transforms Linear programming Convex functions Speech processing Standards

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A Parametric Non-Negative Coupled Canonical Polyadic Decomposition Algorithm for Hyperspectral Super-Resolution

A Parametric Non-Negative Coupled Canonical Polyadic Decompo...

引用

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

作者： xi-Yuan Liu xiao-Feng Gong Lei Wang Wei Feng Qiu-Hua Lin School of Information and Communication Engineering Dalian University of Technology China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, coupled tensor decomposition has been widely used in data fusion of a hyperspectral image (HSI) and a multispectral image (MSI) for hyperspectral super-resolution (HSR). However, exsiting works often ignore the inherent non-negative (NN) property of the image data, or impose the NN constraint via hard-thresholding which may interfere with the optimization procedure and cause the method to be sub-optimal. As such, we propose a novel NN coupled canonical polyadic decomposition (NN-C-CPD) algorithm, which makes use of the parametric method and nonlinear least squares (NLS) framework to impose the NN constraint into the C-CPD computation. More exactly, the NN constraint is converted into the squared relationship between the NN entries of the factor matrices and a set of latent parameters. Based on the chain rule for deriving the derivatives, the key entities such as gradient and Jacobian with regards to the latent parameters can be derived, thus the NN constraint is naturally integrated without interfering with the optimization procedure. Experimental results are provided to demonstrate the performance of the proposed NN-C-CPD algorithm in HSR applications.

关键词： Jacobian matrices Tensors Superresolution Noise signal processing algorithms Matrix decomposition Matrix converters Speech processing Optimization Hyperspectral imaging

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Class Relevance Learning for Out-of-Distribution Detection

Class Relevance Learning for Out-of-Distribution Detection

引用

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

作者： Liguang Zhou Butian xiong Tin Lun Lam Yangsheng xu School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

image classification plays a pivotal role across diverse robotic applications, yet challenges persist when models are deployed in real-world scenarios. These models often fail to detect out-of-distribution (OOD) samples, classes not included in their training. This makes OOD detection a significant challenge for safe and effective real-world use. While existing techniques, like max logits, aim to leverage logits for OOD identification, they often disregard the intricate interclass relationships that underlie effective detection. This paper presents an innovative class relevance learning (CRL) method tailored for OOD detection. Our method establishes a comprehensive class relevance learning framework, strategically harnessing interclass relationships within the OOD pipeline. This framework significantly augments OOD detection capabilities. Extensive experimentation on diverse datasets, encompassing generic image classification datasets (Near OOD and Far OOD datasets), demonstrates the superiority of our method over state-of-the-art alternatives for OOD detection. The code is available on GitHub at: CRL.

关键词： Training Codes Pipelines Estimation signal processing Probability Speech processing Robots image classification Software development management

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Assessing Robustness of Multi-Modal Large Language Models in image Classification through Hierarchical WordNet-Based Evaluation

Assessing Robustness of Multi-Modal Large Language Models in...

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

作者： Chang Liu Hai Chen Boxiang Wang Shibao Zheng Shanghai Jiao Tong University Shanghai China Anhui Unversity Hefei China Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The advancement of multi-modal large language models (MLLMs) has significantly enhanced their capability to process and understand diverse data types, integrating text, images, and other modalities. Despite their impressive performance, evaluating the robustness of these models remains challenging due to the difficulty of aligning their text-based responses with image classification labels. Traditional approaches rely on CLIP scores or other large language models as judges, but these methods lack scientific rigor and fail to capture robustness across different semantic levels. In this paper, we propose a novel evaluation metric that systematically assesses the robustness of MLLMs in image classification using WordNet’s hierarchical structure. Specifically, we parse the text descriptions generated by MLLMs to extract all nouns, then calculate their distances to the groundtruth label in WordNet as a similarity metric. The minimum distance among all nouns is used as the similarity score between the text description and the label. By using WordNet, we can also evaluate classification performance at different semantic levels. Through extensive experiments, we demonstrate that our WordNet-based evaluation metric offers a deeper understanding of MLLMs’ robustness, paving the way for more resilient and reliable models in real-world applications.

关键词： Measurement Visualization Large language models Semantics Lexicon Linguistics signal processing Robustness Speech processing image classification

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Structured Random Model for Fast and Robust Phase Retrieval

Structured Random Model for Fast and Robust Phase Retrieval

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

作者： Zhiyuan Hu Julián Tachella Michael Unser Jonathan Dong Biomedical Imaging Group École Polytechnique fédérale de Lausanne Lausanne Switzerland CNRS ENS de Lyon Lyon France

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Phase retrieval, a nonlinear problem prevalent in imaging applications, has been extensively studied using random models, some of which with i.i.d. sensing matrix components. While these models offer robust reconstruction guarantees, they are computationally expensive and impractical for real-world scenarios. In contrast, Fourier-based models, common in applications such as ptychography and coded diffraction imaging, are computationally more efficient but lack the theoretical guarantees of random models. Here, we introduce structured random models for phase retrieval that combine the efficiency of fast Fourier transforms with the versatility of random diagonal matrices. These models emulate i.i.d. random matrices at a fraction of the computational cost. Our approach demonstrates robust reconstructions comparable to fully random models using gradient descent and spectral methods. Furthermore, we establish that a minimum of two structured layers is necessary to achieve these structured-random properties. The proposed method is suitable for optical implementation and offers an efficient and robust alternative for phase retrieval in practical imaging applications.

关键词： Optical diffraction Computational modeling Optical imaging Computational efficiency Optical sensors Speech processing Optical signal processing Nonlinear optics image reconstruction Optimization

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