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Leveraging Multimodal Methods and Spontaneous Speech for Alzheimer's Disease Identification

Leveraging Multimodal Methods and Spontaneous Speech for Alz...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Gao, Yifan Guo, Long Liu, Hong College of Computer Science National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China

ISBN: (纸本)9798350368741

Cognitive impairment detection through spontaneous speech is a promising avenue for early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI), where timely intervention can significantly improve patient outcomes. The PROCESS Grand Challenge at ICASSP 2025 addresses these tasks by promoting innovative classification and regression methods for detecting cognitive decline. In this paper, we propose a multimodal fusion strategy that combines interpretable linguistic features with temporal embeddings extracted from pre-trained models. Our approach achieves an F1-score of 0.649 for the classification task (predicting healthy, MCI, dementia) and an RMSE of 2.628 for the regression task (MMSE score prediction), securing the top overall ranking in the competition. © 2025 IEEE.

关键词： Interpretable Features Multimodal Temporal Features

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Heart signal processing Using Wavelet Analysis and Deep Learning Algorithms Based on Artificial Intelligence

Heart Signal Processing Using Wavelet Analysis and Deep Lear...

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international conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Zilaie, Morteza Mohammadkhani, Zohreh Asrari, Keyvan Azimi Talebiyan, Seyed Reza Islamic Azad University Science and Research Branch Khorasan Razavi Mashhad Iran The University of Pavia Pavia Italy Imam Reza International University Khorasan Razavi Mashhad Iran

ISBN: (纸本)9783031840999

The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different heart failures produces different types of this signal. In this research, the cardiac signals of the patients are analyzed by an artificial intelligence neural network, and the type of the person’s disease is diagnosed. The artificial intelligence in question can be learned and can be used as an assistant ECG signal is calculated using continuous wavelet transform and then it is subjected to learning and evaluation using a deep convolutional neural network. In the architecture of artificial intelligence, two well-known neural networks GoogLeNet and SqueezeNet are used, which have been sufficiently trained in similar applications such as image processing and machine vision. In the end, the type of heart failure is diagnosed and classified. The cardiac signals of the patients used in the simulation were extracted from the PhysioNet medical engineering standard dataset of MIT University. In the end, the simulation results of the research are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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A decade of DCASE: Achievements, practices, evaluations and future challenges

A decade of DCASE: Achievements, practices, evaluations and ...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Mesaros, Annamaria Serizel, Romain Heittola, Toni Virtanen, Tuomas Plumbley, Mark D. Signal Processing Research Center Tampere University Tampere Finland Université de Lorraine CNRS Inria Loria Nancy France Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

ISBN: (纸本)9798350368741

This paper introduces briefly the history and growth of the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge, workshop, research area and research community. Created in 2013 as a data evaluation challenge, DCASE has become a major research topic in the Audio and Acoustic signal processing area. Its success comes from a combination of factors: the challenge offers a large variety of tasks that are renewed each year;and the workshop offers a channel for dissemination of related work, engaging a young and dynamic community. At the same time, DCASE faces its own challenges, growing and expanding to different areas. One of the core principles of DCASE is open science and reproducibility: publicly available datasets, baseline systems, technical reports and workshop publications. While the DCASE challenge and workshop are independent of IEEE SPS, the challenge receives annual endorsement from the AASP TC, and the DCASE community contributes significantly to the ICASSP flagship conference and the success of SPS in many of its activities. © 2025 IEEE.

关键词： AASP Challenges DCASE Challenge DCASE Workshop

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Leveraging Registers in vision Transformers for Robust Adaptation

Leveraging Registers in Vision Transformers for Robust Adapt...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yellapragada, Srikar Thopalli, Kowshik Narayanaswamy, Vivek Sakla, Wesam Liu, Yang Mubarka, Yamen Samaras, Dimitris Thiagarajan, Jayaraman J. Stony Brook University United States Lawrence Livermore National Laboratory United States

ISBN: (纸本)9798350368741

vision Transformers (ViTs) have shown success across a variety of tasks due to their ability to capture global image representations. Recent studies have identified the existence of high-norm tokens in ViTs, which can interfere with unsupervised object discovery. To address this, the use of"registers" which are additional tokens that isolate high norm patch tokens while capturing global image-level information has been proposed. While registers have been studied extensively for object discovery, their generalization properties particularly in out-of-distribution (OOD) scenarios, remains underexplored. In this paper, we examine the utility of register token embeddings in providing additional features for improving generalization and anomaly rejection. To that end, we propose a simple method that combines the special CLS token embedding commonly employed in ViTs with the average-pooled register embeddings to create feature representations which are subsequently used for training a downstream classifier. We find that this enhances OOD generalization and anomaly rejection, while maintaining in-distribution (ID) performance. Extensive experiments across multiple ViT backbones trained with and without registers reveal consistent improvements of 2-4% in top-1 OOD accuracy and a 2-3% reduction in false positive rates for anomaly detection. Importantly, these gains are achieved without additional computational overhead. © 2025 IEEE.

关键词： Anomaly Rejection OOD Generalization Registers Robustness vision Transformer

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SSDViT: Exploring Siamese and Self Distillation in ViTs for Generalizable Person Re-identification

SSDViT: Exploring Siamese and Self Distillation in ViTs for ...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Jia, Jieru Yang, Jianchao Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (纸本)9798350368741

Person re-identification (re-ID) models often fail to generalize well when deployed to unseen camera networks with domain shift. Domain generalization (DG) aims to address this dilemma by training a model on source domains that learns domain-invariant, and hence generalizable representations. Most methods in the literature are based on Convolutional Neural Networks (CNNs), while the DG performance of vision Transformers (ViTs) remains relatively unexplored. In contrast to CNNs, ViTs lack explicit inductive biases, which makes it extremely data-hungry and easily overfit to source domains. In this paper, we investigate the generalization ability of ViTs and propose a novel Siamese and Self Distillation vision Transformer (SSDViT) framework towards addressing the DG re-ID problem. To be specific, Siamese Distillation exploits the weak-to-strong consistency regularization at the image level to enforce the strongly perturbed image to yield consistent prediction with its weakly perturbed version, which provides a simple yet effective solution to introduce invariance bias. On the other hand, Self Distillation seeks to impose consistency constraints at the feature level by leveraging intermediate knowledge to improve the robustness of learned representations. The proposed unified framework pursues the equivalence of predictions at both the image and embedding levels, which underpins the generalization capabilities of learned representations and alleviates the risk of overfitting to source domains. Without bells and whistles, the proposed approach achieves a new state-of-the-art on various DG re-ID benchmarks. Codes are available at https://***/yJCTrans/SSDViT. © 2025 IEEE.

关键词： Domain Generalization Knowledge Distillation Person Re-identification vision Transformer

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13th international conference on Biomimetic and Biohybrid Systems, Living Machines 2024

13th International Conference on Biomimetic and Biohybrid Sy...

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13th international conference on Biomimetic and Biohybrid Systems, Living Machines 2024

ISBN: (纸本)9783031725968

The proceedings contain 31 papers. The special focus in this conference is on Biomimetic and Biohybrid Systems. The topics include: FlyWheel: A robotic Platform for Modeling Fly Visual Behavior;a Coupled-Oscillator Model of Human Attachment Dynamics Evaluated in a robot Dyadic Interaction;creating an Artificial No-Fly Zone with Sensory Disruptions;maximizing robotic Limb Rigidity and Strain Sensing Capabilities Through Localized Kevlar Fiber Reinforcement;Flexible Strain Gauge Sensors as Real-Time Stretch Receptors for Use in Biomimetic BPA Muscle Applications;sensory Feedback Cancellation: Developing Resonator Networks to Mimic A. leptorhynchu’s Cerebellar processing of Sensory Feedback;binocular vision and Vector-Summation Based Integration of Bilateral Innate and Learned Visual Cues in Insect Navigation;bioinspired Magnetic Navigation for Exploring Celestial Bodies;bioinspired Navigation Based on Distributed Sensing in the Leech Using Dynamic Neural Fields;a Comparative Study of Reinforcement Learning and Insect-Inspired Visual Navigation Methods;erodium Awn’s Water Transport Insights for Controlled Swelling Agent Rearrangement in Anisotropic Structures;simulated Control of an Aquatic Serpentine robot with Stable Heteroclinic Channels;a Comparison of Model-Free Controllers for Trajectory Tracking in a Plant-Inspired Soft Arm;pulse Modulation in Braided Pneumatic Actuators Mimics Contractile Behavior of Biological Muscles;design of a Rat robotic Forelimb;cellular Plasticity Model for Bottom-Up robotic Design;vertical Closure Constraint for Self-replicating Machines;mechanical Design of a Feline robot for Dynamic Scaling Testing;moving Inward with Front Legs Improves Tripod Gaits for Crab-Like robot Walking in Sand;encoding 3D Leg Kinematics Using Spatially-Distributed, Population Coded Network Model;analysis Pipeline for High-Dimensional Neuromechanical Model Improvement;sequence Generator Network for Neuromechanical Control of Rat Hindlimbs.

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Masked Image Pretraining on Language Assisted Representation

Masked Image Pretraining on Language Assisted Representation

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Hou, Zejiang Kung, Sun-Yuan Princeton University United States

ISBN: (纸本)9798350368741

Self-attention based transformer models have been dominating many computer vision tasks in the past few years. Their superb model qualities heavily depend on the excessively large labeled image datasets. In order to reduce the reliance on large labeled datasets, reconstruction based masked autoencoders are gaining popularity, which learn high quality transferable representations from unlabeled images. For the same purpose, recent weakly supervised image pretraining methods explore language supervision from text captions accompanying the images. In this work, we propose Masked Image pretraining on Language Assisted representatioN, dubbed as MILAN. Instead of predicting raw pixels or low level features, our pretraining objective is to reconstruct the image features with substantial semantic signals that are obtained using caption supervision. Moreover, to accommodate our reconstruction target, we propose a more efficient prompting decoder architecture and a semantic aware mask sampling mechanism, which further advance the transfer performance of the pretrained model. Experimental results demonstrate that MILAN delivers higher accuracy than the previous works. When the masked autoencoder is pretrained and finetuned on ImageNet-1K dataset with an input resolution of 224×224, MILAN achieves a top-1 accuracy of 85.4% on ViT-Base, surpassing previous state-of-the-arts by 1%. In the downstream semantic segmentation task, MILAN achieves 52.7 mIoU using ViT-Base on ADE20K dataset, outperforming previous masked pretraining results by 4 points. © 2025 IEEE.

关键词： masked image pretraining vision transformer

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Exploring the Role of CLIP Global Visual Features in Multimodal Large Language Models

Exploring the Role of CLIP Global Visual Features in Multimo...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Bai, Zixing Bai, Yuting School of Computer Science and Technology Fudan University Shanghai China

ISBN: (纸本)9798350368741

The next recognized development direction of large language models (LLMs) is to integrate and enhance multimodal capability. Although current multimodal large language models (MLLMs) have achieved impressive performance by combining the pre-trained visual encoder CLIP and LLM, these works mainly focus on using the CLIP patch visual features. In practice, we find that CLIP global visual features are more efficient than patch visual features in some scenarios, especially in multimodal reasoning tasks. Therefore, we explore the role of CLIP global visual features and propose a new MLLM with the usage of full global visual features in this paper. Our model adopts the parameter-efficient transfer learning (PETL) method Adapter to fine-tune the pre-trained models and a simple MLP-based network to connect the visual encoder and LLM. To validate the performance, we evaluate our model on the first large-scale multimodal science question dataset, ScienceQA. Our model achieves a new state-of-the-art (SoTA) result of 93.96% on ScienceQA, which is higher than the previous SoTA result of 92.53%. © 2025 IEEE.

关键词： Language Multimodal Large Language Models vision

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Training Dialogue Systems by AI Feedback for Improving Overall Dialogue Impression

Training Dialogue Systems by AI Feedback for Improving Overa...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yoshida, Kai Mizukami, Masahiro Kawano, Seiya Kruengkrai, Canasai Sugiyama, Hiroaki Yoshino, Koichiro Nara Institute of Science and Technology Japan Guardian Robot Project RIKEN Japan NTT Communication Science Laboratories Japan Institute of Science Tokyo Japan

ISBN: (纸本)9798350368741

To improve user engagement during conversations with dialogue systems, we must improve individual dialogue responses and dialogue impressions such as consistency, personality, and empathy throughout the entire dialogue. While such dialogue systems have been developing rapidly with the help of large language models (LLMs), reinforcement learning from AI feedback (RLAIF) has attracted attention to align LLM-based dialogue models for such dialogue impressions. In RLAIF, a reward model based on another LLM is used to create a training signal for an LLM-based dialogue model using zero-shot/few-shot prompting techniques. However, evaluating an entire dialogue only by prompting LLMs is challenging. In this study, the supervised fine-tuning (SFT) of LLMs prepared reward models corresponding to 12 metrics related to the impression of the entire dialogue for evaluating dialogue responses. We tuned our dialogue models using the reward model signals as feedback to improve the impression of the system. The results of automatic and human evaluations showed that tuning the dialogue model using our reward model corresponding to dialogue impression improved the evaluation of individual metrics and the naturalness of the dialogue response. © 2025 IEEE.

关键词： Conversation System Dialogue System LLM RLAIF RLHF

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Improving Multimodal Large Language Models through Combining Resampler and MLP Projections

Improving Multimodal Large Language Models through Combining...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Bai, Zixing Bai, Yuting School of Computer Science and Technology Fudan University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

Current multimodal large language models (LLMs) achieve impressive performance through connecting visual encoders and LLMs by resampler or MLP projections. Although the MLP projections are effective and widely used in recent works when compared to resampler projections, it has the flaw of the inability to change the number of tokens, which limits the application scenarios. In this paper, we propose a new projection structure that inherits the efficiency of traditional MLP projections and has the ability to flexibly change the number of tokens. Furthermore, we propose a new multimodal LLM with the proposed projection applied. To validate the performance, we evaluate the proposed model on the first large-scale multimodal science question dataset ScienceQA, and the first multimodal LLM evaluation benchmark MME. Our model achieves a new state-of-the-art (SoTA) result of 94.27% on ScienceQA, which is higher than the previous SoTA result of 92.53% achieved by the LLaVA+GPT-4 judge model. On the MME benchmark, our model achieves better performance when compared to representative chatbot models BLIP-2 and InstructBLIP, with significantly lower training costs. © 2025 IEEE.

关键词： large language models Multimodal vision and language

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