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共 18110 条记录，以下是251-260 订阅

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Prototypical Part Transformer for Interpretable Image Recognition

Prototypical Part Transformer for Interpretable Image Recogn...

引用

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

作者： Anni Yu Yu-Bin Yang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Prototypical Part Networks facilitate interpretable decision-making processes, with the classification score computed by comparing test image patches to learned prototypes. Existing work typically learns prototypes from Convolutional Neural Networks (CNNs). However, learning prototypical parts directly in vision Transformers (ViTs) results in fragmented and noisy prototype activations. To address this, we quantify the dispersion of prototypes’ responsive regions with the Diffusion Index (DI). Subsequently, we propose Prototypical Part Transformer (PPTformer), an interpretable model designed to refine prototype learning in ViTs by introducing distinct prototypical branches, either involving the CLS token or not. In PPTformer, the prototype space is defined by orthogonal class-aware prototype vectors, ensuring disentanglement and informativeness. Additionally, classaware activation refinement is introduced to focus attention and reduce DI. Extensive experiments demonstrate that PPTformer outperforms state-of-the-art prototypical learning methods and its non-interpretable counterparts, providing faithful local and global explanations.

关键词： Learning systems Computer vision Image recognition Prototypes Transformers Vectors Indexes Noise measurement Speech processing Dispersion

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Contrastive Learning via Randomly Generated Deep Supervision

Contrastive Learning via Randomly Generated Deep Supervision

引用

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

作者： Shibo Wang Zili Ma Ka-Hou Chan Yue Liu Tong Tong Qinquan Gao Guangtao Zhai Xiaohong Liu Tao Tan Faculty of Applied Sciences Macao Polytechnic University China Ischemaview Inc Menlo Park CA USA College of Physics and Information Engineering Fuzhou University Fuzhou China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Unsupervised visual representation learning has gained significant attention in the computer vision community, driven by recent advancements in contrastive learning. Most existing contrastive learning frameworks rely on instance discrimination as a pretext task, treating each instance as a distinct category. However, this often leads to intra-class collision in a large latent space, compromising the quality of learned representations. To address this issue, we propose a novel contrastive learning method that utilizes randomly generated supervision signals. Our framework incorporates two projection heads: one handles conventional classification tasks, while the other employs a random algorithm to generate fixed-length vectors representing different classes. The second head executes a supervised contrastive learning task based on these vectors, effectively clustering instances of the same class and increasing the separation between different classes. Our method, Contrastive Learning via Randomly Generated Supervision(CLRGS), significantly improves the quality of feature representations across various datasets and achieves state-of-the-art performance in contrastive learning tasks.

关键词： Representation learning Visualization Computer vision signal processing algorithms Clustering algorithms Contrastive learning signal processing Vectors Robustness Speech processing

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Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking Neural Networks with Dynamic Time Steps for vision Transformers

Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking N...

引用

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

作者： Gourav Datta Zeyu Liu Anni Li Peter A. Beerel Dept. of Electrical Computer & Systems Engineering Case Western Reserve University Cleveland USA Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los Angeles USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN). These algorithms, when applied to the recently spotlighted vision transformers (ViT), either require a large number of time steps or fail to converge. Based on the analysis of the histograms of the ANN and SNN activation maps, we hypothesize that each ViT block has a different sensitivity to the number of time steps. We propose a novel training framework that dynamically allocates the number of time steps to each ViT module depending on a trainable score assigned to each timestep. In particular, we generate a scalar binary time step mask that filters spikes emitted by each neuron in a leaky-integrate-and-fire (LIF) layer. The resulting SNNs have high activation sparsity and require only accumulate operations (AC), except for the input embedding layer, in contrast to expensive multiply-and-accumulates (MAC) needed in traditional ViTs. This yields significant improvements in energy efficiency. We evaluate our training framework and resulting SNNs on image recognition tasks including CIFAR10, CIFAR100, and ImageNet with different ViT architectures. We obtain a test accuracy of 95.97% with 4.97 time steps with direct encoding on CIFAR10.

关键词： Training Computer vision Sensitivity Neurons signal processing algorithms Spiking neural networks Transformers Energy efficiency Convolutional neural networks Speech processing

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DKD2L: Dual Knowledge Distillation Dynamic Learning for sketch-based 3D shape retrieval

DKD2L: Dual Knowledge Distillation Dynamic Learning for sket...

引用

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

作者： Yawen Su Jing Bai Gan Lin School of Computer Science and Engineering North Minzu University Yinchuan China The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Sketch-based 3D shape retrieval has become a prominent area of research in computer vision, confronting challenges related to the inherent diversity and abstraction of sketches, as well as inter-domain discrepancies. This paper introduces a novel approach called Dual Knowledge Distillation Dynamic Learning (DKD 2 L) aimed at enhancing the extraction of spatio-temporal features for sketch-based 3D shape retrieval. We develop a temporal feature extraction network to effectively capture the dynamic temporal characteristics of sketches and improve retrieval efficiency through temporal knowledge distillation. Additionally, to tackle intra-class variation and inter-class imbalance, we apply semantic knowledge distillation, enabling the 3D shape network to guide the sketch network in capturing common semantic information. This approach facilitates precise cross-modal alignment and enhances retrieval accuracy. Extensive experiments on two benchmark datasets demonstrate that DKD 2 L surpasses existing state-of-the-art methods.

关键词： Knowledge engineering Computer vision Three-dimensional displays Shape Semantics signal processing Benchmark testing Feature extraction Acoustics Speech processing

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

Leveraging Multimodal Methods and Spontaneous Speech for Alz...

引用

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

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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 pretrained 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.

关键词： Technological innovation Linguistics signal processing Feature extraction Acoustics Alzheimer's disease Speech processing

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A Comparative Analysis of Denoising Techniques in Image processing 3rd

A Comparative Analysis of Denoising Techniques in Image Proc...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Jaiswal, Nitesh Kumar Srivastava, Subodh Mahto, Manipuran Department of Electronics and Communication Engineering NIT Patna Bihar Patna India

ISBN: (纸本)9789819743582

This paper presents the results obtained from an experimental study on the effectiveness of a noise reduction technique applied to medical images. This study compares the effectiveness of various noise-removal filters in order to determine which is the most efficient. Mode, median, Gaussian, adaptive median, non-local mean, wavelet, and deep learning-based denoising convolutional neural network (CNN) filters were used. This paper seeks to demonstrate experimental results on two types of noisy input images used: impulse noise and Gaussian noise, and compare them to the denoising effect of various methods. Among all the methods evaluated, the deep learning-based Convolutional Neural Network (CNN) approach demonstrates superior performance compared to the other techniques. While maintaining the edges and fine structures in the images, this method was able to eliminate both additive and multiplicative noise. The evaluation of these methods performance primarily relied on several metrics, including visual assessment, Mean Square Error (MSE), Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Normalized Absolute Mean (NAM), and Cross-Correlation (CC). These metrics provided a comprehensive analysis of the effectiveness and quality of the methods in reducing noise and preserving image details. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Attention Disentanglement for Semantic Diffusion Modeling in Text-to-Image Generation

Attention Disentanglement for Semantic Diffusion Modeling in...

引用

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

作者： Hsiang-Chun Yu Jen-Tsung Chien Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Text-to-image model has been recently improved to generate the semantically rich high-quality images by strengthening natural language processing via transformer in a stable diffusion process. However, the challenges are still remained in accurately rendering the objects, colors and compositions, and in precisely resolving the ambiguities in textual descriptions. This paper presents an attention disentanglment for semantic diffusion where the semantic consistency is enhanced for text-to-image generation. By utilizing the cross-attention maps in stable diffusion, this method is feasible to control the semantic features during generation process without the need for retraining. Additionally, a vision-language model is merged to implement this process to ensure that the generated images are closely aligned with the input prompts. This study further presents the attention disentanglement for semantic diffusion where the schemes of syntax parsing and information-theoretic learning are implemented to separate the semantic attributes and relations, thereby improving the accuracy and coherence of the generated images in the experiments.

关键词： Semantics Text to image Process control Syntactics signal processing Transformers Rendering (computer graphics) Natural language processing Speech processing Mutual information

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Rethinking Mamba in Speech processing by Self-Supervised Models

Rethinking Mamba in Speech Processing by Self-Supervised Mod...

引用

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

作者： Xiangyu Zhang Jianbo Ma Mostafa Shahin Beena Ahmed Julien Epps The University of New South Wales Dolby Laboratories

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The Mamba-based model has demonstrated outstanding performance across tasks in computer vision, natural language processing, and speech processing. However, in the realm of speech processing, the Mamba-based model’s performance varies across different tasks. For instance, in tasks such as speech enhancement and spectrum reconstruction, the Mamba model performs well when used independently. However, for tasks like speech recognition, additional modules are required to surpass the performance of attention-based models. We propose the hypothesis that the Mamba-based model excels in "reconstruction" tasks within speech processing. However, for "classification tasks" such as Speech Recognition, additional modules are necessary to accomplish the "reconstruction" step. To validate our hypothesis, we analyze the previous Mamba-based Speech Models from an information theory perspective. Furthermore, we leveraged the properties of HuBERT in our study. We trained a Mamba-based HuBERT model, and the mutual information patterns, along with the model’s performance metrics, confirmed our assumptions.

关键词： Analytical models Computational modeling Speech recognition Speech enhancement signal processing Natural language processing Decoding Speech processing Mutual information Standards

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Object Grasping Assist System with Reading Human Intention and Predicting Object Position

Object Grasping Assist System with Reading Human Intention a...

引用

international conference on Autonomous robots and Agents, ICARA

作者： Tomohiko Hayakawa Tomohiro Sueishi Yuka Hiruma Himari Tochioka Taku Senoo Shouren Huang Masatoshi Ishikawa Research Institute for Science and Technology Tokyo University of Science Tokyo Japan Faculty of Information Science and Technology Hokkaido University Hokkaido Japan

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

In recent years, research on handing an object to a person using a robot arm has been increasing. However, the dynamic characteristics of the robot arm have been an issue in improving high-speed performance. In this study, we developed a system helps a person who has intention to grasp an object at high speed by bouncing the target object when it senses the human hand's intention to grasp it, especially for objects that are too far away to be reached by human hands. High-speed cameras are used to detect a hand and the target object motion on a desk. Next, when the hand begins to move, the solenoid actuator pushes to bounce the object in the direction of the human hand. At the same time, the laser pattern is projected on the desk to predict the destination of the target motion. Experimental results using a lightweight cup as the target object showed that a laser pattern was displayed around 450 ms ago before the moving object stops, and a series of movements were faster when the system was used than when the system was not used.

关键词： Hands Actuators Image processing robot vision systems Grasping Parallel processing Manipulators Cameras Solenoids Laser applications

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Point-UMAE: Unet-like Masked Autoencoders for Point Cloud Self-supervised Learning

Point-UMAE: Unet-like Masked Autoencoders for Point Cloud Se...

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

作者： Hongliang Zeng Ping Zhang Fang Li Tingyu Ye Jiahua Wang Xianbo Yang South China University of Technology Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Masked Autoencoders (MAE) demonstrated exceptional performance in natural language processing and 2D vision tasks and have now been introduced into point cloud representation learning. We propose Point-UMAE, a novel self-supervised learning method based on a Unet-like structure, designed to enhance the capture of local details and global semantics in point clouds. Point-UMAE employs an asymmetric encoder-decoder architecture with a top-down fine-grained masking strategy to improve multi-scale consistency. The pre-trained model achieves state-of-the-art performance across various downstream tasks. Compared to the baseline Point-BERT, our method achieves a classification performance improvement of 1% and 4.14% on the ModelNet40 and ScanObjectNN datasets, respectively. We also investigate the impact of masking strategies and encoder structures on performance.

关键词： Point cloud compression Representation learning Three-dimensional displays Shape Autoencoders Semantics Training data Self-supervised learning signal processing Speech processing

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