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共 18085 条记录，以下是121-130 订阅

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MSRFormer: Hybrid Scale Self-Attention and Local Fast Convolution Transformer for Facial Expression Recognition

MSRFormer: Hybrid Scale Self-Attention and Local Fast Convol...

引用

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

作者： Shen, Zi-Qiang Tang, Yu-Yi Yan, Jun-Feng Li, Yang Zhao, Guo-Ying Hunan University of Chinese Medicine China Hunan Digital Chinese Medicine Engineering Technology Research Center China Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu Finland

ISBN: (纸本)9798350368741

With the emergence of the Transformer architecture, the accuracy of deep learning within the domain of facial emotion recognition has seen further enhancement. However, Transformer comes with increased training complexity and time due to the large parameter count. Additionally, the global receptive field in Transformer's attention leads to unnecessary computations for features with limited spatial extent in image sentiment analysis. In this paper we presents a MSRFormer model, which combines Hybrid-scale self-attention and local fast convolution to address existing issues. The Hybrid-scale self-attention enables precise focus on salient regions of the image, where key features are located. A Local Fast Convolution module was integrated into the MLP head of the Transformer model, enhancing training speed and reducing parameters while maintaining feature learning. we conducted experiments on FERPlus, RAF-DB, and KDEF datasets and confirming its strong performance and broad applicability in facial expression recognition. © 2025 IEEE.

关键词： facial expression recognition hybrid scale self attention local fast convolution transformer

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CEMSSL: Conditional Embodied Self-Supervised Learning is All You Need for High-precision Multi-solution Inverse Kinematics of robot Arms

CEMSSL: Conditional Embodied Self-Supervised Learning is All...

引用

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

作者： Qu Weiming Liu Tianlin Du Jiawei Luo Dingsheng School of Intelligence Science and Technology Peking University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of signal processing for robotics, the inverse kinematics of robot arms presents a significant challenge due to multiple solutions caused by redundant degrees of freedom (DOFs). Precision is also a crucial performance indicator for robot arms. Current methods typically rely on conditional deep generative models (CDGMs), which often fall short in precision. In this paper, we propose Conditional Embodied Self-Supervised Learning (CEMSSL) and introduce a unified framework based on CEMSSL for high-precision multi-solution inverse kinematics learning. This framework enhances the precision of existing CDGMs by up to 2-3 orders of magnitude while maintaining their original properties. Furthermore, our method is extendable to other fields of signal processing where obtaining multi-solution data in advance is challenging, as well as to other problems involving multi-solution inverse processes.

关键词： Kinematics Self-supervised learning signal processing Manipulators Acoustics Speech processing

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Investigation of the Impact of EEG signal processing Techniques on Classification Performance 3rd

Investigation of the Impact of EEG Signal Processing Techniq...

引用

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

作者： Babu, Shyam Wadhwani, Arun Kumar Department of Electrical Engineering Madhav Institute of Technology and Science Gwalior India

ISBN: (纸本)9789819743582

This research paper is aimed at a practical analysis of the impact of the electroencephalogram (EEG) signal processing techniques on novel classification algorithms. The study examines how newly developed classification algorithms may perform significantly depending on the methods used to interpret EEG signals (brain activity data). In this context, first, we have conducted a review on recently contributed research articles based on EEG signal classification. Using this review, we are trying to recover the essential datasets, features, feature selection techniques, classification techniques, and applications. Next, the work is extended to study the nature of EEG data and its influence on data scaling. Further, we have also investigated the performance influence of feature selection technique on scaled data. Additionally, the convolutional neural network has been trained to learn from scaled and non-scaled data. Using four different experimental scenarios we have evaluated the performance of implemented data scaling and feature selection techniques over the EEG signal classification. Based on experimental analysis we have found that standardization is much beneficial than normalization. Additionally, it also helps to improve training performance of a classification model. However, with the merging of principal component analysis and both the scaling technique, we found the PCA and standardization are providing better training yield, but at the time of validation the standardized data is superior then other implemented techniques. Finally, conclusion and future extension plan have been discussed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Optimal Maneuvering of Autonomous Wheeled Vehicles Based on Predictive Control Using Curvature Function

Optimal Maneuvering of Autonomous Wheeled Vehicles Based on ...

引用

Digital signal processing and its Applications (DSPA), international conference on

作者： Dmitry Khablov Laboratory of Technical Control Tools V. A. Trapeznikov Institute of Control Sciences of the RAS Moscow Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

In this work, we explore the possibilities of improving the characteristics of mobile robots based on wheeled vehicles, using the optimization of their movement along the route, taking into account kinematic characteristics and constraints. To do this, after selecting an array of waypoints and approximating it with splines, we divide the path into relatively linear segments and segments with significant maneuvering, for which we calculate the curvature profile. Further, during the movement, the predictive control module of the robot controller uses this curvature profile as a reference path, and to measure the current curvature, we use the microwave sensors of the wheels of the robot's differential drive.

关键词： Current measurement Wheels signal processing algorithms Microwave sensors robot sensing systems Time measurement Trajectory Mobile robots Standards Predictive control

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CPSNet: Comprehensive Enhancement Representation for Polyp Segmentation Task

CPSNet: Comprehensive Enhancement Representation for Polyp S...

引用

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

作者： Cai, Jiati Liu, Xiaogang Yang, Hongjie Ding, Yi Zhong, Ting Qin, Zhen Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Department of Gastroenterology and Hepatology University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Accurately segmenting polyp regions in colonoscopy images is crucial for the diagnosis and intervention of colorectal cancer. However, the task of polyp segmentation remains challenging due to the diverse size and shape variations among polyps, their extreme similarity to the background, and frequent rotation of the lens, which further increases the diversity in polyp presentation. To address these challenges effectively, we propose a comprehensive polyp segmentation network (CPSNet). Specifically, we introduce a Comprehensive Spatial Feature Extraction Module (CFEM) that progressively and densely integrates features while forming receptive windows with various shapes. This enables enhanced perception of polyps at manifold sizes and shapes. Additionally, we propose a Fine-grained Region Strengthen Module (FGSM) to supplement uncertain areas around polyps by mitigating background noise interference. In terms of training strategy, we further introduce a Rotation-augmented Constrained Loss (RC Loss), which reinforces consistency constraints on polyp images under multiple rotation angles. Qualitative and quantitative experiments conducted on five public datasets demonstrate both the plug-and-play capability of CFEM as well as the effectiveness and excellence achieved by CPSNet. © 2025 IEEE.

关键词： colonoscopy computer vision polyp segmentation representation enhancement

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Dual-Triple Transformer Networks for Accurate CT Pleural Effusion Segmentation

Dual-Triple Transformer Networks for Accurate CT Pleural Eff...

引用

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

作者： Yang, Jianwei Fan, Wenkang Fang, Hao Zhu, Zirui Luo, Xiongbiao Department of Computer Science and Technology Xiamen University Xiamen361102 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361102 China Zhongshan Hospital Xiamen University Xiamen361004 China

ISBN: (纸本)9798350368741

Pleural effusion segmentation in computed tomography images is essential to its precise diagnosis and treatment but remains challenging due to blurred boundaries, heterogeneous morphology, and low contrast with adjacent anatomical structures. This work shows a first study on pleural effusion segmentation by introducing a new deep learning architecture of dual-triple transformer networks. Specifically, this architecture builds a dual encoder of swin transformer and 3D deformable convolution, leveraging the multiscale representation capability to capture global contextual information and model complex deformations. Moreover, a triple decoder with a fusion module, a boundary-awareness mechanism, and a transposed-residual convolution block is introduced to effectively propagate these global and local features and refine the segmentation by mitigating ambiguity at the interfaces with surrounding tissues. We validate our method on 143 chest computed tomography scans. The experimental results demonstrate that our proposed model significantly outperforms state-of-the-art segmentation approaches, greatly improving the dice similarity coefficient and mean intersection over union while critically reducing both Hausdorff distance 95 and average symmetric surface distance. © 2025 IEEE.

关键词： Boundary Awareness Dual Encoder Image Segmentation Pleural Effusion vision Transformer

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Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images

Prompting DirectSAM for Semantic Contour Extraction in Remot...

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

作者： Miao, Shiyu Chen, Delong Liu, Fan Zhang, Chuanyi Gu, Yanhui Guo, Shengjie Zhou, Jun College of Computer Science and Software Engineering Hohai University Nanjing China Hong Kong University of Science and Technology Hong Kong School of Computer and Electronic Information Nanjing Normal University Nanjing China School of Information and Communication Technology Griffith University QLD Australia

ISBN: (纸本)9798350368741

The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where semantic contour extraction-such as identifying buildings, road networks, and coastlines-holds significant practical value. Those applications are currently handled via training specialized small models separately on small datasets in each domain. We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which not only inherits the strong segmentation capability acquired from natural images, but also benefits from a large-scale dataset we created for remote sensing semantic contour extraction. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. DirectSAM-RS integrates a prompter module: a text encoder and cross-attention layers attached to the DirectSAM architecture, which allows flexible conditioning on target class labels or referring expressions. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks. © 2025 IEEE.

关键词： Remote sensing Semantic contour extraction vision-language learning

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Semantic-oriented Visual Prompt Learning for Class Incremental Learning

Semantic-oriented Visual Prompt Learning for Class Increment...

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

作者： Guo, Shuai Gu, Yang Ma, Yuan Zhang, Yingwei Weng, Weining Liu, Jun Dai, Weiwei Chen, Yiqiang Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Digital Ophthalmology and Visual Science Changsha Aier Eye Hospital Changsha China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350368741

Class-incremental learning (CIL) enables models to continuously learn new classes while addressing catastrophic forgetting. With the introduction of pre-trained models, new tuning paradigms have emerged for CIL. This paper revisits parameter-efficient fine-tuning (PEFT) methods in the context of incremental learning. Prior studies reveal that PEFT methods' extended parameters do not directly contribute to semantic perception, limiting performance with significant category and domain gaps. To address this, we propose semantic-oriented visual prompt learning (SVPL), which enhances semantic perception and improves task-specific knowledge extraction. SVPL assigns learnable prompts to each class, using a contrastive group alignment to align prompts to task-specific semantic spaces, thus preserving relationships between old and new knowledge. Additionally, hierarchical semantic delivery allows the semantic transformation of prompt groups from shallow to deep layers to facilitate efficient knowledge mining and enable effective learning of new knowledge. Extensive experimental results on five benchmarks demonstrate the superior performance of our methods. © 2025 IEEE.

关键词： catastrophic forgetting class incremental learning pre-trained model prompt learning vision transformer

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LLaVA-SG: Leveraging Scene Graphs as Visual Semantic Expression in vision-Language Models

LLaVA-SG: Leveraging Scene Graphs as Visual Semantic Express...

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

作者： Jingyi Wang Jianzhong Ju Jian Luan Zhidong Deng Department of Computer Science and Technology BNRist THUAI Tsinghua University Beijing China Xiaomi AI Lab Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advances in large vision-language models (LVLMs) typically employ vision encoders based on the vision Transformer (ViT) architecture. The division of the images into patches by ViT results in a fragmented perception, thereby hindering the visual understanding capabilities of LVLMs. In this paper, we propose an innovative enhancement to address this limitation by introducing a Scene Graph Expression (SGE) module in LVLMs. This module extracts and structurally expresses the complex semantic information within images, thereby improving the foundational perception and understanding abilities of LVLMs. Extensive experiments demonstrate that integrating our SGE module significantly enhances the LVLM’s performance in vision-language tasks, indicating its effectiveness in preserving intricate semantic details and facilitating better visual understanding.

关键词： Visualization Computer vision Semantics signal processing Transformers Acoustics Data mining Speech processing

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HaT: Holding Oscillation for Efficient INT8 Quantized CNN Training

HaT: Holding Oscillation for Efficient INT8 Quantized CNN Tr...

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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Chanyung Shin, Dongyeob Lee, Minkyu Jang, Sung-Joon Lee, Sang-Seol Intelligent Image Processing Research Center Korea Electronics Technology Institute Gyeonggi-do Korea Republic of

ISBN: (纸本)9798331510756

Reducing model capacity through low-bit quantization while maintaining performance is a challenging task in deep learning. Quantization during training is particularly difficult compared to quantization in inference models due to issues such as small gradients and quantization errors resulting from weight rounding. A significant issue is weight oscillations, where weights fluctuate between adjacent quantization levels. This introduces noise, leading to decreased training stability and performance degradation. Various methods have been proposed to address this problem. However, most focus on stabilizing the later stages of training or introduce additional computational overhead due to complex algorithms. This study approaches the oscillation phenomenon from a more detailed perspective, focusing on layer-wise, channel-level oscillations throughout the entire training process and their impact on training instability. The proposed Holding and Training (HaT) method continuously monitors oscillations from the early stages of training, temporarily holding channels with high variance, and focusing on the training of the remaining channels. This strategy enhances training stability while simultaneously reducing computational cost. Applying the proposed HaT method to ResNet-20 resulted in up to a 3% improvement in performance and up to a 2 % increase in training speed across various datasets. © 2025 IEEE.

关键词： Convolutional neural networks

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