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共 18111 条记录，以下是341-350 订阅

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PNetGPT: Proprietary Protocol Network Traffic Generation with Pre-trained Transformer

PNetGPT: Proprietary Protocol Network Traffic Generation wit...

引用

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

作者： Zedong Li Shijie Li Dongliang Fang Xin Chen Zhanwei Song Zhi Li Shichao Lv Limin Sun Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generative pre-trained transformers are exceedingly effective as generative models and classifiers, widely used in natural language processing and computer vision. This work contributes to the exploration of generative pre-trained transformer-based models in the proprietary protocol network traffic. However, building a pre-trained model for proprietary protocol network traffic is non-trivial due to the heterogeneous unknown formats and the extreme scarcity of proprietary protocol network traffic datasets. In this paper, we present PNetGPT, a pre-trained transformer-based model for generating proprietary protocol network traffic. We have constructed the inaugural dataset of 2 real-world proprietary protocols. After training on this dataset, PNetGPT possesses the capacity to generate high-quality proprietary protocol network traffic to support various applications of proprietary protocols, including reverse analysis, protocol fuzzy testing, intrusion detection, etc. We evaluated PNetGPT with two real proprietary protocols and demonstrated state-of-the-art (SOTA) performance in handling heterogeneous unknown formats. The code and datasets are available at: https://***/Snail1502/PNetGPT

关键词： Training Protocols Computational modeling Scalability Intrusion detection Telecommunication traffic signal processing Transformers Natural language processing Speech processing

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Beyond Point Annotation: A Weakly Supervised Network Guided by Multi-Level Labels Generated from Four-Point Annotation for Thyroid Nodule Segmentation in Ultrasound Image

Beyond Point Annotation: A Weakly Supervised Network Guided ...

引用

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

作者： Jianning Chi Zelan Li Huixuan Wu Wenjun Zhang Ying Huang Faculty of Robot Science and Engineering Northeastern University Shenyang China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Department of Ultrasound Shengjing Hospital of China Medical University Shenyang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Weakly supervised methods typically guided the pixel-wise training by comparing the predictions to single-level labels containing diverse segmentation-related information at once, but struggled to represent subtle feature differences between nodule and background regions and confused incorrect information, resulting in under-fitting or over-fitting in the segmentation predictions. This work proposes a weakly supervised network that generates multi-level labels from four-point annotation to refine diverse constraints for delicate nodule segmentation. The Distance-Similarity Fusion Prior referring to the points annotations filters out information irrelevant to nodules. The bounding box and pure foreground/background labels, generated from the point annotation, guarantee the rationality of the prediction in the arrangement of target localization and the spatial distribution of target/background regions, respectively. Our proposed network outperforms existing weakly supervised methods on two public datasets with respect to accuracy and robustness, improving the applicability of deep-learning-based segmentation in the clinical practice of thyroid nodule diagnosis.

关键词： Training Image segmentation Accuracy Ultrasonic imaging Annotations Refining signal processing algorithms Robustness Speech processing Thyroid

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PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization

PromptTA: Prompt-driven Text Adapter for Source-free Domain ...

引用

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

作者： Haoran Zhang Shuanghao Bai Wanqi Zhou Jingwen Fu Badong Chen Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Source-free domain generalization (SFDG) tackles the challenge of adapting models to unseen target domains without access to source domain data. To deal with this challenging task, recent advances in SFDG have primarily focused on leveraging the text modality of vision-language models such as CLIP. These methods involve developing a transferable linear classifier based on diverse style features extracted from the text and learned prompts or deriving domain-unified text representations from domain banks. However, both style features and domain banks have limitations in capturing comprehensive domain knowledge. In this work, we propose Prompt-Driven Text Adapter (PromptTA) method, which is designed to better capture the distribution of style features and employ resampling to ensure thorough coverage of domain knowledge. To further leverage this rich domain information, we introduce a text adapter that learns from these style features for efficient domain information storage. Extensive experiments conducted on four benchmark datasets demonstrate that PromptTA achieves state-of-the-art performance. The code is available at https://***/zhanghr2001/PromptTA.

关键词： Adaptation models Codes Benchmark testing signal processing Feature extraction Data models Acoustics Speech processing

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Explainable Detection of Alzheimer’s Disease Through Analysis of Human Behavior in Video

Explainable Detection of Alzheimer’s Disease Through Analys...

引用

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

作者： Bao-Hsuan Huang Po-Chih Kuo Likai Huang Chaur-Jong Hu Cheng-Yu Chen Department of Computer Science National Tsing Hua University Hsinchu Taiwan Center and Department of Neurology Shuang-Ho Hospital New Taipei City Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diagnostic methods for Alzheimer’s disease (AD), such as brain imaging and cognitive impairment questionnaires, are costly and time-consuming, making early detection challenging. In this work, we develop a computer vision-based method to detect AD using behavioral data collected from the Timed Up and Go (TUG) test and the Cookie Theft (CT) picture description task. By analyzing body joints and facial landmarks through Convolutional Neural Networks and Support Vector Machines, we classified subjects into AD and Non-AD categories across four subtasks: Walking, Sit-Stand, Turning, and Describing. Our approach achieved an F1-score of 0.92±0.03, demonstrating the potential of video-based analysis for AD detection. To enhance the explainability of our model, we applied model explanation methods, identifying key features and symptoms involved in the decision-making process.

关键词： Support vector machines Neuroimaging Computational modeling Medical services signal processing Turning Behavioral sciences Alzheimer's disease Speech processing Medical diagnostic imaging

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VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection

VisTa: Visual-contextual and Text-augmented Zero-shot Object...

引用

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

作者： Bin Zhang Xiaoyang Qu Guokuan Li Jiguang Wan Jianzong Wang Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Ping An Technology (Shenzhen) Co. Ltd Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As object detectors are increasingly deployed as black-box cloud services or pre-trained models with restricted access to the original training data, the challenge of zero-shot object-level out-of-distribution (OOD) detection arises. This task becomes crucial in ensuring the reliability of detectors in open-world settings. While existing methods have demonstrated success in image-level OOD detection using pre-trained vision-language models like CLIP, directly applying such models to object-level OOD detection presents challenges due to the loss of contextual information and reliance on image-level alignment. To tackle these challenges, we introduce a new method that leverages visual prompts and text-augmented in-distribution (ID) space construction to adapt CLIP for zero-shot object-level OOD detection. Our method preserves critical contextual information and improves the ability to differentiate between ID and OOD objects, achieving competitive performance across different benchmarks.

关键词： Training Visualization Adaptation models Training data Detectors Benchmark testing signal processing Reliability Speech processing Context modeling

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Learning Hierarchical Attribute Prompt for vision-Language Models

Learning Hierarchical Attribute Prompt for Vision-Language M...

引用

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

作者： Jun Liang Yang Peng Rui Luo Yunyu Zou Yalong Cheng Bingzhi Chen School of Artificial Intelligence South China Normal University Foshan China School of Artificial Intelligence Beijing Institute of Technology Zhuhai Zhuhai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Prompt learning is a common strategy for adapting Visual Language Models (VLMs) to downstream tasks by fine-tuning prompts for task-specific performance. However, existing methods face two key challenges: overfitting to base classes, which limits generalization to novel classes, and the dependence on manually generated or LLM-based descriptions, which are time-consuming and error-prone. To address these issues, we propose the Learning Hierarchical Attribute Prompt (LHAP) method, which introduces fine-grained semantic alignment through hierarchical prompts. By autonomously extracting visual attributes from images, LHAP generates local-level prompts (LLP) to capture fine-grained semantics and global-level prompts (GLP) to model overall semantics. The combination of LLP and GLP not only improves generalization but also mitigates errors and inefficiencies from manual or LLM-based descriptions. Extensive experiments on multiple benchmark datasets consistently demonstrate the superiority and robustness of LHAP over state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Focusing signal processing Benchmark testing Robustness Speech processing Faces Overfitting

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Impact of Glyph Information on Latent Space Diffusion Models for Accurate Handwritten Text Generation

Impact of Glyph Information on Latent Space Diffusion Models...

引用

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

作者： Ying-Li Lin Hao-Chung Cheng Chung-I Huang Chien-Yao Wang Jia-Ching Wang National Central University Taoyuan Taiwan National Center for High-Performance Computing Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The generation of high-quality stylized handwritten text images is a challenging task in computer vision and artificial intelligence. While advanced approaches using Latent Diffusion Models (LDMs) for generating stylized handwritten text have shown effectiveness, they often struggle with maintaining the structural integrity of certain characters, resulting in issues such as missing or extraneous strokes. In this work, we propose GlyphLDM, an innovative model that integrates glyph image information into both the diffusion and denoising processes in the latent space, enhancing the structural accuracy of generated text images. In the early training stages, our method demonstrated a significant improvement in the structural accuracy of the generated text images, with the Average Confidence Score increasing by approximately 40% compared to the baseline method. These experimental results indicate that incorporating glyph image information has promising potential to enhance the structural accuracy and overall quality of generated text images. This approach provides an effective solution for generating more accurate and diverse handwritten text images.

关键词： Training Measurement Handwriting recognition Accuracy Optical character recognition Noise reduction Diffusion processes signal processing Diffusion models Speech processing

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Dual-Space Augmented Intrinsic-LoRA for Wind Turbine Segmentation

Dual-Space Augmented Intrinsic-LoRA for Wind Turbine Segment...

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

作者： Shubh Singhal Raül Pérez-Gonzalo Andreas Espersen Antonio Agudo Institut de Robòtica i Informàtica Industrial CSIC-UPC Barcelona Spain Université de Toulon La Garde France Wind Power LAB Copenhagen Denmark

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurate segmentation of wind turbine blade (WTB) images is critical for effective assessments, as it directly influences the performance of automated damage detection systems. Despite advancements in large universal vision models, these models often underperform in domain-specific tasks like WTB segmentation. To address this, we extend Intrinsic LoRA for image segmentation, and propose a novel dual-space augmentation strategy that integrates both image-level and latent-space augmentations. The image-space augmentation is achieved through linear interpolation between image pairs, while the latent-space augmentation is accomplished by introducing a noise-based latent probabilistic model. Our approach significantly boosts segmentation accuracy, surpassing current state-of-the-art methods in WTB image segmentation.

关键词： Image segmentation Interpolation Accuracy Wind energy Blades Probabilistic logic Data models Wind turbines Sustainable development Speech processing

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Diffusion-based Data Augmentation for Object Counting Problems

Diffusion-based Data Augmentation for Object Counting Proble...

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

作者： Zhen Wang Yuelei Li Jia Wan Nuno Vasconcelos Department of Electrical and Computer Engineering University of California San Diego Harbin Institute of Technology (Shenzhen)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Crowd counting, an important problem in computer vision, is commonly solved with deep learning approaches, such as convolutional networks and transformers. However, Deep networks often overfit when the available labeled crowd data is scarce. To overcome this, we have designed a pipeline that utilizes a diffusion model to generate extensive training data. We pioneer using diffusion models to generate images from high-density head location dot maps (a binary dot map that specifies the location of human heads) and are the first to use these diverse synthetic data to augment the crowd counting models. Our proposed smoothed density map input for ControlNet significantly improves ControlNet’s performance in generating crowds in the correct locations. Also, our proposed counting loss and guidance sampling for the diffusion model effectively minimize the discrepancies between the location dot map and the crowd images generated. Moreover, our versatile framework can be easily adapted to all kinds of counting problems. Extensive experiments demonstrate that our framework improves the counting performance on the ShanghaiTech, NWPU-Crowd, UCF-QNRF, and TRANCOS datasets.

关键词： Deep learning Head Pipelines Training data signal processing Diffusion models Transformers Data models Speech processing Synthetic data

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Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning

Semantic Hierarchical Prompt Tuning for Parameter-Efficient ...

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

作者： Haowei Zhu Fangyuan Zhang Rui Qin Tianxiang Pan Junhai Yong Bin Wang School of Software Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As the scale of vision models continues to grow, Visual Prompt Timing (VPT) has emerged as a parameter-efficient transfer learning technique, noted for its superior performance compared to full fine-tuning. However, indiscriminately applying prompts to every layer without considering their inherent correlations, can cause significant disturbances, leading to suboptimal transferability. Additionally, VPT disrupts the original self-attention structure, affecting the aggregation of visual features, and lacks a mechanism for explicitly mining discriminative visual features, which are crucial for classification. To address these issues, we propose a Semantic Hierarchical Prompt (SHIP) fine-tuning strategy. We adaptively construct semantic hierarchies and use semantic-independent and semantic-shared prompts to learn hierarchical representations. We also integrate attribute prompts and a prompt matching loss to enhance feature discrimination and employ decoupled attention for robustness and reduced inference costs. SHIP significantly improves performance, achieving a 4.9% gain in accuracy over VPT with a ViT-B/16 backbone on VTAB-1k tasks. Our code is available at https://***/haoweiz23/SHIP.

关键词： Visualization Semantics Transfer learning signal processing Performance gain Robustness Timing Marine vehicles Speech processing Tuning

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