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共 18083 条记录，以下是41-50 订阅

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GPA: Enhancing Generalizable Physical Adversarial Attacks Across Multiple vision Tasks

GPA: Enhancing Generalizable Physical Adversarial Attacks Ac...

引用

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

作者： Xie, Mingye Xiang, Suncheng Ruan, Jiacheng Gao, Xian Yu, Zefang Liu, Ting Fu, Yuzhuo Shanghai Jiao Tong University China

ISBN: (纸本)9798350368741

Adversarial attacks pose a significant challenge in deep learning, as carefully crafted perturbations can severely degrade even the most advanced models. In real-world scenarios, where the target models are often unknown, previous works often focus on creating adversarial patterns for specific known models, with the goal of generalizing these patterns to other models. However, such attacks rely heavily on prior model information, leading to poor generalization. To overcome this, we propose a novel method called GPA. Our solution includes an attention extraction module based on a pre-trained vision encoder, which captures precise and generalizable features of model attention on objects. We also introduce attack loss functions that divert attention away from target objects. Compared to state-of-the-art methods, our approach achieves superior attack performance across various downstream vision tasks, including object detection, instance segmentation, and depth estimation. Moreover, the adversarial patterns generated by GPA maintain their effectiveness in real-world scenarios. © 2025 IEEE.

关键词： adversarial attack instance segmentation object detection physical attack

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CAU-Net: U-Shaped medical image segmentation network based on compound attention

CAU-Net: U-Shaped medical image segmentation network based o...

引用

2024 international conference on Computer vision and Image processing, CVIP 2024

作者： Liu, Jiaqi Mo, Jiaqing Xinjiang University School of Computer Science and Technology Key Laboratory of Signal Detection and Processing No.777 Huarui Street Shuimogou District Xinjiang Urumqi830017 China

ISBN: (纸本)9781510688384

In the context of the ongoing advancement of deep learning technologies, traditional image segmentation approaches are gradually become difficult for adapted to the requirements of medical imaging. The importance of medical image segmentation models is increasing, and they play a key role in accurately delineating anatomical structures and lesion regions. This is crucial for accurate diagnosis of diseases, formulation of treatment plans, and monitoring of disease course. In this paper, we propose a compound attention image segmentation model, CAU-Net, which aims to improve the segmentation accuracy of skin lesion images by introducing the receptive field module (RFB), the double squeezing-excitation (DSE) module, and our innovatively proposed compound attention (CA) module lesion image segmentation accuracy. The design of CAU-Net focuses on enhancing the feature extraction capability and contextual information capturing ability, and improving the edge segmentation capability and play a strong role in the processing of medical images with complex edges and chaotic backgrounds. The model reached an mDice to 0.9408 and a recall to 0.9418 on the ISIC 2018 dataset. Future work can further optimize these modules, extend to more medical image tasks. © 2025 SPIE.

关键词： Image segmentation

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Multi-Microphone Speech Emotion Recognition Using the Hierarchical Token-Semantic Audio Transformer Architecture

Multi-Microphone Speech Emotion Recognition Using the Hierar...

引用

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

作者： Cohen, Ohad Hazan, Gershon Gannot, Sharon Faculty of Engineering Bar-Ilan University Ramat-Gan Israel

ISBN: (纸本)9798350368741

The performance of most emotion recognition systems degrades in real-life situations ("in the wild" scenarios) where the audio is contaminated by reverberation. Our study explores new methods to alleviate the performance degradation of Speech Emotion Recognition (SER) algorithms and develop a more robust system for adverse conditions. We propose processing multi-microphone signals to address these challenges and improve emotion classification accuracy. We adopt a state-of-the-art transformer model, the Hierarchical Token-semantic Audio Transformer (HTS-AT), to handle multi-channel audio inputs. We evaluate two strategies: averaging mel-spectrograms across channels and summing patch-embedded representations. Our multi-microphone model achieves superior performance compared to single-channel baselines when tested on real-world reverberant environments. © 2025 IEEE.

关键词： human-robot interaction speech emotion recognition

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Detection method of abnormal load state of diesel generator set based on PANet network and IMF 4

Detection method of abnormal load state of diesel generator ...

引用

4th international conference on Computer vision, Application, and Algorithm, CVAA 2024

作者： Wang, Zhiguang Lu, Zhonghao Ren, Xudong Guo, Dewen Ren, Dongxu The First Company of China Construction Sixth Engineering Bureau Corp. Ltd. Guangzhou510535 China The First Company of China Construction Sixth Engineering Bureau Corp. Ltd. Yibin644000 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Aiming at the problems of missing smooth data, limited detection range and high false alarm rate in diesel generator set load state anomaly detection, an improved method based on PANet network and IMF is proposed. Through multi-sensor data acquisition and pre-processing, the anomaly detection model is constructed by combining PANet network and IMF to achieve comprehensive coverage and high accuracy of load state detection. Experiments prove that the method has excellent performance and is close to the ideal detection effect. © 2025 SPIE.

关键词： Data modeling Vibration Data acquisition Feature extraction signal detection Smoothing Coastal modeling Denoising Engineering signal generators

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HiE-VL: A Large vision-Language Model with Hierarchical Adapter for Handwritten Mathematical Expression Recognition

HiE-VL: A Large Vision-Language Model with Hierarchical Adap...

引用

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

作者： Guo, Hong-Yu Yin, Fei Xu, Jian Liu, Cheng-Lin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Large vision-Language Models (LVLMs) have shown impressive capabilities across various domains, but existing LVLMs have limited performance in dense perception and structured learning problems, such as Handwritten Mathematical Expression Recognition (HMER). The primary challenges stem from the complexity of formula images comprising multiple symbols and complicated inter-symbol relationships. This poses difficulties to LVLMs with locality insensitive visual encoders and structure-agnostic vision-language projectors. To overcome these challenges, we propose HiE-VL, the first LVLM for HMER containing: (1) a primitive-aware high-resolution visual encoder, (2) a hierarchical adapter, (3) a math-context enhanced large language model (LLM). Specifically, the adopted visual encoder allows locating and recognizing symbols in complex formula images. The hierarchical adapter functions as a vision-language projector to progressively capture primitive and structure information for facilitating expression decoding. The whole model is optimized in a two-stage training pipeline. In experiments on two benchmark datasets of HMER, our model achieves significantly higher performance than existing LVLMs like GPT-4V and state-of-the-art HMER models. Our codes are available at https://***/guohy17/HiE-VL. © 2025 IEEE.

关键词： handwritten mathematical expression recognition instruction tuning pre-training vision-language model

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Diffusion Models are Zero-Shot Generative Text-vision Retrievers

Diffusion Models are Zero-Shot Generative Text-Vision Retrie...

引用

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

作者： Li, Bao Xie, Zeke Zhang, Xiaomei Zhu, Xiangyu Lei, Zhen MAIS Institute of Automation Chinese Academy of Sciences Beijing China School of Artifical Intelligence University of Chinese Academy of Sciences Beijing China Guangzhou China CAIR HKISI Chinese Academy of Sciences Hong Kong

ISBN: (纸本)9798350368741

Large-scale text-to-image diffusion models have demonstrated impressive capabilities for downstream tasks by leveraging strong vision-language alignment from generative pre-training. Recently, a number of works have explored how to use the power of text-to-image diffusion models for text-image matching tasks. While previous generative text-image matching methods have shown potential in retrieving the most challenging candidates, they still suffer from extremely slow retrieval speeds and lack the ability to handle temporal dimensions, making them impractical for text-video retrieval. In this paper, we propose ZSGenRet, a simple yet effective zero-shot generative text-vision retrieval framework for both text-image and text-video tasks, based on pre-trained text-to-image diffusion models. We further incorporate inversion saliency detection to identify key frames in videos and enhance the semantic representation of the vision encoder. Experimental results demonstrate that ZSGenRet significantly improves text-video retrieval performance and achieves competitive results on text-image retrieval while remarkably improving efficiency. To the best of our knowledge, the proposed ZSGenRet is the first to explore zero-shot text-video retrieval based on diffusion models. © 2025 IEEE.

关键词： diffusion models Text-vision retrieval vision-language models zero-shot learning

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Enhancing Road Navigation for the Visually Impaired: A Laser-Based Night vision Approach 4

Enhancing Road Navigation for the Visually Impaired: A Laser...

引用

4th international conference on robotics, Electrical and signal processing Techniques, ICREST 2025

作者： Sakib, Fahim Faysal Akash, Nasin Ayenul Rayied, Shahzad Hussain Juel, Md. Sajjadul Islam Hasan, Tabin American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331530938

This study proposes a Convolutional Neural Network (CNN) based system to aid visually impaired individuals by classifying road surfaces. Leveraging image analysis and processing techniques, the model uses laser beams to identify road types under different conditions, including low-light scenarios. The challenge faced by visually impaired people in navigating roads with traditional white canes limited in scope and effectiveness motivated this research. The system incorporates a custom dataset of over 3000 high-quality images, employing contour detection to recognize obstacles and road types from various distances. The model achieves an accuracy exceeding 99% indicating reliability. This advancement offers a significant improvement in navigation and safety providing a practical solution for enhancing mobility and independence for visually impaired users. Future work will focus on expanding the model to detect additional objects further enhancing user safety and environmental awareness. © 2025 IEEE.

关键词： Laser beams

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Reward Generation via Large vision-Language Model in Offline Reinforcement Learning

Reward Generation via Large Vision-Language Model in Offline...

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

作者： Lee, Younghwan Luu, Tung M. Lee, Donghoon Yoo, Chang D. Electrical Engineering KAIST Daejeon Korea Republic of Robotics Program KAIST Daejeon Korea Republic of

ISBN: (纸本)9798350368741

In offline reinforcement learning (RL), learning from fixed datasets presents a promising solution for domains where real-time interaction with the environment is expensive or risky. However, designing dense reward signals for offline dataset requires significant human effort and domain expertise. Reinforcement learning with human feedback (RLHF) has emerged as an alternative, but it remains costly due to the human-in-the-loop process, prompting interest in automated reward generation models. To address this, we propose Reward Generation via Large vision-Language Models (RG-VLM), which leverages the reasoning capabilities of LVLMs to generate rewards from offline data without human involvement. RG-VLM improves generalization in long-horizon tasks and can be seamlessly integrated with the sparse reward signals to enhance task performance, demonstrating its potential as an auxiliary reward signal. © 2025 IEEE.

关键词： Large vision-Language Model Offline Reinforcement Learning Reward Labeling

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Robust CLIP-Guided Deep Thinking: A Two-Stage Optimization Strategy for Enhancing Adversarial Robustness and Reliability in LVLMs

Robust CLIP-Guided Deep Thinking: A Two-Stage Optimization S...

引用

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

作者： Sui, Yize Huang, Wanrong Yang, Wenjing Zhao, Chaofan Ren, Jing Wang, Ji College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9798350368741

Large vision-Language models (LVLMs) have demonstrated remarkable performance in a wide range of vision-language tasks as an efficient input/output system. However, the lack of adversarial robustness at the input side and the widespread hallucination phenomenon at the output side significantly undermine user trust in them. Current solutions to the former tend to sacrifice the general performance of LVLMs, while solving the latter requires a large amount of engineering costs. To address these challenges, we propose a two-stage optimization strategy called RCDT (Robust CLIP-guided Deep Thinking), which aims to enhance the adversarial robustness of LVLMs with minimal general performance loss while reducing hallucinations. First, we introduce a constrained adversarial fine-tuning approach for CLIP to limit the general performance loss during the enhancement of robustness. Furthermore, this CLIP is used to think deeply about the output process of LVLMs to reduce hallucinations. Experiments show that RCDT not only reduce general performance loss by more than half while maintaining adversarial robustness compared to the baselines, but also demonstrate good performance in mitigating hallucinations. © 2025 IEEE.

关键词： adversarial robustness hallucination trusted large vision-language model

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Application of Computer vision Technology in the Field of Automatic Target Reporting 14th

Application of Computer Vision Technology in the Field of Au...

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14th international conference on Frontier Computing, FC 2024

作者： Zhu, Rui Yu, Jie Yao, Fushan Shi, Wenhua Army Academy of Armored Force Jilin Changchun China The Army of 95795 Guangxi Guilin China

ISBN: (纸本)9789819627936

Computer vision is to measure and judge by machine instead of man, and convert the captured target scene into image signal through camera device. Transmitting it to the image processing system and converting it into a digital signal through the significant mark of the image;Through the calculation of the digital signal, the characteristics of the target are obtained, so as to analyze, sort out, or obtain the judgment results. It is applied to the field of automatic target reporting, so as to improve the efficiency and quality. In view of the problems existing in manual target detection, combined with the actual needs, this paper intends to apply computer vision technology to automatic target reporting, focusing on the basic principle and system composition of automatic target reporting. Combined with the actual application, this paper studies and demonstrates the highly feasible automatic target reporting technology and forms a summary report, Lay a foundation for the subsequent research and development of automatic target reporting system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine vision

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