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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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A Comparative Study of CNN and Transformer Models for Image recognition in Autonomous Driving 2

A Comparative Study of CNN and Transformer Models for Image ...

引用

2nd International conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Gupta, Rahul Singh, Shashank Yadav, Vandana Dhanda, Namrata Sharma, Pakhi Monad University U.P. Hapur India Department of Computer Science & Engineering S.R. Institute of Management & Technology Lucknow India Department of Computer Science & Engineering Amity University U.P. Lucknow India

ISBN: (纸本)9798331530389

An autonomous driving system requires efficient image recognition to interpret the environment, detect obstacles, and make real-time decisions. This study compares Convolutional Neural Networks (CNNs) and vision Transformers (ViTs) for image recognition tasks in autonomous driving. CNNs achieve higher accuracy (92.5% vs. 91.2%) and faster inference times (25ms vs. 35ms), making them more suitable for real-time applications. ViTs, however, perform better in challenging conditions, such as low lighting and occlusions, by capturing detailed information across entire images. The findings suggest that both architectures offer distinct advantages and could be combined to enhance the reliability and efficiency of autonomous driving systems. © 2025 ieee.

关键词： Convolutional neural networks

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A Novel Framework for Human Action recognition Based on Features Fusion and Decision Tree 4

A Novel Framework for Human Action Recognition Based on Feat...

引用

4th International conference on Advancements in Computational Sciences, ICACS 2023

作者： Bukht, Tanvir Fatima Naik Rahman, Hameedur Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9781665463775

Image-based detection of human actions has recently emerged as a hot research area in the fields of computer vision and pattern recognition. It is concerned with detecting a person's actions or behavior from a static image. In this article, we are using a decision tree to develop an action recognition technique. In order to enhance the clarity of the video frames, the proposed method begins by implementing the HSI color transformation in the initial stage. Subsequently, it utilizes filters to minimize noise. The silhouette is extracted using a statistical method. We use SIFT and ORB for feature extraction. Next, using a parallel process, extract the shape and texture features needed for fusion applying name length control features. Additionally, the best high-dimensional data for classification is explored using vectors and the t-distributed stochastic neighbour embedding (t-SNE).The final step involves the features to be input into a decision tree, where they will be sorted into relevant human actions based on those final characteristics. The recognition rate of the UT interaction data used in the experimental process is 94.6%. © 2023 ieee.

关键词： Decision trees

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Comparative Analysis of Deep Learning Architectures for Multilingual Digit recognition 2

Comparative Analysis of Deep Learning Architectures for Mult...

引用

2nd ieee International conference on Data Science and Network Security, ICDSNS 2024

作者： Mamatha Bai, B.G. Sarangi, Akruti Kashyap, Akanksha Yadav, Himanshi Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350373110

Digit recognition is foundational in pattern recog-nition and machine learning, with applications in document processing and optical character recognition. Current research often targets English digits, overlooking language-independent systems. This paper bridges this gap by analyzing deep-learning architectures and activation functions for Hindi, English, and Kannada digits. We evaluate CNN, LeNet, ResNet, MobileNet, and Inception (GoogleNet) models, and activation functions like sigmoid, tanh, linear, ELU, Leaky ReLU, and ReLU. The results indicate ResNet achieves 99.498% accuracy in English, 99.43% in Hindi, and 97.67% in Kannada datasets. This study enhances the design and optimization of robust digit recognition systems across diverse languages and scripts, promoting efficient models for practical applications. Despite advancements with CNNs, research predominantly focuses on English digits, highlighting the need for lanauage-independent recognition systems. © 2024 ieee.

关键词： Convolutional neural networks

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Semi-Supervised Depth Estimation by Multi-Task Learning 26

Semi-Supervised Depth Estimation by Multi-Task Learning

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Fu, Qingshun Dong, Xuan Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Depth estimation is a historical problem in computer vision. It is essential to get accurate depth information from cameras to implement autopilot. Most of the early studies were based on multiple observation points. Therefore, monocular depth estimation was proposed to overcome this restriction, trying to estimate depth information of every pixel from a single image. In this paper, we propose a semi-supervised multi-task model to predict depth map and semantic segmentation by making use of the implicit relation between the two tasks by sharing parameters. Moreover, our presented model is able to predict favorable depth map against the state-of-the-art on the KITTI benchmark. The code we used to train and evaluate our models is available at: https://***/fyhfly/Multi-task-Depth-Estimation.

关键词： computer vision Semantic segmentation Estimation Predictive models Multitasking Natural language processing Decoding

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One-Shot GAN: Learning to Generate Samples from Single Images and Videos

One-Shot GAN: Learning to Generate Samples from Single Image...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sushko, Vadim Gall, Juergen Khoreva, Anna Bosch Ctr Artificial Intelligence Stuttgart Germany Univ Bonn Bonn Germany

ISBN: (纸本)9781665448994

Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence. In this work, we introduce One-Shot GAN that can learn to generate samples from a training set as little as one image or one video. We propose a two-branch discriminator, with content and layout branches designed to judge the internal content separately from the scene layout realism. This allows synthesis of visually plausible, novel compositions of a scene, with varying content and layout, while preserving the context of the original sample. Compared to previous single-image GAN models, One-Shot GAN achieves higher diversity and quality of synthesis. It is also not restricted to the single image setting, successfully learning in the introduced setting of a single video.

关键词： Training Visualization computer vision conferences Computational modeling Layout Tools

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Surgical Suturing Skill Assessment Using Estimated Hand Roll Angle from a Deep-learning computer vision Algorithm 46

Surgical Suturing Skill Assessment Using Estimated Hand Roll...

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46th Annual International conference of the ieee Engineering in Medicine and Biology Society, EMBC 2024

作者： Gao, Jianxin Shayan, Amir Mehdi Singh, Simar P. Bible, Joe Singapogu, Ravikiran Groff, Richard E. Clemson University Department of Electrical and Computer Engineering ClemsonSC United States Clemson University Department of Bioengineering ClemsonSC United States Clemson University School of Mathematical and Statistical Sciences ClemsonSC United States

ISBN: (纸本)9798350371499

This paper proposes a deep-learning computer vision algorithm to estimate hand roll angles for metric-based assessment of surgical suturing skills. The number of rolls metric, previously calculated directly from IMU data, counts the number of hand roll reversals during a single suture. To calculate this metric using computer vision, we apply a deep-learning algorithm that can reliably estimate hand roll angles after training on suturing videos collected on the SutureCoach simulator. Results show that the estimation accuracy of the deep-learning algorithm is robust to different video backgrounds. The number of rolls metrics were used to analyze suturing performance in the SutureCoach dataset, which includes attending and resident surgeons and novices. The means of number of rolls based on computer vision differs between most skill levels at both surface and depth conditions, a pattern which holds for number of rolls based on the IMU as well. The proposed algorithm provides a solution for non-contact hand roll angle estimation, which opens up the possibility of inter-operative surgical skill assessment. The code is available at https://***/axin233/hand-roll-estimation © 2024 ieee.

关键词： Deep learning

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Adversarial Attack on Semantic Segmentation Preprocessed with Super Resolution 26

Adversarial Attack on Semantic Segmentation Preprocessed wit...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Lim, Gyeongsup Kim, Minjae Hur, Junbeom Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

computer vision tasks, such as image classification, semantic segmentation, and super resolution, are broadly utilized in many applications. Recent studies revealed that machine learning-based models for the computer vision tasks are vulnerable to adversarial attacks. Since the adversarial attack can disturb the computer vision models in real-world systems, many countermeasures have been proposed against the adversarial attacks, such as denoising, resizing, and machine learning-based super resolution model as a preprocessing. Recently, a prior work demonstrated that the super resolution model as a preprocessing can be vulnerable to the adversarial attack targeted to the preprocessing itself, only when the perturbation is inactive before the preprocessing. However, we also found that the perturbation before the preprocessing can be another serious threat if the super resolution model is used for a mitigation of adversarial attacks. In this paper, we propose Layered Adversary Generation (LAG) that generates the adversarial example by recursively injecting noises to clean image in white-box environment. We then show that LAG is effective to attack a semantic segmentation model even if the super resolution models with/without two countermeasures as auxiliary methods such as resizing and denoising are adopted to mitigate the adversarial attacks. Furthermore, we demonstrate that LAG is transferable across other super resolution models. Lastly, we discuss our attack method in gray-box and black-box environments, and suggests a mitigation for robust preprocessing.

关键词： computer vision Computational modeling Semantic segmentation Perturbation methods Superresolution Noise reduction pattern recognition

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Image Generators with Conditionally-Independent Pixel Synthesis

Image Generators with Conditionally-Independent Pixel Synthe...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Anokhin, I Demochkin, K. Khakhulin, T. Sterkin, G. Lempitsky, V Korzhenkov, D. Samsung AI Ctr Moscow Russia Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (纸本)9781665445092

Existing image generator networks rely heavily on spatial convolutions and, optionally, self-attention blocks in order to gradually synthesize images in a coarse-to-fine manner. Here, we present a new architecture for image generators, where the color value at each pixel is computed independently given the value of a random latent vector and the coordinate of that pixel. No spatial convolutions or similar operations that propagate information across pixels are involved during the synthesis. We analyze the modeling capabilities of such generators when trained in an adversarial fashion, and observe the new generators to achieve similar generation quality to state-of-the-art convolutional generators. We also investigate several interesting properties unique to the new architecture.

关键词： computer vision Image color analysis Computational modeling Impedance matching Superresolution computer architecture Rendering (computer graphics)

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AidUI: Toward Automated recognition of Dark patterns in User Interfaces 23

AidUI: Toward Automated Recognition of Dark Patterns in User...

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45th ieee/ACM International conference on Software Engineering (ICSE)

作者： Mansur, S. M. Hasan Salma, Sabiha Awofisayo, Damilola Moran, Kevin George Mason Univ Dept Comp Sci Fairfax VA 22030 USA Duke Univ Dept Comp Sci Durham NC 27706 USA George Mason Univ Aspiring Scientist Summer Internship Program ASSI Fairfax VA 22030 USA

ISBN: (纸本)9781665457019

Past studies have illustrated the prevalence of UI dark patterns, or user interfaces that can lead end-users toward (unknowingly) taking actions that they may not have intended. Such deceptive UI designs can be either intentional (to benefit an online service) or unintentional (through complicit design practices) and can result in adverse effects on end users, such as oversharing personal information or financial loss. While significant research progress has been made toward the development of dark pattern taxonomies across different software domains, developers and users currently lack guidance to help recognize, avoid, and navigate these often subtle design motifs. However, automated recognition of dark patterns is a challenging task, as the instantiation of a single type of pattern can take many forms, leading to significant variability. In this paper, we take the first step toward understanding the extent to which common UI dark patterns can be automatically recognized in modern software applications. To do this, we introduce AIDUI, a novel automated approach that uses computer vision and natural language processing techniques to recognize a set of visual and textual cues in application screenshots that signify the presence of ten unique UI dark patterns, allowing for their detection, classification, and localization. To evaluate our approach, we have constructed CONTEXTDP, the current largest dataset of fully-localized UI dark patterns that spans 175 mobile and 83 web UI screenshots containing 301 dark pattern instances. The results of our evaluation illustrate that AIDUI achieves an overall precision of 0.66, recall of 0.67, F1-score of 0.65 in detecting dark pattern instances, reports few false positives, and is able to localize detected patterns with an IoU score of 0.84. Furthermore, a significant subset of our studied dark patterns can be detected quite reliably (F1 score of over 0.82), and future research directions may allow for improved detection of add

关键词： Dark pattern UI Analysis UI Design

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Crossing cuts polygonal puzzles: Models and Solvers

Crossing cuts polygonal puzzles: Models and Solvers

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Harel, Peleg Ben-Shahar, Ohad

ISBN: (纸本)9781665445092

Jigsaw puzzle solving, the problem of constructing a coherent whole from a set of non-overlapping unordered fragments, is fundamental to numerous applications, and yet most of the literature has focused thus far on less realistic puzzles whose pieces are identical squares. Here we formalize a new type of jigsaw puzzle where the pieces are general convex polygons generated by cutting through a global polygonal shape with an arbitrary number of straight cuts. We analyze the theoretical properties of such puzzles, including the inherent challenges in solving them once pieces are contaminated with geometrical noise. To cope with such difficulties and obtain tractable solutions, we abstract the problem as a multi-body spring-mass dynamical system endowed with hierarchical loop constraints and a layered reconstruction process that is guided by the pictorial content of the pieces. We define evaluation metrics and present experimental results on both apictorial and pictorial puzzles to indicate that they are solvable completely automatically.

关键词： Measurement computer vision Shape Computational modeling pattern recognition

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