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共 327 条记录，以下是151-160 订阅

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A Multi-stage Feature Enhancement Network with Second-Attention for image Captioning 6

A Multi-stage Feature Enhancement Network with Second-Attent...

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6th International conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Yang, Xiaobao Yang, Yang Wu, Junsheng Ma, Sugang Hou, Zhiqiang Song, Bohui Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Computer Science Northwestern Polytechnical University Xi'an710071 China

ISBN: (纸本)9798400707674

In the past few years, self-attention in Transformer has been widely used in natural language processing (NLP) and computer vision (CV) due to its excellent ability to capture global information, especially in image captioning, where the use of self-attention can significantly improve the representation of visual information. However, the obtained image feature from Transformer-based models suffers from two main problems, one is information redundancy due to the global aggregation of self-attention, and the other is the lack of semantic information caused by single-scale feature extraction. Therefore, we first propose second-attention in this paper, by redistributing image attention weights, second-attention can effectively remove irrelevant information and enhance the attention to essential objects and relations. Meanwhile, to enrich semantic information and further enhance the role of second-attention, we design a Multi-stage Feature Enhancement (MFE) network to improve the ability to represent visual information. After conducting extensive experiments on the MS COCO dataset, we achieve significant improvements in all popular benchmarks, particularly BLEU4 increased by 1.6%, CIDEr increased by 3.4%. © 2023 ACM.

关键词： Semantics

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Recombining vision Transformer Architecture for Fine-Grained Visual Categorization 29th

Recombining Vision Transformer Architecture for Fine-Grained...

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29th International conference on MultiMedia Modeling (MMM)

作者： Deng, Xuran Liu, Chuanbin Lu, Zhiying Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Peoples R China

ISBN: (纸本)9783031278174;9783031278181

Fine-grained visual categorization (FGVC) is a challenging task in the image analysis field which requires comprehensive discriminative feature extraction and representation. To get around this problem, previous works focus on designing complex modules, the so-called necks and heads, over simple backbones, while bringing a huge computational burden. In this paper, we bring a new insight: vision Transformer itself is an all-in-one FGVC framework that consists of basic Backbone for feature extraction, Neck for further feature enhancement and Head for selecting discriminative feature. We delve into the feature extraction and representation pattern of ViT for FGVC and empirically show that simply recombining the original ViT structure to leverage multi-level semantic representation without introducing any other parameters is able to achieve higher performance. Under such insight, we proposed RecViT, a simple recombination and modification of original ViT, which can capture multi-level semantic features and facilitate fine-grained recognition. In RecViT, the deep layers of the original ViT are served as Head, a few middle layers as Neck and shallow layers as Backbone. In addition, we adopt an optional Feature processing Module to enhance discriminative feature representation at each semantic level and align them for final recognition. With the above simple modifications, RecViT obtains significant improvement in accuracy in FGVC benchmarks: CUB-200-2011, Stanford Cars and Stanford Dogs.

关键词： Fine-Grained Visual Categorization vision Transformer

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ArtDiff: Artwork Generation via Conditional Diffusion Models 23

ArtDiff: Artwork Generation via Conditional Diffusion Models

引用

15th International conference on Digital image processing, ICDIP 2023

作者： Liu, Wenhui Liu, Bin Lin, Xiao Li, Bo He, Zhifen Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Nanchang China Department of Supercomputing Management Jiangxi Science and Technology Infrastructure Center Jiangxi Nanchang China

ISBN: (纸本)9798400708237

Artwork Generation is an important research area of computer vision. Recently, kinds of generative models have achieved great success in natural image generation. However, artwork generation has rarely been studied due to the unfixed structure of artworks. Combined with prevailing diffusion models, we propose a simple yet effective framework, named as ArtDiff, for artwork generation. Given a name of an artist, we can generate diverse and novel artworks which reflecting the style of the artist. To the best of our knowledge, ArtDiff is the first diffusion model that generate artworks with the guidance of artist's name. Experimental results demonstrate that ArtDiff is able to recognize the artist's preference and generate artworks with reasonable structures and fine-grained details. © 2023 ACM.

关键词： Diffusion

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Edge Map Extraction of High-Resolution Facial images

Edge Map Extraction of High-Resolution Facial Images

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World conference on Information Systems for Business Management, ISBM 2023

作者： Timmanagoudar, Harshit Krishna, Gandra Sai Koti, Anirudh Jolad, Harsh Preethi, P. Department of Computer Science PES University Karnataka Bangalore560085 India

ISBN: (纸本)9789819986118

This work focuses on edge detection, a vital task in several fields, such as computer vision, image processing, and pattern recognition. Since exact facial analysis and recognition have numerous applications in areas like biometrics, surveillance, and human-computer interaction, this study’s focus is specifically on obtaining precise edge maps of facial pictures with exceptional resolution. Several essential processes are carried out in order to produce accurate edge maps. In order to lessen noise and attenuate the very low-level features in high-resolution photographs, a smoothing operation is first conducted. Utilizing a variety of edge detection methods, such as directional gradients and cumulative gradient magnitude, variations in pixel intensities along various directions are examined. The Zhang-Suen technique, renowned for its efficiency in thinning binary pictures, is used to remove extraneous pixels from the discovered edges to further refine them. By maintaining only the necessary boundary information, this phase ensures that the edge maps produced are more accurate. According to subjective and comparative evaluations and the outcomes of applying the algorithm to face photos from various datasets, the suggested approach is suitable for building edge maps of facial photographs with high resolution as well as low resolution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Edge detection

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Investigation of Machine Learning Algorithms for pattern recognition in image processing 5

Investigation of Machine Learning Algorithms for Pattern Rec...

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5th International conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Kate, Chennaiah Kalpana, C. Sharma, Arvind Yadav, Ajay Singh Kumar, Ashok Kumar, S. Sandeep St. Peter's Engineering College Department of Information Technology Telangana Hyderabad500100 India Npr College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Dindigul624401 India Government Women Engineering College Department of Electronics and Communication Engineering Rajasthan Ajmer305002 India Srm Institute of Science and Technology Department of Mathematics Uttar Pradesh Ghaziabad201204 India BanasthaliVidyapith Department of Computer Science Rajasthan 304022 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh 522502 India

ISBN: (纸本)9798350321425

In order to recognize patterns in images, this study tests the performance of many 'machine learning algorithms' and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ('deep learning, support vector machines, decision trees, and random forests') and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its 'accuracy, precision, recall, and F1 score', among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms. © 2023 IEEE.

关键词： Feature extraction

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Optimal KAZE and AKAZE Features for Facial Similarity Matching 1

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Proceedings of the 7th International conference on Advances in Computing and Data Sciences, ICACDS 2023

作者： Vinay, A. Vasu, Kishan Athirala Lodha, Pranav Yogi Natarajan, S. Sudarshan, T.S.B. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru560085 India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

Face recognition is one of the premier disciplines in the vast field of computer vision and image analysis. A popular method is the Gaussian scale space analysis which limits the performance by smoothing both the noise and natural boundaries in the same proportion. In order to prevent the loss of natural boundaries to smoothing we tap into nonlinear scale space techniques such as KAZE and Accelerated KAZE. KAZE is a multistage 2-D feature detection and description algorithm. It makes use of AOS schemes to develop the nonlinear scale space for analysis. Though the results are satisfactory, it is computationally intense as they solve a humongous module of linear equations. To ascertain the mentioned limitation of KAZE we make use of Accelerated-KAZE, which uses pyramidal structure with Fast Explicit Diffusion incorporated in it, thus minimizing the computation in the step of feature detection of nonlinear scale space. Also, with the use of M-LDB (Modified-Local Difference Binary) descriptor the problem of rotation is solved. Usage of RANSAC after processing in two methods had some disadvantages. It gave bad results when the inliers ratio in the dataset is low. Thus, Optimal RANSAC is employed which works well even when the inliers ratio is as low as 5%. The proposed methods are tested on many standard datasets and various performance parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF conference on computer vision and pattern recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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Road Condition Detection and Crowdsourced Data Collection for Accident Prevention: A Deep Learning Approach 12

Road Condition Detection and Crowdsourced Data Collection fo...

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12th International conference on image processing Theory, Tools and Applications, IPTA 2023

作者： Jahan, Md Saroar Islam, Mominul Hossain, Md Sanjid Kabir Mim, Jhuma Oussalah, Mourad Akter, Nasrin Oulu Finland Daffodil International University Dept. of Cse Dhaka Bangladesh Lut Univerity Computer Vision and Pattern Recognition Lappeenranta Finland University of Oulu Cmvs Faculty of Itee Oulu Finland

ISBN: (纸本)9798350325416

Bangladesh is one of the countries struggling to prevent road accidents, which is a global cause for concern. An early warning system that indicates road conditions can contribute to the prevention task. For this purpose, a deep-learning based approach using a Convolutional Neural Network (CNN) to learn from random road images the safety factor is developed. This results in a three-class categorization: (i) Severely risky roads, (ii) Mildly risky roads, and (iii) Normal roads. The application of deep learning techniques in this study yields an accuracy of 95.5% in detecting problematic road conditions. Furthermore, based on the study's findings, a mobile application has been developed. The app enables real-time crowdsourced data collection of road conditions and provides a platform for users to share this information in real-time with other drivers, thereby, contributing to prevent accidents and raise awareness among drivers and users by pinpointing the location of the risky road. Finally, crowdsourced data has been reused to update the trained model, which further improves the classifier accuracy. © 2023 IEEE.

关键词： Safety factor

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GLSI Texture Descriptor Based on Complex Networks for Music Genre Classification

GLSI Texture Descriptor Based on Complex Networks for Music ...

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International Joint conference on Neural Networks (IJCNN)

作者： Coca Salazar, Andres Eduardo Fed Univ Technol Parana UTFPR TD Comp Engn Coordinat COENC Toledo PR Brazil

ISBN: (纸本)9781665488679

The texture classification of an image is related to an important musical attribute, the music genre. This relationship is depicted in the visual representation of the audio signal, called as spectrogram. In this paper, we propose a new Music Genre Classification (MGC) system that processes the spectrogram texture using the Gray Level and Structural Information (GLSI) descriptor, and represents the interconnection between the descriptor codes through complex networks. The GLSI descriptor is an improvement of the CLBP (Completed Local Binary pattern) descriptor, which quantifies the texture of an image with three codes: signal (CLBP-S), magnitude (CLBP-M), and central (CLBP-C). By transforming the CLBP-C code, GLSI adds macro-structural information. The network nodes represent the descriptor codes, and the respective edges, the relationship according to the horizontal and vertical consecutive condition. We defined two representations for the nodes: 1) individual code node, obtaining the G(s), G(m) and G(g) networks, and 2) triple code node, obtaining the G(smg) network. For the experimental stage, we used the GTZAN dataset, three types of spectrograms: conventional, mel-spectrogram and gammatonegram;and mining with network topological measures. For each type of spectrogram, we performed three experiments according to feature vector combinations, such as measures of: 1) G(s), G(m) and G(g), 2) G(smg), and 3) all networks. In the machine learning stage, we used the ensemble classifier Bagging with Random Forest, and 10fold cross-validation repeated 100 times. The experiment using all measures and all spectrograms revealed a satisfactory result, indicating that the MGC proposed is promising. We also propose a new equation to calculate the GLSI code, which proved to be much faster and with more intuitive encoding.

关键词： Digital image processing computer vision applications pattern recognition performance evaluation

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Portrait Matting via Semantic and Detail Guidance 1

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7th asian conference on pattern recognition, ACPR 2023

作者： Wang, Deming Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

Portrait matting is a challenging computer vision task that aims to estimate the per-pixel opacity of the foreground human regions. To produce high-quality alpha mattes, the majority of available methods employ a user-supplied trimap as an auxiliary input. However, obtaining trimap is difficult, which restricts the application of these methods. Recently, trimap-free approaches have been developed, but the alpha quality is significantly lower than that of trimap-based methods due to the lack of guidance. In this paper, we propose a portrait matting framework via semantic and detail guidance. We use the mask obtained from portrait segmentation as semantic guidance to improve semantic understanding of portrait matting, and we present a detail encoder block to capture the low-level feature around the subject as detail guidance, which helps to refine alpha details, such as hair. The proposed approach achieves remarkable results on both composition and real-world benchmarks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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