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A Stable vision Transformer for Out-of-Distribution Generalization 6th

A Stable Vision Transformer for Out-of-Distribution Generali...

引用

6th chinese conference on pattern recognition and computer vision (PRCV)

作者： Yu, Haoran Liu, Baodi Wang, Yingjie Zhang, Kai Tao, Dapeng Liu, Weifeng China Univ Petr East China Qingdao Peoples R China Yunnan Univ Kunming Yunnan Peoples R China

ISBN: (纸本)9789819985425;9789819985432

vision Transformer (ViT) has achieved amazing results in many visual applications where training and testing instances are drawn from the independent and identical distribution (I.I.D.). the performance will drop drastically when the distribution of testing instances is different from that of training ones in real open environments. To tackle this challenge, we propose a Stable vision Transformer (SViT) for out-of-distribution (OOD) generalization. In particular, the SViT weights the samples to eliminate spurious correlations of token features in vision Transformer and finally boosts the performance for OOD generalization. According to the structure and feature extraction characteristics of the ViT models, we design two forms of learning sample weights: SViT(C) and SViT(T). To demonstrate the effectiveness of two forms of SViT for OOD generalization, we conduct extensive experiments on the popular PACS and OfficeHome datasets and compare them with SOTA methods. the experimental results demonstrate the effectiveness of SViT(C) and SViT(T) for various OOD generalization tasks.

关键词： Out-of-Distribution Generalization Independence Samples Weighting vision Transformer

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TransDDPM: Transformer-Based Denoising Diffusion Probabilistic Model for Image Restoration 6th

TransDDPM: Transformer-Based Denoising Diffusion Probabilist...

引用

6th chinese conference on pattern recognition and computer vision (PRCV)

作者： Wei, Pan Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9789819985517;9789819985524

Although diffusion models have achieved impressive success for image generation, its application for image restoration is still underexplored. Following tremendous success in natural language processing, transformers have also shown great success for computer vision. Although several researches indicate that increasing transformer depth/width improves the applicability of diffusion models, application of Transformers in diffusion models is still underexplored due to quadratic complexity with the spatial resolution. In this work, we proposed a Transformer-based Denoising Diffusion Probabilistic Model (TransDDPM) for image restoration. With multi-head cross-covariance attention (MXCA), TransDDPM can operates global self-attention with cross-covariance matrix in channel dimension rather than spatial dimension. Another gated feed-forward network (GFFN) is included to enhance the ability to exploit spatial local context. Powered by these designs, TransDDPM is capable for both long-range dependencies and short-range dependencies and flexible for images of various resolutions. Comprehensive experiments demonstrate our TransDDPM achieves state-of-the art performance on several restoration tasks, e.g., image deraining, image dehazing and motion deblurring.

关键词： DDPM Cross-Covariance Attention Image Restoration

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M2DAR: Multi-View Multi-Scale Driver Action recognition with vision Transformer

M2DAR: Multi-View Multi-Scale Driver Action Recognition with...

引用

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

作者： Ma, Yunsheng Yuan, Liangqi Abdelraouf, Amr Han, Kyungtae Gupta, Rohit Li, Zihao Wang, Ziran Purdue University College of Engineering United States Toyota Motor North America InfoTech Labs United States

ISBN: (纸本)9798350302493

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M2DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at https://***/PurdueDigitalTwin/M2DAR. © 2023 IEEE.

关键词： Statistical tests

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Detecting Omissions in Geographic Maps through computer vision 7

Detecting Omissions in Geographic Maps through Computer Visi...

引用

7th International conference on Multimedia Analysis and pattern recognition, MAPR 2024

作者： Nguyen, Phuc Do, Anh Hoai, Minh VinAI Research Viet Nam Ministry of Information and Communications Viet Nam

ISBN: (纸本)9798350368437

this paper explores the application of computer vision technologies to the analysis of maps, an area with substantial historical, cultural, and political significance. Our focus is on developing and evaluating a method for automatically identifying maps that depict specific regions and feature landmarks with designated names, a task that involves complex challenges due to the diverse styles and methods used in map creation. We address three main subtasks: differentiating maps from non-maps, verifying the accuracy of the region depicted, and confirming the presence or absence of particular landmark names through advanced text recognition techniques. Our approach utilizes a Convolutional Neural Network and transfer learning to differentiate maps from non-maps, verify the accuracy of depicted regions, and confirm landmark names through advanced text recognition. We also introduce the VinMap dataset, containing annotated map images of Vietnam, to train and test our method. Experiments on this dataset demonstrate that our technique achieves F1-score of 85.51% for identifying maps excluding specific territorial landmarks. this result suggests practical utility and indicates areas for future improvement. https://***/VinAIResearch/VinMap © 2024 IEEE.

关键词： Convolutional neural networks

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KDFAS: Multi-stage Knowledge Distillation vision Transformer for Face Anti-spoofing 6th

KDFAS: Multi-stage Knowledge Distillation Vision Transformer...

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6th chinese conference on pattern recognition and computer vision (PRCV)

作者： Zhang, Jun Zhang, Yunfei Shao, Feixue Ma, Xuetao Zhou, Daoxiang Taiyuan Univ Technol Coll Data Sci Taiyuan 030024 Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9789819984688;9789819984695

With the commercial application of face recognition systems, face anti-spoofing has been studied extensively to enhance security in recent years. In this work, a lightweight network via knowledge distillation for face anti-spoofing is proposed. the main innovations of our approach are threefold: (1) In convolutional neural network based knowledge distillation, the local receptive field of teacher network may be inconsistent with that of student network, which results in misguiding. In our method, vision transformer architecture is leveraged because of its global modeling capabilities. (2) Beyond conventional decision-level knowledge transfer in the classification step via kullback-leibler loss, we present multi-stage feature-level knowledge distillation strategy to guide the feature learning of student network which can transfer richer knowledge from teacher to student network. (3) In contrast to traditional projection head learning, we construct a covariance matrix to solve the embedding dimensionality mismatching problem between teacher and student network in middle layers. Compared to teacher model of 1.28 GB, the memory of student model is only 330.8MB, which effectively achieves a tradeoff between memory and accuracy. Extensive experiments on three standard benchmarks demonstrate the superiority of our proposed method, which evidently corroborates the significance of multi-stage knowledge distillation for face anti-spoofing.

关键词： Face Anti-Spoofing vision Transformer Knowledge Distillation

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A Pre-trained Model for chinese Medical Record Punctuation Restoration 6th

A Pre-trained Model for Chinese Medical Record Punctuation R...

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6th chinese conference on pattern recognition and computer vision (PRCV)

作者： Yu, Zhipeng Ling, Tongtao Gu, Fangqing Sheng, Huangxu Liu, Yi Guangdong Univ Technol Guangzhou Peoples R China IMSL Shenzhen Key Lab Shenzhen Peoples R China

ISBN: (纸本)9789819985395;9789819985401

In the medical field, text after automatic speech recognition (ASR) is poorly readable due to a lack of punctuation. Even worse, it can lead to the patient misunderstanding the doctor's orders. therefore, punctuation after ASR to enhance readability is an indispensable step. Most recent work has been fine-tuning downstream tasks on pre-trained models, but the models lack knowledge of relevant domains. Furthermore, most of the research is based on the English language, and there is less work in chinese and even less in the medical field. Based on this, we thought of adding chinese medical data to the model pre-training stage and adding the task of punctuation restoration in this work. From this, we proposed the Punctuation Restoration Pre-training Mask Language Model (PRMLM) task in the pre-training stage and used contrastive learning at this stage to enhance the model effect. then, we propose a Punctuation Prior Knowledge Fine-tuning (PKF) method to play the role of contrast learning better when fine-tuning the downstream punctuation restoration task. In our medical field dataset, we performed a series of comparisons with existing algorithms to verify the proposed algorithm's effectiveness.

关键词： Punctuation restoration Automatic speech recognition Pre-training mask language model Supervised contrast learning

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Progressive Frequency-Aware Network for Laparoscopic Image Desmoking 6th

Progressive Frequency-Aware Network for Laparoscopic Image D...

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6th chinese conference on pattern recognition and computer vision (PRCV)

作者： Zhang, Jiale Huang, Wenfeng Liao, Xiangyun Wang, Qiong Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Comp Vis & Virtual Real Te Beijing Peoples R China

ISBN: (纸本)9789819984312;9789819984329

Laparoscopic surgery offers minimally invasive procedures with better patient outcomes, but smoke presence challenges visibility and safety. Existing learning-based methods demand large datasets and high computational resources. We propose the Progressive Frequency-Aware Network (PFAN), a lightweight GAN framework for laparoscopic image desmoking, combining the strengths of CNN and Transformer for progressive information extraction in the frequency domain. PFAN features CNN-based Multi-scale Bottleneck-Inverting (MBI) Blocks for capturing local high-frequency information and Locally-Enhanced Axial Attention Transformers (LAT) for efficiently handling global low-frequency information. PFAN efficiently desmokes laparoscopic images even with limited training data. Our method outperforms state-of-the-art approaches in PSNR, SSIM, CIEDE2000, and visual quality on the Cholec80 dataset and retains only 629K parameters. Our code and models are made publicly available at: https://***/jlzcode/PFAN.

关键词： Medical Image Analysis vision Transformer CNN

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Stable Visual pattern Mining via pattern Probability Distribution 6th

Stable Visual Pattern Mining via Pattern Probability Distrib...

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6th chinese conference on pattern recognition and computer vision (PRCV)

作者： Ma, Yifan Liang, Xuefeng Lin, Xiaoyu Shi, Guanghui Xidian Univ Sch Artificial Intelligence Xian Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou Peoples R China

ISBN: (纸本)9789819985425;9789819985432

Visual patterns are the fundamental elements that compose an image and often convey higher-level semantics. Visual pattern mining can be widely applied to real-world applications and various downstream tasks, such as tourist destination recommendation, scenic area promotion, etc. therefore, it is a fundamental issue in computer vision. Visual pattern has two properties: frequency and discrimination. Most emerging studies first perform self-supervised representation learning and a supervised classification task to extract discriminative feature, and then mine the visual patterns through clustering algorithms to ensure the frequency. However, these methods require the manual setting of the hyperparameters (e.g. clustering radius eps in DBSCAN, etc.) to which the mining results are sensitive. Improper hyperparameters will result in obtaining redundant visual patterns or missing visual patterns, and the optimal hyperparameters differ among categories. To address these problems, we propose a new stable mining method which directly obtains visual patterns in each category without using clustering algorithms. First, we propose a pattern Distribution Extractor (PDE) to ensure the discrimination and extract the pattern probability distribution, which represents the probability of the image belonging to each candidate pattern cluster. Experiments demonstrate pattern probability distribution provides more stability in mining stage than the previous methods. Further, we propose a Semantic Density Mining (SDM) strategy which first adaptively removes the non-frequent images to ensure the frequency and then directly obtains the visual patterns in each category according to the pattern probability distribution. Extensive experiments demonstrate the effectiveness of our proposed method.

关键词： visual pattern mining self-supervised learning joint optimization

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A Hierarchical theme recognition Model for Sandplay therapy 6th

A Hierarchical Theme Recognition Model for Sandplay Therapy

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6th chinese conference on pattern recognition and computer vision (PRCV)

作者： Feng, Xiaokun Hu, Shiyu Chen, Xiaotang Huang, Kaiqi Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Sandplay therapy functions as a pivotal tool for psychological projection, where testers construct a scene to mirror their inner world while psychoanalysts scrutinize the testers' psychological state. In this process, recognizing the theme (i.e., identifying the content and emotional tone) of a sandplay image is a vital step in facilitating higher-level analysis. Unlike traditional visual recognition that focuses solely on the basic information (e.g., category, location, shape, etc.), sandplay theme recognition needs to consider the overall content of the image, then relies on a hierarchical knowledge structure to complete the reasoning process. Nevertheless, the research of sandplay theme recognition is hindered by following challenges: (1) Gathering high-quality and enough sandplay images paired with expert analyses to form a scientific dataset is challenging, due to this task relies on a specialized sandplay environment. (2) theme is a comprehensive and high-level information, making it difficult to adopt existing works directly in this task. In summary, we have tackled the above challenges from the following aspects: (1) Based on carefully analysis of the challenges (e.g., small-scale dataset and complex information), we present the HIST (HIerarchical Sandplay theme recognition) model that incorporates external knowledge to emulate the psychoanalysts' reasoning process. (2) Taking the split theme (a representative and evenly distributed theme) as an example, we proposed a high-quality dataset called SP2 (SandPlay SPlit) to evaluate our proposed method. Experimental results demonstrate the superior performance of our algorithm compared to other baselines, and ablation experiments confirm the importance of incorporating external knowledge. We anticipate this work will contribute to the research in sandplay theme recognition. the relevant datasets and codes will be released continuously.

关键词： Sandplay therapy Sandplay theme recognition Visual recognition

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Centerline Extraction of Line Structured Light in 3D Reconstruction 7

Centerline Extraction of Line Structured Light in 3D Reconst...

引用

7th International conference on Electrical, Mechanical and computer Engineering, ICEMCE 2023

作者： Zhang, Ji Wuhan Institute of Technology Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan China

ISBN: (纸本)9798350382877

At present, 3D reconstruction of binocular vision is a hot topic in the field of computer vision, and the extraction of laser fringe centers in line structured light images is a crucial step. In order to obtain the feature point set between the images, it is necessary to extract the centerline of the linear structured light bands. Firstly, through image preprocessing, perform image stereo correction and image Gaussian filtering, and adaptive threshold segmentation algorithm is used to segment the stripe area of the line structured light, and the image thinning algorithm is used to extract the initial centerline of the line structured light. Finally, the point set is constructed and the initial centerline is optimized twice by the gray-scale centroid method, and the centerline of the linear structured light stripe is accurately extracted. the results show that, compared with the Steger algorithm and the skeleton thinning method, the proposed method extracts the centerlines of the linear structured light stripes with higher accuracy. © 2023 IEEE.

关键词： Binocular vision

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