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GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review

GAN-Based Data Augmentation and Anonymization for Skin-Lesio...

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

作者： Bissoto, Alceu Valle, Eduardo Avila, Sandra Univ Campinas UNICAMP Inst Comp IC Campinas Brazil Univ Campinas UNICAMP RECOD Lab Sch Elect & Comp Engn FEEC Campinas Brazil

ISBN: (纸本)9781665448994

Despite the growing availability of high-quality public datasets, the lack of training samples is still one of the main challenges of deep-learning for skin lesion analysis. Generative Adversarial Networks (GANs) appear as an enticing alternative to alleviate the issue, by synthesizing samples indistinguishable from real images, with a plethora of works employing them for medical applications. Nevertheless, carefully designed experiments for skin-lesion diagnosis with GAN-based data augmentation show favorable results only on out-of-distribution test sets. For GAN-based data anonymization - where the synthetic images replace the real ones - favorable results also only appear for out-of-distribution test sets. Because of the costs and risks associated with GAN usage, those results suggest caution in their adoption for medical applications.

关键词： Training Biomedical equipment computer vision conferences Medical services Generative adversarial networks Skin

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PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet Laws

PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet La...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Bor-Shiun Chen, Ping-Yang Hsieh, Yi-Kuan Hsieh, Jun-Wei Chang, Ming-Ching He, JiaXin Teng, Shin-You Yue, HaoYuan Tseng, Yu-Chee National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Yang Ming Chiao Tung University College of Ai and Green Energy Tainan71150 Taiwan University at Albany State University of New York Department of Computer Science NY12222 United States Vanderbilt University Department of Computer Science United States

ISBN: (纸本)9798350302493

We present a video analytic system for enforcing motorcycle helmet regulation as a participation to the AI City Challenge 2023 [18] Track 5 contest. The advert of powerful object detectors enables real-time localization of the road users and even the ability to determine if a motorcyclist or a rider is wearing a helmet. Ensuring road safety is important, as the helmets can effectively provide protection against severe injuries and fatalities. However, monitoring and enforcing helmet compliance is challenging, given the large number of motorcyclists and limited visual input such as occlusions. To address these challenges, we propose a novel two-step approach. First, we introduce the PRB-FPN+, a state-of-the-art detector that excels in object localization. We also explore the benefits of deep supervision by incorporating auxiliary heads within the network, leading to enhanced performance of our deep learning architectures. Second, we utilize an advanced tracker named SMILEtrack to associate and refine the target track-lets. Comprehensive experimental results demonstrate that the PRB-FPN+ outperforms the state-of-the-art detectors on MS-COCO. Our system achieved a remarkable rank of 8 on the AI City Challenge 2023 [18] Track 5 Public Leader-board. Code implementation is available at: https://***/NYCU-AICVLab/AICITY-2023-Track5. © 2023 ieee.

关键词： Object detection

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Rethinking the Self-Attention in vision Transformers

Rethinking the Self-Attention in Vision Transformers

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

作者： Kim, Kyungmin Wu, Bichen Dai, Xiaoliang Zhang, Peizhao Yan, Zhicheng Vajda, Peter Kim, Seon Yonsei Univ Seoul South Korea Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665448994

Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on image (ImageNet-1K) and video (Kinetics-400) understanding show we can achieve 95% sparsity on the self-attention maps while maintaining the performance drop to be less than 2 points. This motivates us to rethink the role of self-attention in vision transformer models.

关键词： computer vision conferences Computational modeling pattern recognition

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Gaussian Label Distribution Learning for Spherical Image Object Detection

Gaussian Label Distribution Learning for Spherical Image Obj...

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

作者： Xu, Hang Liu, Xinyuan Zhao, Qiang Ma, Yike Yan, Chenggang Dai, Feng Hangzhou Dianzi Univ Hangzhou Peoples R China Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Spherical image object detection emerges in many applications from virtual reality to robotics and automatic driving, while many existing detectors use ln-norms loss for regression of spherical bounding boxes. There are two intrinsic flaws for ln-norms loss, i.e., independent optimization of parameters and inconsistency between metric (dominated by IoU) and loss. These problems are common in planar image detection but more significant in spherical image detection. Solution for these problems has been extensively discussed in planar image detection by using IoU loss and related variants. However, these solutions cannot be migrated to spherical image object detection due to the undifferentiable of the Spherical IoU (SphIoU). In this paper, we design a simple but effective regression loss based on Gaussian Label Distribution Learning (GLDL) for spherical image object detection. Besides, we observe that the scale of the object in a spherical image varies greatly. The huge differences among objects from different categories make the sample selection strategy based on SphIoU challenging. Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance. Extensive results on various two datasets with different baseline detectors show the effectiveness of our approach.

关键词： computer vision theory

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Video Analytics for Detecting Motorcyclist Helmet Rule Violations

Video Analytics for Detecting Motorcyclist Helmet Rule Viola...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Tsai, Chun-Ming Hsieh, Jun-Wei Chang, Ming-Ching He, Guan-Lin Chen, Ping-Yang Chang, Wei-Tsung Hsieh, Yi-Kuan University of Taipei Department of Computer Science Taipei10048 Taiwan National Yang Ming Chiao Tung University College of Ai and Green Energy Tainan71150 Taiwan University at Albany State University of New York Department of Computer Science NY12222 United States

ISBN: (纸本)9798350302493

The use of helmets is essential for motorcyclists' safety, but non-compliance with helmet rules remains a common issue. In this study, we extend the frontier of AI video analytic technologies for detecting violations of helmet rules among motorcyclists. Our method can handle highly challenging conditions for traditional methods, including occlusions, fast vehicle movement, shadows, large viewing angles, poor illumination and weather conditions. We adopt the widely used YOLOv7 object detector and develop a first baseline using YOLOv7-E6E. We further develop two improved versions, namely YOLOv7-CBAM and YOLOv7-SimAM that better address the challenges. Experiments are performed on the 2023 AI City Challenge Track 5 contest benchmark. Evaluation on the 100 test videos of the contest demonstrates the effectiveness of our approach. The baseline YOLOv7-E6E model trained with image size 1920 achieves 0.6112 mAP. The YOLOv7-CBAM achieves 0.6389 mAP, and YOLOv7-SimAM achieves 0.6422 mAP, where both are trained with image size 1280. These models rank sixth, fifth, and fourth on the public leaderboard, respectively, which outperforms over 36 global participating teams. The code for our models is available at: https://***/cmtsai2023/AICITY2023-Track5-DVHRM. © 2023 ieee.

关键词： Object detection

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Surveillance Face Presentation Attack Detection Challenge

Surveillance Face Presentation Attack Detection Challenge

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Fang, Hao Liu, Ajian Wan, Jun Escalera, Sergio Escalante, Hugo Jair Lei, Zhen University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Chinese Academy of Sciences Mais Institute of Automation Beijing China Universitat de Barcelona Computer Vision Center Catalonia Barcelona Spain Óptica y Electrónica JInstituto Nacional de Astrofísica Puebla Mexico Cair Hkisi Cas

ISBN: (纸本)9798350302493

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains 10,195 videos from 101 subjects of different age groups, which are collected by 7 mainstream surveillance cameras. Based on this dataset and protocol-3 for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios. © 2023 ieee.

关键词： Face recognition

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NeighborTrack: Single Object Tracking by Bipartite Matching with Neighbor Tracklets and Its Applications to Sports

NeighborTrack: Single Object Tracking by Bipartite Matching ...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Chen, Yu-Hsi Wang, Chien-Yao Yang, Cheng-Yun Chang, Hung-Shuo Lin, Youn-Long Chuang, Yung-Yu Liao, Hong-Yuan Mark Institute of Information Science Academia Sinica Taiwan Purdue University Department of Electrical Engineering United States National Tsing Hua University Department of Computer Science Taiwan National Taiwan University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350302493

We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requires no additional data or retraining. Instead, it uses the confidence score predicted by the backbone SOT network to automatically derive neighbor information and then uses this information to improve the tracking results. When tracking an occluded target, its appearance features are untrustworthy. However, a general siamese network often cannot tell whether the tracked object is occluded by reading the confidence score alone, because it could be misled by neighbors with high confidence scores. Our proposed NeighborTrack takes advantage of unoccluded neighbors' information to reconfirm the tracking target and reduces false tracking when the target is occluded. For the VOT challenge dataset commonly used in short-term object tracking, we improve three famous SOT networks, Ocean, TransT, and OSTrack, by an average of 1.92% EAO and 2.11% robustness. For the mid- and long-term tracking experiments based on OSTrack, we achieve state-of-the-art 72.25% AUC on LaSOT and 75.7% AO on GOT-10K. Most of the tracking examples we have used are related to sports. © 2023 ieee.

关键词： Target tracking

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Memory-friendly Scalable Super-resolution via Rewinding Lottery Ticket Hypothesis

Memory-friendly Scalable Super-resolution via Rewinding Lott...

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

作者： Lin, Jin Luo, Xiaotong Hong, Ming Qu, Yanyun Xie, Yuan Wu, Zongze Xiamen Univ Sch Informat Fujian Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Shenzhen Univ Sch Mechatron & Control Engn Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Scalable deep Super-Resolution (SR) models are increasingly in demand, whose memory can be customized and tuned to the computational recourse of the platform. The existing dynamic scalable SR methods are not memory-friendly enough because multi-scale models have to be saved with a fixed size for each model. Inspired by the success of Lottery Tickets Hypothesis (LTH) on image classification, we explore the existence of unstructured scalable SR deep models, that is, we find gradual shrinkage sub-networks of extreme sparsity named winning tickets. In this paper, we propose a Memory-friendly Scalable SR framework (MSSR). The advantage is that only a single scalable model covers multiple SR models with different sizes, instead of reloading SR models of different sizes. Concretely, MSSR consists of the forward and backward stages, the former for model compression and the latter for model expansion. In the forward stage, we take advantage of LTH with rewinding weights to progressively shrink the SR model and the pruning-out masks that form nested sets. Moreover, stochastic self-distillation (SSD) is conducted to boost the performance of sub-networks. By stochastically selecting multiple depths, the current model inputs the selected features into the corresponding parts in the larger model and improves the performance of the current model based on the feedback results of the larger model. In the backward stage, the smaller SR model could be expanded by recovering and fine-tuning the pruned parameters according to the pruning-out masks obtained in the forward. Extensive experiments show the effectiveness of MMSR. The smallest-scale sub-network could achieve the sparsity of 94% and outperforms the compared lightweight SR methods. [GRAPHICS] .

关键词： Efficient and scalable vision

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Contactless Respiratory Rate Monitoring For ICU Patients Based On Unsupervised Learning

Contactless Respiratory Rate Monitoring For ICU Patients Bas...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Liu, Zimeng Huang, Bin Lin, Chun-Liang Wu, Chieh-Liang Zhao, Changchen Chao, Wen-Cheng Wu, Yu-Cheng Zheng, Yadan Wang, Zhiru Beihang University Hangzhou Innovataion Institute China Beihang University School of Automation Science and Electrical Engineering China National Chung Hsing University College of Electrical Engineering and Computer Science Taiwan Taichung Veterans General Hospital Department of Critical Care Medicine Taiwan National Chung Hsing University College of Medicine Department of Post-Baccalaureate Medicine Taiwan National Chung Hsing University Big Data Center Taiwan Beijing University of Posts and Telecommunications Computer Science School China Beijing Foreign Studies University Graduate School of Translation and Interpretation China

ISBN: (纸本)9798350302493

Recently, the task of contactless physiological signal monitoring based on deep learning technologies has attracted a large number of scholars. However, few studies focus on the application of real-world scenarios, especially in clinical medicine scenes. In this paper, a novel video-based contactless respiratory rate measurement algorithm is developed for the Intensive Care Unit (ICU) patients. Firstly, a large-scale clinical real-world database towards ICU patient is collected in this study. Then, based on the dataset, the unsupervised learning is first introduced to extract the respiration waveform from the chest area of patients. Lastly, a respiratory rate estimator based on neural networks is proposed and trained on a periodical physiological signal simulation dataset, and utilizes the transfer learning technique to extract the respiratory rate from only 10-second respiration waveform. We obtained estimated respiratory rate with an MAE of 2.8 breaths/min and an STD of 3.0 breaths/min against the reference value computed from the specialized medical device. Extensive experiments demonstrate that our proposed methods achieve competitive results over state-of-the-art (SOTA) method in the real-world scenario. © 2023 ieee.

关键词： Intensive care units

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Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN

Focus on Content not Noise: Improving Image Generation for N...

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ieee/cvf International conference on computer vision (ICCV)

作者： Utz, Jonas Weise, Tobias Schlereth, Maja Wagner, Fabian Thies, Mareike Gu, Mingxuan Uderhardt, Stefan Breininger, Katharina Friedrich Alexander Univ Erlangen Nuremberg Dept Artificial Intelligence Biomed Engn Erlangen Germany FAU Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany FAU Erlangen Nurnberg Dept Med Rheumatol & Immunol 3 Erlangen Germany FAU Erlangen Nurnberg Deutsch Zentrum Immuntherapie Erlangen Germany FAU Erlangen Nurnberg Exploratory Res Unit Opt Imaging Ctr Erlangen Erlangen Germany

ISBN: (纸本)9798350307443

Annotating nuclei in microscopy images for the training of neural networks is a laborious task that requires expert knowledge and suffers from inter- and intra-rater variability, especially in fluorescence microscopy. Generative networks such as CycleGAN can inverse the process and generate synthetic microscopy images for a given mask, thereby building a synthetic dataset. However, past works report content inconsistencies between the mask and generated image, partially due to CycleGAN minimizing its loss by hiding shortcut information for the image reconstruction in high frequencies rather than encoding the desired image content and learning the target task. In this work, we propose to remove the hidden shortcut information, called steganography, from generated images by employing a low pass filtering based on the discrete cosine transform (DCT). We show that this increases coherence between generated images and cycled masks and evaluate synthetic datasets on a downstream nuclei segmentation task. Here we achieve an improvement of 5.4 percentage points in the F1-score compared to a vanilla CycleGAN. Integrating advanced regularization techniques into the CycleGAN architecture may help mitigate steganography-related issues and produce more accurate synthetic datasets for nuclei segmentation.

关键词： cycle gan data generation dct fluorescence microscopy nuclei segmentation steganography synthetic data

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