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LogicAL: Towards logical anomaly synthesis for unsupervised anomaly localization

LogicAL: Towards logical anomaly synthesis for unsupervised ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ying Zhao Ricoh Software Research Center (Beijing) Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly localization is a practical technology for improving industrial production line efficiency. Due to anomalies are manifold and hard to be collected, existing unsupervised researches are usually equipped with anomaly synthesis methods. However, most of them are biased towards structural defects synthesis while ignoring the underlying logical constraints. To fill the gap and boost anomaly localization performance, we propose an edge manipulation based anomaly synthesis framework, named LogicAL, that produces photo-realistic both logical and structural anomalies. We introduce a logical anomaly generation strategy that is adept at breaking logical constraints and a structural anomaly generation strategy that complements to the structural defects synthesis. We further improve the anomaly localization performance by introducing edge reconstruction into the network structure. Extensive experiments on the challenge MVTecLOCO, MVTecAD, VisA and MADsim datasets verify the advantage of proposed LogicAL on both logical and structural anomaly localization.

关键词： Location awareness Manifolds computer vision Image edge detection conferences Semantics Production

来源：评论

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Rethinking the Self-Attention in vision Transformers

Rethinking the Self-Attention in Vision Transformers

引用

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

来源：评论

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An analysis of best-practice strategies for replay and rehearsal in continual learning

An analysis of best-practice strategies for replay and rehea...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Alexander Krawczyk Alexander Gepperth Applied Computer Science Department University of Applied Sciences Fulda Fulda Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study is in the context of class-incremental continual learning using replay, which has seen notable progress in recent years, fueled by concepts like conditional, latent or maximally interfered replay. However, there are many design choices to take when it comes to implementing replay strategies, with potentially very different outcomes in the various class-incremental scenarios. Some of the obvious design choices in replay are the use of experience replay (ER), the use of different generators like GANs –vs– VAEs for generative replay, or whether to re-initialize generators after each task. For replay strategies in general, it is an open question how many samples to generate for each new task, and what weights to give generated and new samples in the loss. On top of this, there are many possible CL evaluation protocols differing in the amount of tasks, the balancing of tasks or fundamental complexity (e.g., MNIST -vs- latent CIFAR/SVHN), and thus few generic conclusions about best practices for replay/rehearsal have found consensus in the literature. This study aims at establishing such best-practices by conducting an extensive set of representative replay experiments.

关键词： Continuing education computer vision Protocols conferences Generators pattern recognition Complexity theory

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Quality-based Artifact Modeling for Facial Deepfake Detection in Videos

Quality-based Artifact Modeling for Facial Deepfake Detectio...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sara Concas Simone Maurizio La Cava Roberto Casula Giulia Orrù Giovanni Puglisi Gian Luca Marcialis University of Cagliari Cagliari Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Facial deepfakes are becoming more and more realistic, to the point that it is often difficult for humans to distinguish between a fake and a real video. However, it is acknowledged that deepfakes contain artifacts at different levels; we hypothesize a connection between manipulations and visible or non-visible artifacts, especially where the subject’s movements are difficult to reproduce in detail. Accordingly, our approach relies on different quality measures, No-Reference (NR) and Full-Reference (FR), over the detected faces in the video. The measurements allow us to adopt a frame-by-frame approach to build an effective matrix-based representation of a video sequence. We show that the results obtained by this basic feature set for a neural network architecture constitute the first step that encourages the empowerment of this representation, aimed to extend our investigation to further deepfake classes. The FaceForensics++ dataset is chosen for experiments, which allows the evaluation of the proposed approach over different deepfake generation algorithms.

关键词： Deepfakes computer vision conferences Video sequences Neural networks computer architecture Benchmark testing

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Tracked-Vehicle Retrieval by Natural Language Descriptions with Multi-Contextual Adaptive Knowledge

Tracked-Vehicle Retrieval by Natural Language Descriptions w...

引用

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

作者： Le, Huy Dinh-Anh Qui-Vinh Nguyen, Quang Luu, Duc Trung Thi-Thanh Chau, Truc Chung, Nhat Minh Viet-Uyen Ha, Synh International University School of Computer Science and Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350302493

This paper introduces our solution for Track 2 in AI City Challenge 2023. The task is tracked-vehicle retrieval by natural language descriptions with a real-world dataset of various scenarios and cameras. Our solution mainly focuses on four points: (1) To address the linguistic ambiguity in the language query, we leverage our proposed standardized version for text descriptions for the domain-adaptive training and post-processing stage. (2) Our baseline vehicle retrieval model utilizes CLIP to extract robust visual and textual feature representations to learn the unified cross-modal representations between textual and visual features. (3) Our proposed semi-supervised domain adaptive (SSDA) training method is leveraged to address the domain gap between the train and test set. (4) Finally, we propose a multi-contextual post-processing technique that prunes out the wrong results based on multi-contextual attributes information that effectively boosts the final retrieval results. Our proposed framework has yielded a competitive performance of 82.63% MRR accuracy on the test set, achieving 1st place in the competition. Codes will be available at https://***/zef1611/AIC23-NLRetrieval-HCMIU-CVIP © 2023 ieee.

关键词： Tracked vehicles

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Event-based Ball Spin Estimation in Sports

Event-based Ball Spin Estimation in Sports

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Takuya Nakabayashi Kyota Higa Masahiro Yamaguchi Ryo Fujiwara Hideo Saito Keio University Yokohama Japan NEC Corporation Kawasaki Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Ball spin estimation in sports is important for analyzing the game. Since spin is generally too fast to be captured by a conventional camera, a high-speed camera is often used to capture images of the ball and estimate its spin. However, since a high-speed camera is not robust to changes in the lighting conditions, it is difficult to estimate spin in some environments. To solve these problems, this paper proposes a new method for ball spin estimation using an event camera. An event camera is a sensor inspired by the visual system of animals, which outputs the brightness changes in a scene. Event cameras have advantages such as high temporal resolution and high dynamic range, and can accurately capture the motion of a fast-spinning ball in various lighting conditions. Experimental results in a synthesized dataset showed that the proposed method can stably estimate spin up to 500 rps. It is also confirmed that the proposed method can estimate spin in the data obtained from actual sports games.

关键词： Estimation Lighting Games Visual systems Cameras Vectors pattern recognition

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Multi-View Action recognition for Distracted Driver Behavior Localization

Multi-View Action Recognition for Distracted Driver Behavior...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yuehuan Xu Shuai Jiang Zhe Cui Fei Su Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System and Network Culture China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The detection and recognition of distracted driving behaviors has emerged as a new vision task with the rapid development of computer vision, which is considered as a challenging temporal action localization (TAL) problem in computer vision. The primary goal of temporal localization is to determine the start and end time of actions in untrimmed videos. Currently, most state-of-the-art temporal localization methods adopt complex architectures, which are cumbersome and time-consuming. In this paper, we propose a robust and efficient two-stage framework for distracted behavior classification-localization based on the sliding window approach, which is suitable for untrimmed naturalistic driving videos. To address the issues of high similarity among different behaviors and interference from background classes, we propose a multi-view fusion and adaptive thresholding algorithm, which effectively reduces missing detections. To address the problem of fuzzy behavior boundary localization, we design a post-processing procedure that achieves fine localization from coarse localization through post connection and candidate behavior merging criteria. In the AICITY2024 Task3 TestA, our method performs well, achieving Average Intersection over Union(AIOU) of 0.6080 and ranking eighth in AICITY2024 Task3. Our code will be released in the near future.

关键词： Location awareness computer vision Filtering Splicing Urban areas Merging Interference

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BiMAE - A Bimodal Masked Autoencoder Architecture for Single-Label Hyperspectral Image Classification

BiMAE - A Bimodal Masked Autoencoder Architecture for Single...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Maksim Kukushkin Martin Bogdan Thomas Schmid Leipzig University Leipzig Martin Luther University Halle-Wittenberg Halle (Saale)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Hyperspectral imaging offers manifold opportunities for applications that may not, or only partially, be achieved within the visual spectrum. Our paper presents a novel approach for Single-Label Hyperspectral Image Classification, demonstrated through the example of a key challenge faced by agricultural seed producers: seed purity testing. We employ Self-Supervised Learning and Masked Image Modeling techniques to tackle this task. Recognizing the challenges and costs associated with acquiring hyperspectral data, we aim to develop a versatile method capable of working with visible, arbitrary combinations of spectral bands (multispectral data) and hyperspectral sensor data. By integrating RGB and hyperspectral data, we leverage the detailed spatial information from RGB images and the rich spectral information from hyperspectral data to enhance the accuracy of seed classification. Through evaluations in various real-life scenarios, we demonstrate the flexibility, scalability, and efficiency of our approach.

关键词： Manifolds Visualization Costs Scalability conferences Self-supervised learning pattern recognition

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Listen Then See: Video Alignment with Speaker Attention

Listen Then See: Video Alignment with Speaker Attention

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Aviral Agrawal Carlos Mateo Samudio Lezcano Iqui Balam Heredia-Marin Prabhdeep Singh Sethi Carnegie Mellon University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video-based Question Answering (Video QA) is a challenging task and becomes even more intricate when addressing Socially Intelligent Question Answering (SIQA). SIQA requires context understanding, temporal reasoning, and the integration of multimodal information, but in addition, it requires processing nuanced human behavior. Furthermore, the complexities involved are exacerbated by the dominance of the primary modality (text) over the others. Thus, there is a need to help the task’s secondary modalities to work in tandem with the primary modality. In this work, we introduce a cross-modal alignment and subsequent representation fusion approach that achieves state-of-the-art results (82.06% accuracy) on the Social IQ 2.0 dataset for SIQA. Our approach exhibits an improved ability to leverage the video modality by using the audio modality as a bridge with the language modality. This leads to enhanced performance by reducing the prevalent issue of language overfitting and resultant video modality bypassing encountered by current existing techniques. Our code and models are publicly available at [1].

关键词： Bridges Visualization computer vision Codes Accuracy conferences Question answering (information retrieval)

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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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