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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1548 条记录，以下是151-160 订阅

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A Strong Baseline for Vehicle Re-Identification

A Strong Baseline for Vehicle Re-Identification

引用

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

作者： Huynh, Su, V Nguyen, Nam H. Nguyen, Ngoc T. Nguyen, Vinh T. Q. Huynh, Chau Chuong Nguyen Cybercore AI Ho Chi Minh City Vietnam

ISBN: (纸本)9781665448994

Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. the technical challenges require the algorithms must be robust in different views, resolution, occlusion and illumination conditions. In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance. We then present our solutions, specifically targeting the dataset Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap between real and synthetic data, (2) network modification by stacking multi heads with attention mechanism, (3) adaptive loss weight adjustment. Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark. the code is available at https://***/cybercore-co-ltd/track2_aicity_2021.

关键词： Training computer vision Target tracking Head conferences Urban areas Stacking

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Patchwise Generative ConvNet: Training Energy-Based Models from a Single Natural Image for Internal Learning

Patchwise Generative ConvNet: Training Energy-Based Models f...

引用

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

作者： Zheng, Zilong Xie, Jianwen Li, Ping Univ Calif Los Angeles Los Angeles CA 90095 USA Baidu Res Cognit Comp Lab Bellevue WA USA

ISBN: (纸本)9781665445092

Exploiting internal statistics of a single natural image has long been recognized as a significant research paradigm where the goal is to learn the internal distribution of patches within the image without relying on external training data. Different from prior works that model such a distribution implicitly with a top-down latent variable model (e.g., generator), this paper proposes to explicitly represent the statistical distribution within a single natural image by using an energy-based generative framework, where a pyramid of energy functions, each parameterized by a bottom-up deep neural network, are used to capture the distributions of patches at different resolutions. Meanwhile, a coarse-to-fine sequential training and sampling strategy is presented to train the model efficiently. Besides learning to generate random samples from white noise, the model can learn in parallel with a self-supervised task (e.g., recover the input image from its corrupted version), which can further improve the descriptive power of the learned model. the proposed model is simple and natural in that it does not require an auxiliary model (e.g., discriminator) to assist the training. Besides, it also unifies internal statistics learning and image generation in a single framework. Experimental results presented on various image generation and manipulation tasks, including super-resolution, image editing, harmonization, style transfer, etc, have demonstrated the effectiveness of our model for internal learning.

关键词： Training Image recognition Image synthesis Computational modeling Superresolution Training data Statistical distributions

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Unified Face Attack Detection with Micro Disturbance and a Two-Stage Training Strategy

Unified Face Attack Detection with Micro Disturbance and a T...

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Jiaruo Yu Dagong Lu Xingyue Shi Chenfan Qu Fengjun Guo IntSig Information Co. Ltd Shanghai China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face recognition systems are widely used in real-world scenarios but are susceptible to physical and digital attacks. Effective methods for unified detection of both physical face attacks and digital face attacks are essential to ensure the reliability of face recognition systems. However, how to obtain a unified face attack detection model that has adequate ability of fine-grained perception and cross-domain generalization ability remains an open challenge. To address this issue, we first propose a two-stage training strategy, which utilizes unlabeled face images with masked image modeling and unleashes the potential of vision transformers. Furthermore, we propose a novel method termed as Micro Disturbance, which successfully enriches the representation distribution of forged faces and increases the diversity of the training data, thereby addressing the issue of cross-domain generalization. Attribute to the effectiveness of our proposed methods, our model finally wins the third place in the 5th Face Anti-Spoofing Challenge@cvpr2024, with an impressive ACER score of 5.511.

关键词： Training computer vision Social networking (online) Face recognition Forensics conferences Training data

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Separating lungs in CT scans for improved COVID19 detection

Separating lungs in CT scans for improved COVID19 detection

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Robert Turnbull Simon Mutch Melbourne Data Analytics Platform University of Melbourne Grattan St. Carlton VIC Australia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

this paper outlines our submission for the 4th COV19D competition as part of the ‘Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis’ (DEFAI-MIA) workshop at the computer vision and pattern recognition conference (cvpr). the competition consists of two challenges. the first is to train a classifier to detect the presence of COVID-19 from over one thousand CT scans from the COV19-CT-DB database. the second challenge is to perform domain adaptation by taking the dataset from Challenge 1 and adding a small number of scans (some annotated and other not) for a different distribution. We preprocessed the CT scans to segment the lungs, and output volumes with the lungs individually and together. We then trained 3D ResNet and Swin Transformer models on these inputs. We annotated the unlabeled CT scans using an ensemble of these models and chose the high-confidence predictions as pseudo-labels for fine-tuning. this achieved the winning macro F1 score of 94.89% for Challenge 1 of the competition. It also achieved a second-best macro F1 score of 77.21% for Challenge 2.

关键词： COVID-19 Training computer vision three-dimensional displays Computed tomography conferences Lung

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Exploring Facial Expression recognition through Semi-Supervised Pre-training and Temporal Modeling

Exploring Facial Expression Recognition through Semi-Supervi...

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ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Jun Yu Zhihong Wei Zhongpeng Cai Gongpeng Zhao Zerui Zhang Yongqi Wang Guochen Xie Jichao Zhu Wangyuan Zhu Qingsong Liu Jiaen Liang University of Science and Technology of China Unisound AI Technology Co. Ltd

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Facial Expression recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields. this paper aims to present our approach for the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition, scheduled to be held at cvpr2024. In the facial expression recognition task, the limited size of the FER dataset poses a challenge to the expression recognition model’s generalization ability, resulting in subpar recognition performance. To address this problem, we employ a Semi-supervised learning technique to generate expression category pseudo labels for unlabeled face data. At the same time, we uniformly sampled the labeled facial expression samples and implemented a debiased feedback learning strategy to address the problem of category imbalance in the dataset and the possible data bias in semi-supervised learning. Moreover, to further compensate for the limitation and bias of features obtained only from static images, we introduced a Temporal Encoder to capture temporal relationships between neighbouring expression image features. In the 6th ABAW competition, our method achieved the third place in the official test set, a result that fully demonstrates the effectiveness and competitiveness of our proposed method.

关键词： computer vision Image recognition Accuracy Face recognition conferences Genetic expression Semisupervised learning

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VSpSR: Explorable Super-Resolution via Variational Sparse Representation

VSpSR: Explorable Super-Resolution via Variational Sparse Re...

引用

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

作者： Zhou, Hangqi Huang, Chao Gao, Shangqi Zhuang, Xiahai Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (纸本)9781665448994

Super-resolution (SR) is an ill-posed problem, which means that infinitely many high-resolution (HR) images can be degraded to the same low-resolution (LR) image. To study the one-to-many stochastic SR mapping, we implicitly represent the non-local self-similarity of natural images and develop a Variational Sparse framework for Super-Resolution (VSpSR) via neural networks. Since every small patch of a HR image can be well approximated by the sparse representation of atoms in an over-complete dictionary, we design a two-branch module, i.e., VSpM, to explore the SR space. Concretely, one branch of VSpM extracts patch-level basis from the LR input, and the other branch infers pixel-wise variational distributions with respect to the sparse coefficients. By repeatedly sampling coefficients, we could obtain infinite sparse representations, and thus generate diverse HR images. According to the preliminary results of NTIRE 2021 challenge on learning SR space, our team ranks 7-th in terms of released scores.

关键词： Visualization computer vision Dictionaries conferences Superresolution Neural networks Space exploration

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Robust Deep Interaction recognition Framework with Multi-Stage Feature Analysis

Robust Deep Interaction Recognition Framework with Multi-Sta...

引用

ieee International conference on Multi Topic

作者： Tanvir Fatima Naik Bukht Ahmad Jalal Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

computer vision and pattern recognition have been interested in human interactions on images. It is indeed that interaction recognition is an area of interest in most research areas. If this is the case, the research proposed uses a Deep Neural Network to design an activity recognition system. An HSI color transformation is integrated at the initial stages of the system to improve the clarity of the video frames. Additionally, we apply Gaussian filters to minimize noise interference. MOT and statistical methods are used to silhouette extraction. the extraction process uses the Texton maps, FAST, and Local Intensity Order pattern (LIOP) technique. the Gray Wolf algorithm is then used to discriminate the features, and then the most meaningful independent content that describes the data structure is identified. Finally, the ANN is fed the last feature and classified into relevant human interactions. this approach is tested with the SBU Interaction dataset and the recognition rate attained is 92% through the experimental method.

关键词： computer vision Accuracy Statistical analysis Computational modeling Surveillance Artificial neural networks Feature extraction Real-time systems Classification algorithms Low latency communication

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A Video-Based End-to-end Pipeline for Non-nutritive Sucking Action recognition and Segmentation in Young Infants 26th

A Video-Based End-to-end Pipeline for Non-nutritive Sucking ...

引用

26th International conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Zhu, Shaotong Wan, Michael Hatamimajoumerd, Elaheh Jain, Kashish Zlota, Samuel Kamath, Cholpady Vikram Rowan, Cassandra B. Grace, Emma C. Goodwin, Matthew S. Hayes, Marie J. Schwartz-Mette, Rebecca A. Zimmerman, Emily Ostadabbas, Sarah Northeastern Univ Dept Elect & Comp Engn Augmented Cognit Lab Boston MA 02115 USA Northeastern Univ Roux Inst Portland ME USA Northeastern Univ Khoury Coll Comp Sci Boston MA USA Northeastern Univ Bouve Coll Hlth Sci Boston MA USA Univ Maine Psychol Dept Orono ME USA

ISBN: (纸本)9783031438943;9783031438950

We present an end-to-end computer vision pipeline to detect non-nutritive sucking (NNS)-an infant sucking pattern with no nutrition delivered-as a potential biomarker for developmental delays, using off-the-shelf baby monitor video footage. One barrier to clinical (or algorithmic) assessment of NNS stems from its sparsity, requiring experts to wade through hours of footage to find minutes of the relevant activity. Our NNS activity segmentation algorithm tackles this problem by identifying periods of NNS with high certainty-up to 94.0% average precision and 84.9% average recall across 30 heterogeneous 60 s clips, drawn from our manually annotated NNS clinical in-crib dataset of 183 h of overnight baby monitor footage from 19 infants. Our method is based on an underlying NNS action recognition algorithm, which uses spatiotemporal deep learning networks and infant-specific pose estimation, achieving 94.9% accuracy in binary classification of 960 2.5 s balanced NNS vs. non-NNS clips. Tested on our second, independent, and public NNS in-the-wild dataset, NNS recognition classification reaches 92.3% accuracy, and NNS segmentation achieves 90.8% precision and 84.2% recall. Our code and the manually annotated NNS in-the-wild dataset can be found at https://***/ostadabbas/NNS-Detection- and-Segmentation. Supported by MathWorks and NSF-CAREER Grant #2143882.

关键词： Non-nutritive sucking Action recognition Action segmentation Optical flow Temporal convolution

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Fast Vehicle Turning-Movement Counting using Localization-based Tracking

Fast Vehicle Turning-Movement Counting using Localization-ba...

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

作者： Gloudemans, Derek Work, Daniel B. Vanderbilt Univ Inst Software Integrated Syst 1025 16th Ave S Nashville TN 37212 USA

ISBN: (纸本)9781665448994

Despite the high utility of traffic volume and turning movement data, such data is still hard to come by for the vast majority of roadways and intersections in nearly every city. Edge computing devices offer a promising tool for recording turning movement data if lightweight algorithms can be designed to run in real-time with relatively modest computational complexity. To that end, this work presents Vehicle Turning-Movement Counting using Localization-based Tracking (LBT-Count). this method is fast because it never performs detection on a full frame. Instead, only a few portions of the image are cropped and used to detect objects within the frame. the method achieves competitive performance on the public evaluation server for Track 1 of the AI City Challenge (7th overall on the first 50% of data). Furthermore, we show that LBT-Count is 52% faster than an analogous counting algorithm utilizing a traditional tracking-by-detection framework on available challenge data.

关键词： Performance evaluation Tracking Urban areas Tools Turning Agriculture Real-time systems

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Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans

Domain adaptation, Explainability & Fairness in AI for Medic...

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ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Dimitrios Kollias Anastasios Arsenos Stefanos Kollias Queen Mary University of London UK National Technical University of Athens Greece National Infrastructures for Research and Technology Greece

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

the paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the ’Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-AI-MIA)’ Workshop of the 2024 computer vision and pattern recognition (cvpr) conference. the Competition is the 4th in the series, following the first three Competitions held in the framework of ICCV 2021, ECCV 2022 and ICASSP 2023 International conferences respectively. It includes two Challenges on: i) Covid-19 Detection and ii) Covid-19 Domain Adaptation. the Competition use data from COV19-CT-DB database, which is described in the paper and includes a large number of chest CT scan series. Each chest CT scan series consists of a sequence of 2-D CT slices, the number of which is between 50 and 700. Training, validation and test datasets have been extracted from COV19-CT-DB and provided to the participants in both Challenges. the paper presents the baseline models used in the Challenges and the performance which was obtained respectively, together with the best corresponding performances of the methods submitted and evaluated in the Challenges.

关键词： COVID-19 Training computer vision Image analysis Databases Computed tomography conferences

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