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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods ...

引用

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

作者： Perez-Pellitero, Eduardo Catley-Chandar, Sibi Shaw, Richard Leonardis, Ales Timofte, Radu Zhang, Zexin Liu, Cen Peng, Yunbo Lin, Yue Yu, Gaocheng Zhang, Jin Ma, Zhe Wang, Hongbin Chen, Xiangyu Wang, Xintao Wu, Haiwei Liu, Lin Dong, Chao Zhou, Jiantao Yan, Qingsen Zhang, Song Chen, Weiye Liu, Yuhang Zhang, Zhen Zhang, Yanning Shi, Javen Qinfeng Gong, Dong Zhu, Dan Sun, Mengdi Chen, Guannan Hu, Yang Li, Haowei Zou, Baozhu Liu, Zhen Lin, Wenjie Jiang, Ting Jiang, Chengzhi Li, Xinpeng Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Marin-Vega, Juan Sloth, Michael Schneider-Kamp, Peter Rottger, Richard Li, Chunyang Bao, Long He, Gang Xu, Ziyao Xu, Li Zhan, Gen Sun, Ming Wen, Xing Li, Junlin Li, Jinjing Li, Chenghua Gang, Ruipeng Li, Fangya Liu, Chenming Feng, Shuang Lei, Fei Liu, Rui Ruan, Junxiang Dai, Tianhong Li, Wei Lu, Zhan Liu, Hengyan Huang, Peian Ren, Guangyu Luo, Yonglin Liu, Chang Tu, Qiang Ma, Sai Cao, Yizhen Tel, Steven Heyrman, Barthelemy Ginhac, Dominique Lee, Chul Kim, Gahyeon Park, Seonghyun An Gia Vien Truong Thanh Nhat Mai Yoon, Howoon Tu Vo Holston, Alexander Zaheer, Sheir Park, Chan Y. Huawei Noahs Ark Lab Hong Kong Peoples R China Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland Netease Games AI Lab Shanghai Peoples R China AntGroup Hangzhou Peoples R China Univ Macau Macau Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Tencent PCG Shenzhen Peoples R China Univ Sci & Technol China Hefei Peoples R China Univ Adelaide Adelaide SA Australia Xidian Univ Xian Peoples R China Northwestern Polytech Univ Xian Peoples R China Univ New South Wales Sydney NSW Australia BOE Technol Grp Co Ltd Beijing Peoples R China CZUR Technol Grp Co Ltd Liaoning Peoples R China Megvii Technol Beijing Peoples R China Univ Southern Denmark Dept Math & Comp Sci IMADA Aarhus Denmark Esoft Syst Odense Denmark Xiaomi Beijing Peoples R China ByteDance Beijing Peoples R China Kuaishou Technol Beijing Peoples R China Commun Univ China Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China NRTA Acad Broadcasting Sci Beijing Peoples R China Tetras AI Technol Hangzhou Peoples R China Imperial Coll London London England Tsinghua Univ Beijing Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland Sun Yat Sen Univ Guangzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Beijing Inst Technol Beijing Peoples R China NRTA Acad Broadcasting Sciencience Beijing Peoples R China Univ Burgundy ImViA Lab Dijon France Dongguk Univ Dept Multimedia Engn Seoul South Korea Gachon Univ Seongnam South Korea KC Machine Learning Lab Seoul South Korea

ISBN: (纸本)9781665487399

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with cvpr 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under- or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).

关键词： computer vision Runtime conferences Imaging Dynamic range Particle measurements Market research

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Detection of COVID-19 Using Machine Learning 1

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International conference on computer vision and Machine Intelligence, CVMI 2022

作者： Kumar, Saurav Tripathi, Rohit Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9789811978678

ISBN: (纸本)9789811978661

In this paper, we aimed at experimenting and developing suitable machine learning algorithms along with some deep learning architectures, to achieve the task of COVID-19 classification. We experimented with various supervised learning algorithms for the classification of COVID-19 images and normal images, from X-ray images and radiography images. We evaluated the performance of our models on performance metrics like accuracy, precision, recall, F1 score, and Cohen Kappa score. The proposed model achieved the accuracy of 98.5%, F1 score of 97%, precision of 95%, and Cohen Kappa score of 0.88. In this experiment, we jotted down the values of performance of various performance metrics, that were obtained after tuning various parameters of the model and on different image resolutions of input image. We performed linear regression on the results to study the behavior of various performance metrics when we tuned our model with different parameters with different input image resolutions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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From Evaluation to Verification: Towards Task-oriented Relevance Metrics for Pedestrian Detection in Safety-critical Domains

From Evaluation to Verification: Towards Task-oriented Relev...

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

作者： Lyssenko, Maria Gladisch, Christoph Heinzemann, Christian Woehrle, Matthias Triebel, Rudolph Robert Bosch GmbH Corp Res Robert Bosch Campus 1 D-71272 Renningen Germany Tech Univ Munich Boltzmannstr 3 D-85748 Garching Germany German Aerosp Ctr DLR Munchener Str 20 D-82234 Wessling Germany

ISBN: (纸本)9781665448994

Whenever a visual perception system is employed in safety-critical applications such as automated driving, a thorough, task-oriented experimental evaluation is necessary to guarantee safe system behavior. While most standard evaluation methods in computer vision provide a good comparability on benchmarks, they tend to fall short on assessing the system performance that is actually relevant for the given task. In our work, we consider pedestrian detection as a highly relevant perception task, and we argue that standard measures such as Intersection over Union (IoU) give insufficient results, mainly because they are insensitive to important physical cues including distance, speed, and direction of motion. Therefore, we investigate so-called relevance metrics, where specific domain knowledge is exploited to obtain a task-oriented performance measure focusing on distance in this initial work. Our experimental setup is based on the CARLA simulator and allows a controlled evaluation of the impact of that domain knowledge. Our first results indicate a linear decrease of the IoU related to the pedestrians' distance, leading to the proposal of a first relevance metric that is also conditioned on the distance.

关键词： Measurement Meters computer vision System performance pattern recognition Proposals Motion measurement

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Separating lungs in CT scans for improved COVID19 detection

Separating lungs in CT scans for improved COVID19 detection

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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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Puck localization and multi-task event recognition in broadcast hockey videos

Puck localization and multi-task event recognition in broadc...

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

作者： Vats, Kanav Fani, Mehrnaz Clausi, David A. Zelek, John Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781665448994

Puck localization is an important problem in ice hockey video analytics useful for analyzing the game, determining play location, and assessing puck possession. The problem is challenging due to the small size of the puck, excessive motion blur due to high puck velocity and occlusions due to players and boards. In this paper, we introduce and implement a network for puck localization in broadcast hockey video. The network leverages expert NHL play-by-play annotations and uses temporal context to locate the puck. Player locations are incorporated into the network through an attention mechanism by encoding player positions with a Gaussian-based spatial heatmap drawn at player positions. Since event occurrence on the rink and puck location are related, we also perform event recognition by augmenting the puck localization network with an event recognition head and training the network through multi-task learning. Experimental results demonstrate that the network is able to localize the puck with an AUC of 73:1% on the test set. The puck location can be inferred in 720p broadcast videos at 5 frames per second. It is also demonstrated that multi-task learning with puck location improves event recognition accuracy.

关键词： Location awareness Training Head Image resolution Annotations Visual analytics Ice

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On the Application of Binary Neural Networks in Oblivious Inference

On the Application of Binary Neural Networks in Oblivious In...

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

作者： Samragh, Mohammad Hussain, Siam Zhang, Xinqiao Huang, Ke Koushanfar, Farinaz Univ Calif San Diego La Jolla CA 92093 USA San Diego State Univ San Diego CA 92182 USA

ISBN: (纸本)9781665448994

This paper explores the application of Binary Neural Networks (BNN) in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on her data by a trained model held by the server without disclosing the data or leaning the model parameters. We make two contributions to this field. First, we devise light-weight cryptographic protocols designed specifically to exploit the unique characteristics of BNNs. Second, we present dynamic exploration of the runtime -accuracy tradeoff of BNNs in a single -shot training process. While previous works trained multiple BNNs with different computational complexities (which is cumbersome due to the slow convergence of BNNs), we train a single BNN that can perform inference under different computational budgets. Compared to CryptFlow2, the state-of-the-art in oblivious inference of non binary DNNs, our approach reaches 2x faster inference at the same accuracy. Compared to XONN, the state-of-the-art in oblivious inference of binary networks, we achieve 2x to llx faster inference while obtaining higher accuracy.

关键词： Training computer vision Data analysis Computational modeling Face recognition Neural networks Data models

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Detecting Stable Keypoints from Events through Image Gradient Prediction

Detecting Stable Keypoints from Events through Image Gradien...

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

作者： Chiberre, Philippe Perot, Etienne Sironi, Amos Lepetit, Vincent PROPHESEE Paris France Univ Gustave Eiffel CNRS Ecole Ponts LIGM Marne La Vallee France

ISBN: (纸本)9781665448994

We present a method that detects stable keypoints from an event stream at high speed with a low memory footprint. Our key observation connects two points: It should be easier to reconstruct the image gradients rather than the image itself from the events, and the Harris corner detector, one of the most reliable keypoint detectors for short baseline regular images, depends on the image gradients, not the image. We therefore introduce a recurrent convolutional neural network to predict image gradients from events. As image gradients and events are correlated, this prediction task is relatively easy and we can keep this network very small. We train our network solely on synthetic data. Extracting Harris corners from these gradients is then very efficient. Moreover, in contrast to learned methods, we can change the hyperparameters of the detector without retraining. Our experiments confirm that predicting image gradients rather than images is much more efficient, and that our approach predicts stable corner points which are easier to track for a longer time compared to state-of-the-art event-based methods.

关键词： Image analysis Detectors computer architecture Streaming media Cameras pattern recognition Reliability

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DeVLBert: Out-of-distribution Visio-Linguistic Pretraining with Causality

DeVLBert: Out-of-distribution Visio-Linguistic Pretraining w...

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

作者： Zhang, Shengyu Jiang, Tan Wang, Tan Kuang, Kun Zhao, Zhou Zhu, Jianke Yu, Jin Yang, Hongxia Wu, Fei Zhejiang Univ Hangzhou Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose to investigate out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned. Existing methods for this problem are purely likelihood-based, leading to the spurious correlations and hurt the generalization ability when transferred to out-of-domain downstream tasks. By spurious correlation, we mean that the conditional probability of one token (object or word) given another one can be high (due to the dataset biases) without robust (causal) relationships between them. To mitigate such dataset biases, we propose a Deconfounded Visio-Linguistic Bert framework, abbreviated as DeVLBert(1), to perform intervention based-learning. We borrow the idea of the backdoor adjustment from the research field of causality and propose several neural-network based architectures for Bert-style out-of-domain pretraining. The quantitative results on three downstream tasks, Image Retrieval (IR), Zero-shot IR, and Visual Question Answering, show the effectiveness of DeVLBert by boosting generalization ability(2).

关键词： Visualization computer vision Correlation conferences Image retrieval computer architecture Knowledge discovery

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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...

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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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BasisNet: Two-stage Model Synthesis for Efficient Inference

BasisNet: Two-stage Model Synthesis for Efficient Inference

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

作者： Zhang, Mingda Chu, Chun-Te Zhmoginov, Andrey Howard, Andrew Jou, Brendan Zhu, Yukun Zhang, Li Hwa, Rebecca Kovashka, Adriana Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665448994

In this work, we present BasisNet which combines recent advancements in efficient neural network architectures, conditional computation, and early termination in a simple new form. Our approach incorporates a lightweight model to preview the input and generate input-dependent combination coefficients, which later controls the synthesis of a more accurate specialist model to make final prediction. The two-stage model synthesis strategy can be applied to any network architectures and both stages are jointly trained. We also show that proper training recipes are critical for increasing generalizability for such high capacity neural networks. On ImageNet classification benchmark, our BasisNet with MobileNets as backbone demonstrated clear advantage on accuracy-efficiency trade-off over several strong baselines. Specifically, BasisNet-MobileNetV3 obtained 80.3% top-1 accuracy with only 290M MultiplyAdd operations, halving the computational cost of previous state-of-the-art without sacrificing accuracy. With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80.0% on ImageNet.

关键词： Training computer vision Computational modeling conferences Neural networks computer architecture Predictive models

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