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共 4491 条记录，以下是91-100 订阅

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Affective Gaming: A Comprehensive Survey

Affective Gaming: A Comprehensive Survey

引用

26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Kotsia, Irene Zafeiriou, Stefanos Fotopoulos, Spiros Queen Mary Univ London Sch Elect Engn & Comp Sci London England Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England Univ Patras Dept Phys Elect Lab GR-26110 Patras Greece

ISBN: (纸本)9780769549903

A typical gaming scenario involves a player interacting with a game by a specialized input device, such as a joystic, a mouse, a keyboard etc. Recent technological advances have enabled the introduction of more elaborated approaches in which the player is able to interact with the game using his/her body pose, facial expressions, actions, even his physiological signals (heart beat rate, encephalogram, skin conductivity etc). The future lies in 'affective gaming', that is games that will be 'intelligent' enough not only to extract the player's commands by his speech and gestures but also by his behavioral cues, and his/her emotional states and adjust their game plot accordingly, in order to ensure more realistic and satisfactory gameplay experience. In this paper, we review the area of affective gaming by describing existing approaches and discussing recent technological advances. We elaborate on different sources of affect information and summarize the existing commercial affective gaming applications. We proceed with outlining some of the most important problems that have to be tackled in order to create more realistic and efficient interactions between players and games and conclude by high-lighting the challenges such systems must overcome.

关键词： brain-computer interfaces computer games

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Persistent-Transient Duality in Human Behavior Modeling

Persistent-Transient Duality in Human Behavior Modeling

引用

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

作者： Hung Tran Vuong Le Venkatesh, Svetha Truyen Tran Deakin Univ Appl AI Inst Geelong Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose to model the persistent-transient duality in human behavior using a parent-child multi-channel neural network, which features a parent persistent channel that manages the global dynamics and children transient channels that are initiated and terminated on-demand to handle detailed interactive actions. The short-lived transient sessions are managed by a proposed Transient Switch. The neural framework is trained to discover the structure of the duality automatically. Our model shows superior performances in human-object interaction motion prediction.

关键词： computer vision Computational modeling conferences Dynamics Neural networks Switches Predictive models

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Application of computer vision and vector space model for tactical movement classification in badminton 30

Application of computer vision and vector space model for ta...

引用

30th ieee/CVF conference on computer vision and pattern recognition workshops (cvprw)

作者： Weeratunga, Kokum Dharmaratne, Anuja How, Khoo Boon Monash Univ Sch Informat Technol Clayton Vic Australia Monash Univ Sch Engn Clayton Vic Australia Natl Sports Inst Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781538607336

Performance profiling in sports allow evaluating opponents' tactics and the development of counter tactics to gain a competitive advantage. The work presented develops a comprehensive methodology to automate tactical profiling in elite badminton. The proposed approach uses computer vision techniques to automate data gathering from video footage. The image processing algorithm is validated using video footage of the highest level tournaments, including the Olympic Games. The average accuracy of player position detection is 96.03% and 97.09% on the two halves of a badminton court. Next, frequent trajectories of badminton players are extracted and classified according to their tactical relevance. The classification performs at 97.79% accuracy, 97.81% precision, 97.44% recall, and 97.62% F-score. The combination of automated player position detection, frequent trajectory extraction, and the subsequent classification can be used to automatically generate player tactical profiles.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Intelligent Scene Caching to Improve Accuracy for Energy-Constrained Embedded vision

Intelligent Scene Caching to Improve Accuracy for Energy-Con...

引用

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

作者： Simpson, Benjamin Lubana, Ekdeep Liu, Yuchen Dick, Robert Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781728193601

We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-aware manner. The key concept is to combine guided, highly heterogeneous sampling with an intelligent Scene Cache. This enables the system to adapt to spatial and temporal patterns in the scene, thus reducing redundant data capture and processing. A software prototype of our framework running on a general-purpose embedded processor enables superior object detection accuracy (by 56%) at similar energy consumption (slight improvement of 4%) compared to an H.264 hardware accelerator.

关键词： Image enhancement

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NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study

NTIRE 2021 Challenge on Quality Enhancement of Compressed Vi...

引用

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

作者： Yang, Ren Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781665448994

This paper introduces a novel dataset for video enhancement and studies the state-of-the-art methods of the NTIRE 2021 challenge on quality enhancement of compressed video. The challenge is the first NTIRE challenge in this direction, with three competitions, hundreds of participants and tens of proposed solutions. Our newly collected Large-scale Diverse Video (LDV) dataset is employed in the challenge. In our study, we analyze the solutions of the challenges and several representative methods from previous literature on the proposed LDV dataset. We find that the NTIRE 2021 challenge advances the state-of-theart of quality enhancement on compressed video.

关键词： computer vision Databases conferences Benchmark testing Solids pattern recognition

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Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack

Multi-Class Multi-Movement Vehicle Counting Based on CenterT...

引用

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

作者： Kocur, Viktor Ftacnik, Milan Comenius Univ Fac Math Phys & Informat Bratislava Slovakia

ISBN: (纸本)9781665448994

In this paper we present our approach to the Track 1 of the 2021 AI City Challenge. The goal of the challenge track is to to analyse footage captured with traffic cameras by counting the number of vehicles performing various pre-defined motions of interest. Our approach is based on the CenterTrack object detection and tracking neural network used in conjunction with a simple IoU-based tracking algorithm. In the public evaluation server our system achieved the S1 score of 0.8449 placing it at the 8th place on the public leaderboard.

关键词： computer vision Tracking conferences Urban areas Neural networks Object detection computer architecture

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Stacked U-Nets with Multi-Output for Road Extraction 31

Stacked U-Nets with Multi-Output for Road Extraction

引用

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

作者： Sun, Tao Chen, Zehui Yang, Wenxiang Wang, Yin Tongji Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

With the recent advances of Convolutional Neural Networks (CNN) in computer vision, there have been rapid progresses in extracting roads and other features from satellite imagery for mapping and other purposes. In this paper, we propose a new method for road extraction using stacked U-Nets with multiple output. A hybrid loss function is used to address the problem of unbalanced classes of training data. Post-processing methods, including road map vectorization and shortest path search with hierarchical thresholds, help improve recall. The overall improvement of mean IoU compared to the vanilla VGG network is more than 20%.

关键词： Roads Decoding computer vision Encoding Feature extraction Neural networks Satellites

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Privacy Leakage of Adversarial Training Models in Federated Learning Systems

Privacy Leakage of Adversarial Training Models in Federated ...

引用

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

作者： Zhang, Jingyang Chen, Yiran Li, Hai Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks. In this work, we further reveal this unsettling property of AT by designing a novel privacy attack that is practically applicable to the privacy-sensitive Federated Learning (FL) systems. Using our method, the attacker can exploit AT models in the FL system to accurately reconstruct users' private training images even when the training batch size is large. Code is available at https://***/zjysteven/PrivayAttack_AT_FL.

关键词： Training Deep learning Privacy computer vision conferences Neural networks Collaborative work

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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...

引用

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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Where are my clothes? A multi-level approach for evaluating deep instance segmentation architectures on fashion images

Where are my clothes? A multi-level approach for evaluating ...

引用

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

作者： Jouanneau, Warren Bugeau, Aurelie Palyart, Marc Papadakis, Nicolas Vezard, Laurent Univ Bordeaux LaBRI CNRS Bordeaux INP F-33400 Talence France Lectra F-33610 Cestas France Univ Bordeaux IMB CNRS Bordeaux INPUMR 5251 F-33400 Talence France

ISBN: (纸本)9781665448994

In this paper we present an extensive evaluation of instance segmentation in the context of images containing clothes. We propose a multi level evaluation that completes the classical overlapping criteria given by IoU. In particular, we quantify both the contour and color content accuracy of the the predicted segmentation masks. We demonstrate that the proposed evaluation framework is relevant to obtain meaningful insights on models performance through experiments conducted on five state of the art instance segmentation methods.

关键词： Image segmentation computer vision Image color analysis conferences Computational modeling computer architecture pattern recognition

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