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Automatic Play Segmentation of Hockey Videos

Automatic Play Segmentation of Hockey Videos

引用

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

作者： Pidaparthy, Hemanth Dowling, Michael H. Elder, James H. York Univ N York ON Canada Queens Univ Kingston ON Canada

ISBN: (纸本)9781665448994

Most team sports such as hockey involve periods of active play interleaved with breaks in play. When watching a game remotely, many fans would prefer an abbreviated game showing only periods of active play. Here we address the problem of identifying these periods in order to produce a time-compressed viewing experience. Our approach is based on a hidden Markov model of play state driven by deep visual and optional auditory cues. We find that our deep visual cues generalize well across different cameras and that auditory cues can improve performance but only if unsupervised methods are used to adapt emission distributions to domain shift across games. Our system achieves temporal compression rates of 20-50% at a recall of 96%.

关键词： Visualization Fans computer vision conferences Hidden Markov models Games Cameras

来源：评论

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Super-Resolution based Video Coding Scheme

Super-Resolution based Video Coding Scheme

引用

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

作者： Cho, Hyun Min Choi, Kiho Gacheon Univ Sch Comp 1342 Seongnamdaero Seongnam Si Gyeonggi Do South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we present a super-resolution-based video coding scheme that compresses video data by combining traditional hybrid video coding and Convolutional neural network-based video coding. During video encoding, downsampling reduces the resolution of an original video in both horizontal and vertical directions to reduce original video data, and Convolutional neural networkbased super-resolution is employed after the decoding process to recover the resolution of the reconstructed video during upsampling. For core encoding and decoding processes, the latest video coding standard (i.e., VVC/H.266) is conducted. The experimental results show that the proposed method can provide efficient coding performance while maintaining good visual quality.

关键词： Video coding Visualization Image coding Superresolution Encoding Decoding pattern recognition

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WiCV 2019: The Sixth Women In computer vision Workshop 32

WiCV 2019: The Sixth Women In Computer Vision Workshop

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Amerini, Irene Balashova, Elena Ebrahimi, Sayna Leonard, Kathryn Nagrani, Arsha Salvador, Amaia Univ Florence Florence Italy Princeton Univ Princeton NJ 08544 USA Univ Calif Berkeley Berkeley CA USA Occident Coll Los Angeles CA USA Univ Oxford Oxford England Univ Politen Catalunya Barcelona Spain

ISBN: (纸本)9781728125060

In this paper we present the Women in computer vision Workshop - WiCV 2019, organized in conjunction with CVPR 2019. This event is meant for increasing the visibility and inclusion of women researchers in computer vision field. computer vision and machine learning have made incredible progress over the past years, but the number of female researchers is still low both in the academia and in the industry. WiCV is organized especially for this reason: to raise visibility of female researchers, to increase collaborations between them, and to provide mentorship to female junior researchers in the field. In this paper, we present a report of trends over the past years, along with a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.

关键词： computer vision

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Surrogate Contrastive Network for Supervised Band Selection in Multispectral computer vision Tasks 32

Surrogate Contrastive Network for Supervised Band Selection ...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Bernal, Edgar A. Univ Rochester Rochester Data Sci Consortium 260 E Main St Rochester NY 14604 USA

ISBN: (纸本)9781728125060

computer vision techniques that operate on hyper- and multispectral imagery benefit from the additional amount of spectral information relative to those that exploit traditional RGB or monochromatic visual data. However, the increased volume of data to be processed brings about additional memory, storage and computational requirements. In order to address such limitations, a wide range of techniques for dimensionality reduction have been introduced by previous work. In this paper, we propose a framework for spectral band selection that is highly data- and computationally efficient. The method leverages a convolutional siamese network learned by optimizing a contrastive loss, and performs band selection based on the low-dimensional data embeddings produced by the network. We empirically demonstrate the efficacy of the method on an object detection task from aerial multispectral imagery. The results show that, in spite of the method's frugality, it produces very competitive band selection results against the evaluated competing techniques.

关键词： Object detection

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CL-Gym: Full-Featured PyTorch Library for Continual Learning

CL-Gym: Full-Featured PyTorch Library for Continual Learning

引用

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

作者： Mirzadeh, Seyed Iman Ghasemzadeh, Hassan Washington State Univ Pullman WA 99164 USA

ISBN: (纸本)9781665448994

Continual learning (CL) has become one of the most active research venues within the artificial intelligence community in recent years. Given the significant amount of attention paid to continual learning, the need for a library that facilitates both research and development in this field is more visible than ever. However, CL algorithms' codes are currently scattered over isolated repositories written with different frameworks, making it difficult for researchers and practitioners to work with various CL algorithms and benchmarks using the same interface. In this paper, we introduce CL-Gym, a full-featured continual learning library that overcomes this challenge and accelerates the research and development. In addition to the necessary infrastructure for running end-to-end continual learning experiments, CL-Gym includes benchmarks for various CL scenarios and several state-of-the-art CL algorithms. In this paper, we present the architecture, design philosophies, and technical details behind CL-Gym (1).

关键词： computer vision Philosophical considerations conferences computer architecture Learning (artificial intelligence) Benchmark testing Libraries

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Self Supervised Scanpath Prediction Framework for Painting Images

Self Supervised Scanpath Prediction Framework for Painting I...

引用

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

作者： Tliba, Marouane Kerkouri, Mohamed Amine Chetouani, Aladine Bruno, Alessandro Univ Orleans Orleans France Bournemouth Univ Poole Dorset England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In our paper, we propose a novel strategy to learn distortion invariant latent representation from painting pictures for visual attention modelling downstream task. In further detail, we design an unsupervised framework that jointly maximises the mutual information over different painting styles. To show the effectiveness of our approach, we firstly propose a lightweight scanpath baseline model and compare its performance to some state-of-the-art methods. Secondly, we train the encoder of our baseline model on large-scale painting images to study the efficiency of the proposed self-supervised strategy. The lightweight decoder proves effective in learning from the self-supervised pre-trained encoder with better performances than the end-to-end fine-tuned supervised baseline on two painting datasets, including a proposed new visual attention modelling dataset.

关键词： Visualization Self-supervised learning Predictive models Observers Quality assessment pattern recognition Task analysis

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Evaluating the Immediate Applicability of Pose Estimation for Sign Language recognition

Evaluating the Immediate Applicability of Pose Estimation fo...

引用

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

作者： Moryossef, Amit Tsochantaridis, Ioannis Dinn, Joe Camgoez, Necati Cihan Bowden, Richard Jiang, Tao Rios, Annette Muller, Mathias Ebling, Sarah Bar Ilan Univ Ramat Gan Israel Google Mountain View CA 94043 USA Univ Surrey Guildford Surrey England Univ Zurich Zurich Switzerland

ISBN: (纸本)9781665448994

Sign languages are visual languages produced by the movement of the hands, face, and body. In this paper, we evaluate representations based on skeleton poses, as these are explainable, person-independent, privacy-preserving, low-dimensional representations. Basically, skeletal representations generalize over an individual's appearance and background, allowing us to focus on the recognition of motion. But how much information is lost by the skeletal representation? We perform two independent studies using two state-of-the-art pose estimation systems. We analyze the applicability of the pose estimation systems to sign language recognition by evaluating the failure cases of the recognition models. Importantly, this allows us to characterize the current limitations of skeletal pose estimation approaches in sign language recognition.

关键词： Analytical models Visualization computer vision Assistive technology Face recognition conferences Pose estimation

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Active vision Dataset Benchmark 31

Active Vision Dataset Benchmark

引用

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

作者： Ammirato, Phil Berg, Alexander C. Kosecka, Jana Univ N Carolina Chapel Hill NC 27514 USA George Mason Univ Fairfax VA 22030 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Several recent efforts in computer vision indicate a trend toward studying and understanding problems in larger scale environments, beyond single images, and focus on connections to tasks in navigation, mobile manipulation, and visual question answering. A common goal of these tasks is the capability of moving in the environment, acquiring novel views during perception and while performing a task. This capability comes easily in synthetic environments, however achieving the same effect with real images is much more laborious. We propose using the existing Active vision Dataset to form a benchmark for such problems in a real-world settings with real images. The dataset is well suited for evaluating tasks of multiview active recognition, target driven navigation, and target search, and also can be effective for studying the transfer of strategies learned in simulation to real settings.

关键词： Task analysis Training Visualization Robots Benchmark testing Navigation

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Symmetric Parallax Attention for Stereo Image Super-Resolution

Symmetric Parallax Attention for Stereo Image Super-Resoluti...

引用

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

作者： Wang, Yingqian Ying, Xinyi Wang, Longguang Yang, Jungang An, Wei Guo, Yulan Natl Univ Def Technol Zunyi Guizhou Peoples R China

ISBN: (纸本)9781665448994

Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used. Since stereo images are highly symmetric under epipolar constraint, in this paper, we improve the performance of stereo image SR by exploiting symmetry cues in stereo image pairs. Specifically, we propose a symmetric bi-directional parallax attention module (biPAM) and an inline occlusion handling scheme to effectively interact cross-view information. Then, we design a Siamese network equipped with a biPAM to super-resolve both sides of views in a highly symmetric manner. Finally, we design several illuminance-robust losses to enhance stereo consistency. Experiments on four public datasets demonstrate the superior performance of our method.

关键词： Training computer vision conferences Superresolution Estimation Bidirectional control Robustness

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A Universal Encoder Rate Distortion Optimization Framework for Learned Compression

A Universal Encoder Rate Distortion Optimization Framework f...

引用

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

作者： Zhao, Jing Li, Bin Li, Jiahao Xiong, Ruiqin Lu, Yan Peking Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665448994

Learning-based image compression has drawn increasing attention in recent years. Despite impressive progress has been made, it still lacks a universal encoder optimization method to seek efficient representation for different images. In this paper, we develop a universal rate distortion optimization framework for learning-based compression, which adaptively optimizes latents and side information together for each image. The proposed framework is independent of network architecture and can be flexibly applied to existing and potential future compression networks. Experimental results demonstrate that we can achieve 6.6% bit rate saving against the latest traditional codec, i.e., VVC, yielding the state-of-the-art compression ratio. Moreover, with the proposed optimization framework, we win the first place in CLIC validation phase for all the three different bit rates in terms of PSNR.

关键词： computer vision Image coding Codecs conferences Bit rate Rate-distortion Optimization methods

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