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NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

NTIRE 2022 Challenge on Super-Resolution and Quality Enhance...

引用

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

作者： Yang, Ren Timofte, Radu Zheng, Meisong Xing, Qunliang Qiao, Minglang Xu, Mai Jiang, Lai Liu, Huaida Chen, Ying Ben, Youcheng Zhou, Xiao Fu, Chen Cheng, Pei Yu, Gang Li, Junyi Wu, Renlong Zhang, Zhilu Shang, Wei Lv, Zhengyao Chen, Yunjin Zhou, Mingcai Ren, Dongwei Zhang, Kai Zuo, Wangmeng Ostyakov, Pavel Dmitry, Vyal Soltanayev, Shakarim Sergey, Chervontsev Magauiya, Zhussip Zou, Xueyi Yan, Youliang Michelini, Pablo Navarrete Lu, Yunhua Zhang, Diankai Liu, Shaoli Gao, Si Wu, Biao Zheng, Chengjian Zhang, Xiaofeng Lu, Kaidi Wang, Ning Thuong Nguyen Canh Bach, Thong Wang, Qing Sun, Xiaopeng Ma, Haoyu Zhao, Shijie Li, Junlin Xie, Liangbin Shi, Shuwei Yang, Yujiu Wang, Xintao Gu, Jinjin Dong, Chao Shi, Xiaodi Nian, Chunmei Jiang, Dong Lin, Jucai Xie, Zhihuai Ye, Mao Luo, Dengyan Peng, Liuhan Chen, Shengjie Liu, Xin Wang, Qian Liang, Boyang Dong, Hang Huang, Yuhao Chen, Kai Guo, Xingbei Sun, Yujing Wu, Huilei Wei, Pengxu Huang, Yulin Chen, Junying Lee, Ik Hyun Khowaja, Sunder Ali Yoon, Jiseok Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Julius Maximilian Univ Wurzburg Wurzburg Germany Alibaba Grp Dept Tao Technol Beijing Peoples R China Beihang Univ Beijing Peoples R China Tencent Shenzhen Peoples R China Harbin Inst Technol Harbin Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China BOE Technol Grp Co Ltd Beijing Peoples R China ZTE Audio & Video Technol Platform Dept Nanjing Peoples R China Osaka Univ Osaka Japan ByteDance Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT Shenzhen Peoples R China Hangzhou Univ Elect Sci & Technol Hangzhou Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Xinjiang Univ Urumqi Xinjiang Peoples R China China Mobile Res Inst Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Peking Univ Beijing Peoples R China City Univ Hong Kong Hong Kong Peoples R China South China Univ Technol Guangzhou Peoples R China Tech Univ Korea Siheung Si South Korea Univ Sindh Sindh Pakistan IKLAB New York NY USA

ISBN: (纸本)9781665487399

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the superresolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https:// ***/RenYanghome/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://***/RenYang-home/NTIRE22_VEnh_SR.

关键词： computer vision Target tracking Codes conferences Superresolution pattern recognition

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Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Image Retrieval

Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Ima...

引用

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

作者： Fuentes, Anibal Saavedra, Jose M. Impresee Inc 600 Calif St San Francisco CA 94108 USA

ISBN: (纸本)9781665448994

Architectures based on siamese networks with triplet loss have shown outstanding performance on the image-based similarity search problem. This approach attempts to discriminate between positive (relevant) and negative (irrelevant) items. However, it undergoes a critical weakness. Given a query, it cannot discriminate weakly relevant items, for instance, items of the same type but different color or texture as the given query, which could be a serious limitation for many real-world search applications. Therefore, in this work, we present a quadruplet-based architecture that overcomes the aforementioned weakness. Moreover, we present an instance of this quadruplet network, which we call Sketch-QNet, to deal with the color sketch-based image retrieval (CSBIR) problem, achieving new state-of-the-art results.

关键词： Training Visualization computer vision Image color analysis conferences Image retrieval computer architecture

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Filter Distribution Templates in Convolutional Networks for Image Classification Tasks

Filter Distribution Templates in Convolutional Networks for ...

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

作者： Izquierdo-Cordova, Ramon Mayol-Cuevas, Walterio Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (纸本)9781665448994

Neural network designers have reached progressive accuracy by increasing models depth, introducing new layer types and discovering new combinations of layers. A common element in many architectures is the distribution of the number of filters in each layer. Neural network models keep a pattern design of increasing filters in deeper layers such as those in LeNet, VGG, ResNet, MobileNet and even in automatic discovered architectures such as NASNet. It remains unknown if this pyramidal distribution of filters is the best for different tasks and constrains. In this work we present a series of modifications in the distribution of filters in three popular neural network models and their effects in accuracy and resource consumption. Results show that by applying this approach, some models improve up to 8.9% in accuracy showing reductions in parameters up to 54%.

关键词： Computational modeling Neural networks Memory management Manuals Data models pattern recognition Convolutional neural networks

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Symmetric Parallax Attention for Stereo Image Super-Resolution

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

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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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Automatic Play Segmentation of Hockey Videos

Automatic Play Segmentation of Hockey Videos

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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

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Classification of Colorectal Cancer Tissues Using Stacking Ensemble Learning 7th

Classification of Colorectal Cancer Tissues Using Stacking E...

引用

7th International conference on Communication, Device and Networking, ICCDN 2024

作者： Das, Abhrodeep Hazra, Animesh Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri735102 India

ISBN: (纸本)9789819764648

Advancement in digital pathology has enabled deep learning-based computer vision techniques for automated diagnosis and prognosis of diseases. The essentiality of early detection and prognosis of any cancer category lies in the fact that it can speed up the subsequent medical treatment procedures of patients. About 10% of all cancer cases worldwide are related to colorectal cancer (CRC), and it is also the third most common category of cancer (Egeblad et al. in Dev Cell 18:884–901, 2010). So, it is clinically important to classify and make an objective evaluation of colorectal cancer histological images. The classification performance of current methodologies primarily relies on the use of various combinations of texture-based features and classifiers or transfer learning to classify different organisational kinds. As a result of the diversity of tissue types and characteristics present in histological images, classification is still a challenging task. In this study, we have put forth a novel and effective stacking (Wolpert in Neural Netw 5:241–59, 1992) ensemble (Zhang and Yunqian (eds) Ensemble machine learning: methods and applications. Springer Science;Business Media, 2012) technique for classification on the histopathological image analysis benchmark dataset Kather-5 K (Kather et al. in Sci Rep 6:27988, 2016). The ensemble consists of two cutting-edge deep learning architectures, ResNet18 (He et al. in Proceedings of the ieee conference on computer vision and pattern recognition 2016, pp 770–778) and EfficientNetB0 (Tan and Le Efficientnet: rethinking model scaling for convolutional neural networks. International conference on machine learning 2019 May 24. PMLR, pp 6105–6114), acting as weak learners, and an ANN acting as the meta-learner. The proposed approach obtained a remarkable accuracy of 97.20% in CRC classification on the said dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Colorectal cancer Convolutional neural network Ensemble learning Residual network Stacking

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Color Me Good: Branding in the Coloring Style of Movie Posters

Color Me Good: Branding in the Coloring Style of Movie Poste...

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

作者： Agrawal, Rishabh Sivaprasad, Sarath Pedanekar, Niranjan TCS Res Tata Consultancy Serv Pune 411013 Maharashtra India

ISBN: (纸本)9781665448994

Brand logos are often rendered in a different style based on a context such as an event promotion. For example, Warner Bros. uses a different variety of their brand logo for different movies for promotion and aesthetic appeal. In this paper, we propose an automated method to render brand logos in the coloring style of branding material such as movie posters. For this, we adopt a photo-realistic neural style transfer method using movie posters as the style source. We propose a color-based image segmentation and matching method to assign style segments to logo segments. Using these, we render the well-known Warner Bros. logo in the coloring style of 141 movie posters. We also present survey results where 287 participants rate the machine-stylized logos for their representativeness and visual appeal.

关键词： Image segmentation Visualization computer vision Image color analysis conferences Semantics Brightness

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Multistage Fusion of Face Matchers

Multistage Fusion of Face Matchers

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

作者： Tulyakov, Sergey Sankaran, Nishant Mohan, Deen Setlur, Srirangaraj Govindaraju, Venu Univ Buffalo Ctr Unified Biometr & Sensors Buffalo NY 14260 USA

ISBN: (纸本)9781665448994

Multistage, or serial, fusion refers to the algorithms sequentially fusing an increased number of matching results at each step and making decisions about accepting or rejecting the match hypothesis, or going to the next step. Such fusion methods are beneficial in the situations where running additional matching algorithms needed for later stages is time consuming or expensive. The construction of multistage fusion methods is challenging, since it requires both learning fusion functions and finding optimal decision thresholds for each stage. In this paper, we propose the use of single neural network for learning the multistage fusion. In addition we discuss the choices for the performance measurements of the trained algorithms and for the selection of network training optimization criteria. We perform the experiments using three face matching algorithms and IJB-A and IJB-C databases.

关键词： Training Measurement computer vision Error analysis Databases Face recognition conferences

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Edge Guided Progressively Generative Image Outpainting

Edge Guided Progressively Generative Image Outpainting

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

作者： Lin, Han Pagnucco, Maurice Song, Yang Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9781665448994

Deep-learning based generative models are proven to be capable for achieving excellent results in numerous image processing tasks with a wide range of applications. One significant improvement of deep-learning approaches compared to traditional approaches is their ability to regenerate semantically coherent images by only relying on an input with limited information. This advantage becomes even more crucial when the input size is only a very minor proportion of the output size. Such image expansion tasks can be more challenging as the missing area may originally contain many semantic features that are critical in judging the quality of an image. In this paper we propose an edge-guided generative network model for producing semantically consistent output from a small image input. Our experiments show the proposed network is able to regenerate high quality images even when some structural features are missing in the input.

关键词： Measurement computer vision Image edge detection Computational modeling Image processing conferences Semantics

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DarkLight Networks for Action recognition in the Dark

DarkLight Networks for Action Recognition in the Dark

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

作者： Chen, Rui Chen, Jiajun Liang, Zixi Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co 101 Waihuan Xi Rd Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781665448994

Human action recognition in the dark is a significant task with various applications, e.g., night surveillance and self-driving at night. However, the lack of video datasets for human actions in the dark hinders its development. Recently, a public dataset ARID has been introduced to stimulate progress for the task of human action recognition in dark videos. Currently, there are multiple models that perform well for action recognition in videos shot under normal illumination. However, research shows that these methods may not be effective in recognizing actions in dark videos. In this paper, we construct a novel neural network architecture: DarkLight Networks, which involves (i) a dual-pathway structure where both dark videos and its brightened counterpart are utilized for effective video representation;and (ii) a self-attention mechanism, which fuses and extracts corresponding and complementary features from the two pathways. Our approach achieves state-of-the-art results on ARID.

关键词： computer vision Fuses Surveillance conferences Neural networks Lighting Feature extraction

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