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Attention! Stay Focus!

Attention! Stay Focus!

引用

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

作者： Vo, Tu BridgeAI Inc Seoul South Korea

ISBN: (纸本)9781665448994

We develop a deep convolutional neural networks (CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images. Specifically, we develop a double attention network which consists of attentional encoders, triple locals and global local modules to effectively extract useful information from each image in the dual-pixels and select the useful information from each image and synthesize the final output image. We demonstrate the effectiveness of the proposed deblurring algorithm in terms of both qualitative and quantitative aspects by evaluating on the test set in the NTIRE 2021 Defocus Deblurring using Dual-pixel Images Challenge [1] [4].

关键词： Deep learning computer vision conferences computer architecture Feature extraction Cameras Decoding

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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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Estimating (and fixing) the Effect of Face Obfuscation in Video recognition

Estimating (and <i>fixing</i>) the Effect of Face Obfuscatio...

引用

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

作者： Tomei, Matteo Baraldi, Lorenzo Bronzin, Simone Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy Metaliquid Srl Milan Italy

ISBN: (纸本)9781665448994

Recent research has shown that faces can be obfuscated in large-scale datasets with a minimal performance impact on image classification and downstream tasks like object recognition. In this paper, we investigate the role of face obfuscation in video classification datasets and quantify a more significant reduction in performance caused by face blurring. To reduce such performance effects, we propose a generalized distillation approach in which a privacy-preserving action recognition network is trained with privileged information given by face identities. We show, through experiments performed on Kinetics-400, that the proposed approach can fully close the performance gap caused by face anonymization.

关键词： Visualization computer vision Face recognition conferences Computational modeling Pipelines Object recognition

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Delta Sampling R-BERT for limited data and low-light action recognition

Delta Sampling R-BERT for limited data and low-light action ...

引用

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

作者： Hira, Sanchit Das, Ritwik Modi, Abhinav Pakhomov, Daniil Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

We present an approach to perform supervised action recognition in the dark. In this work, we present our results on the ARID dataset[60]. Most previous works only evaluate performance on large, well illuminated datasets like Kinetics and HMDB51. We demonstrate that our work is able to achieve a very low error rate while being trained on a much smaller dataset of dark videos. We also explore a variety of training and inference strategies including domain transfer methodologies and also propose a simple but useful frame selection strategy. Our empirical results demonstrate that we beat previously published baseline models by 11%.

关键词： Training Deep learning Technological innovation Schedules computer vision conferences pattern recognition

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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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OTST: A Two-Phase Framework for Joint Denoising and Remosaicing in RGBW CFA

OTST: A Two-Phase Framework for Joint Denoising and Remosaic...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Fan, Zhihao Wu, Xun Meng, Fanqing Wu, Yaqi Zhang, Feng Tetras.AI University of Shanghai for Science and Technology China Tsinghua University China Tongji University China Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798350302493

RGBW, a newly emerged type of Color Filter Array (CFA), possesses strong low-light photography capabilities. RGBW CFA shows significant application value when low-light sensitivity is critical, such as in security cameras and smartphones. However, the majority of commercial image signal processors (ISP) are primarily designed for Bayer CFA, research pertaining to RGBW CFA is very rare. To address above limitations, in this study, we propose a two-phase framework named OTST for the RGBW Joint Denoising and Remosaicing (RGBW-JRD) task. For the denoising stage, we propose Omni-dimensional Dynamic Convolution based Half-Shuffle Transformer (ODC-HST) which can fully utilize image's long-range dependencies to dynamically remove the noise. For the remosaicing stage, we propose a Spatial Compressive Transformer (SCT) to efficiently capture both local and global dependencies across spatial and channel dimensions. Experimental results demonstrate that our two-phase RGBW-JRD framework outperforms existing RGBW denoising and remosaicing solutions across a wide range of noise levels. In addition, the proposed approach ranks the 2nd place in MIPI 2023 RGBW Joint Remosaic and Denoise competition. © 2023 ieee.

关键词： computer vision

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Contrastive Learning for Natural Language-Based Vehicle Retrieval

Contrastive Learning for Natural Language-Based Vehicle Retr...

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

作者： Tam Minh Nguyen Quang Huu Pham Linh Bao Doan Hoang Viet Trinh Viet-Anh Nguyen Viet-Hoang Phan Sun Asterisk Inc R&D Lab AI Res Team Tokyo Japan Hanoi Univ Sci & Technol Hanoi Vietnam

ISBN: (纸本)9781665448994

AI City Challenge 2021 Task 5: The Natural Language-Based Vehicle Tracking is a Natural Language-based Vehicle Retrieval task, which requires retrieving a single-camera track using a set of three natural language descriptions of the specific targets. In this paper, we present our methods to tackle the difficulties of the provided task. Experiments with our approaches on the competitive dataset from AICity Challenge 2021 show that our techniques achieve Mean Reciprocal Rank score of 0.1701 on the public test dataset and 0.1571 on the private test dataset.

关键词： computer vision Target tracking conferences Urban areas Natural languages computer architecture pattern recognition

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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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Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference

Neural Architecture Search of Deep Priors: Towards Continual...

引用

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

作者： Mundt, Martin Pliushch, Iuliia Ramesh, Visvanathan Goethe Univ Frankfurt Germany

ISBN: (纸本)9781665448994

In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstrate that it is possible to find random weight architectures, a deep prior, that enables a linear classification to perform on par with fully trained deep counterparts. Through ablation experiments, we exclude the possibility of winning a weight initialization lottery and confirm that suitable deep priors do not require additional inference. In an extension to continual learning, we investigate the possibility of catastrophic interference free incremental learning. Under the assumption of classes originating from the same data distribution, a deep prior found on only a subset of classes is shown to allow discrimination of further classes through training of a simple linear classifier.

关键词： Training computer vision conferences Neural networks computer architecture Interference pattern recognition

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