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Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning

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arXiv 2023年

作者： Cao, Cong Yue, Huanjing Liu, Xin Yang, Jingyu School of Electrical and Information Engineering Tianjin University China Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland

Capturing high dynamic range (HDR) images (videos) is attractive because it can reveal the details in both dark and bright regions. Since the mainstream screens only support low dynamic range (LDR) content, tone mapping algorithm is required to compress the dynamic range of HDR images (videos). Although image tone mapping has been widely explored, video tone mapping is lagging behind, especially for the deep-learning-based methods, due to the lack of HDR-LDR video pairs. In this work, we propose a unified framework (IVTMNet) for unsupervised image and video tone mapping. To improve unsupervised training, we propose domain and instance based contrastive learning loss. Instead of using a universal feature extractor, such as VGG to extract the features for similarity measurement, we propose a novel latent code, which is an aggregation of the brightness and contrast of extracted features, to measure the similarity of different pairs. We totally construct two negative pairs and three positive pairs to constrain the latent codes of tone mapped results. For the network structure, we propose a spatial-feature-enhanced (SFE) module to enable information exchange and transformation of nonlocal regions. For video tone mapping, we propose a temporal-feature-replaced (TFR) module to efficiently utilize the temporal correlation and improve the temporal consistency of video tone-mapped results. We construct a large-scale unpaired HDR-LDR video dataset to facilitate the unsupervised training process for video tone mapping. Experimental results demonstrate that our method outperforms state-of-the-art image and video tone mapping methods. Our code and dataset are available at https://***/caocong/UnCLTMO. Copyright © 2023, The Authors. All rights reserved.

关键词： Conformal mapping

computer Vision Approaches for Automated Bee Counting Application

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arXiv 2024年

作者： Bilik, Simon Janakova, Ilona Ligocki, Adam Ficek, Dominik Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland

关键词： Object detection

From recognition to Prediction: Leveraging Sequence Reasoning for Action Anticipation

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arXiv 2024年

作者： Liu, Xin Hao, Chao Yu, Zitong Yue, Huanjing Yang, Jingyu School of Electrical and Information Engineering Tianjin University China Computer Vision and Pattern Recognition Laboratory School of Engineering Sciences Lappeenranta-Lahti University of Technology LUT Finland School of Computing and Information Technology Great Bay University China

The action anticipation task refers to predicting what action will happen based on observed videos, which requires the model to have a strong ability to summarize the present and then reason about the future. Experience and common sense suggest that there is a significant correlation between different actions, which provides valuable prior knowledge for the action anticipation task. However, previous methods have not effectively modeled this underlying statistical relationship. To address this issue, we propose a novel end-to-end video modeling architecture that utilizes attention mechanisms, named Anticipation via recognition and Reasoning (ARR). ARR decomposes the action anticipation task into action recognition and sequence reasoning tasks, and effectively learns the statistical relationship between actions by next action prediction (NAP). In comparison to existing temporal aggregation strategies, ARR is able to extract more effective features from observable videos to make more reasonable predictions. In addition, to address the challenge of relationship modeling that requires extensive training data, we propose an innovative approach for the unsupervised pre-training of the decoder, which leverages the inherent temporal dynamics of video to enhance the reasoning capabilities of the network. Extensive experiments on the Epic-kitchen-100, EGTEA Gaze+, and 50salads datasets demonstrate the efficacy of the proposed methods. The code is available at https://***/linuxsino/ARR. Copyright © 2024, The Authors. All rights reserved.

关键词： Video analysis

Pose focus transformer meet inter-part relation

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Expert Systems with Applications 2024年 240卷

作者： Luo, Yanmin Lin, Hongwei Huang, Wenlin Wang, Youjie Du, Jixiang Guo, Jing-Ming College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China Maynooth International Engineering College Fuzhou University Fuzhou350108 China Department of Electrical Engineering National Taiwan University of Science and Technology Taipei10607 China

Human pose estimation in crowded scenes is a challenging task. Due to overlap and occlusion, it is difficult to infer pose clues from individual keypoints. We proposed PFFormer, a new transformer-based approach that treats pose estimation as a hierarchical set prediction problem that first focuses on human windows and coarsely predicts whole-body poses globally within them. In PFFormer, we designed a Windows Clustering Transformer (WCT), which reorganizes the image windows by filtering the attentive windows and fusing the inattentive ones, allowing the transformer to focus on the important regions while reducing the interference from the complex background, followed by compensating for the loss of information with a global transformer. Then we partition the learned body pose into a set of structural parts and perform the Inter-Part Relation Module (IPRM) to capture the correlation between multiple parts. These full-body poses and component features are refined at a finer level through the Part-to-Joint Decoder (PJD). Extensive experiments show that PFFormer performs favorably against its counterpart on challenging datasets, including COCO2017, CrowdPose, and OChuman datasets. The performance of crowded scenes, in particular, demonstrates the robustness of the proposed methods to deal with occlusion. © 2023 Elsevier Ltd

关键词： Information filtering

IEEE T-BIOM Editorial Board Changes

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IEEE Transactions on Biometrics, Behavior, and Identity Scie...

IEEE Transactions on biometrics, Behavior, and Identity Science 2024年第1期7卷 1-2页

作者： Naser Damer Weihong Deng Jianjiang Feng Vishal M. Patel Ajita Rattani Mark Nixon Smart Living & Biometric Technologies Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Department of Computer Science and Engineering College of Engineering University of North Texas Denton TX USA

CdCLR: Clip-Driven Contrastive Learning for Skeleton-Based Action recognition

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CdCLR: Clip-Driven Contrastive Learning for Skeleton-Based A...

IEEE Visual Communications and Image Processing (VCIP)

作者： Rong Gao Xin Liu Jingyu Yang Huanjing Yue School of Electrical and Information Engineering Tianjin University China Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Finland

ISBN: (纸本)9781665475938

In this study, we propose a Clip-Driven Contrastive Learning for Skeleton-Based Action recognition (CdCLR). In-stead of considering sequences as instances, CdCLR extracts clips from the sequences as new instances. Aim to implement inherent supervision-guided contrastive learning through joint optimal training of sequences discrimination, clips discrimination, and order verification. Mining abundant positive/negative pairs inside sequence while learning inter-and intra-sequence semantic repre-sentations. Extensive experiments on the NTU RGB+D 60, UCLA and iMiGUE datasets present that CdCLR exhibits superior performance under various evaluation protocols and reaches state-of-the-art. Our code is available at https://***/Erich-G/CdCLRI.

关键词： Training Protocols Image recognition Codes Visual communication Semantics

Deep Learning in Palmprint recognition-A Comprehensive Survey

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： biometrics

rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement

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arXiv 2023年

作者： Liu, Xin Zhang, Yuting Yu, Zitong Lu, Hao Yue, Huanjing Yang, Jingyu School of Electrical and Information Engineering Tianjin University Tianjin300072 China Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta53850 Finland Great Bay University Dongguan523000 China Guangzhou511453 China

Remote photoplethysmography (rPPG) is an important technique for perceiving human vital signs, which has received extensive attention. For a long time, researchers have focused on supervised methods that rely on large amounts of labeled data. These methods are limited by the requirement for large amounts of data and the difficulty of acquiring ground truth physiological signals. To address these issues, several self-supervised methods based on contrastive learning have been proposed. However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior in physiological signals and seem to have a limited ability to cope with noisy. In this paper, a linear self-supervised reconstruction task was designed for extracting the inherent self-similar prior in physiological signals. Besides, a specific noise-insensitive strategy was explored for reducing the interference of motion and illumination. The proposed framework in this paper, namely rPPG-MAE, demonstrates excellent performance even on the challenging VIPL-HR dataset. We also evaluate the proposed method on two public datasets, namely PURE and UBFC-rPPG. The results show that our method not only outperforms existing self-supervised methods but also exceeds the state-of-the-art (SOTA) supervised methods. One important observation is that the quality of the dataset seems more important than the size in self-supervised pre-training of rPPG. The source code is released at https://***/linuxsino/rPPG-MAE. Copyright © 2023, The Authors. All rights reserved.

关键词： Supervised learning

Multi-Unit Floor Plan recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

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arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation