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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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StyleCineGAN: Landscape Cinemagraph Generation Using a Pre-trained StyleGAN

StyleCineGAN: Landscape Cinemagraph Generation Using a Pre-t...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jongwoo Choi Kwanggyoon Seo Amirsaman Ashtari Junyong Noh Visual Media Lab KAIST

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent un-conditional video generation, we leverage a powerful pre-trained image generator to synthesize high-quality cinema-graphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN, our approach utilizes its deep feature space for both GAN inversion and cin-emagraph generation. Specifically, we propose multi-scale deep feature warping (MSDFW), which warps the intermediate features of a pre-trained StyleGAN at different resolutions. by using MSDFW, the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

关键词： Training computer vision Semantics Animation Generators pattern recognition Spatial resolution

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Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation

Curriculum Graph Co-Teaching for Multi-Target Domain Adaptat...

引用

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

作者： Roy, Subhankar Krivosheev, Evgeny Zhong, Zhun Sebe, Nicu Ricci, Elisa Univ Trento Trento TN Italy Fdn Bruno Kessler Povo TN Italy

ISBN: (纸本)9781665445092

In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains. We identify two key aspects that can help to alleviate multiple domain-shifts in the MTDA: feature aggregation and curriculum learning. To this end, we propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier head, with one of them being a graph convolutional network (GCN) which aggregates features from similar samples across the domains. To prevent the classifiers from over-fitting on its own noisy pseudo-labels we develop a co-teaching strategy with the dual classifier head that is assisted by curriculum learning to obtain more reliable pseudo-labels. Furthermore, when the domain labels are available, we propose Domain-aware Curriculum Learning (DCL), a sequential adaptation strategy that first adapts on the easier target domains, followed by the harder ones. We experimentally demonstrate the effectiveness of our proposed frameworks on several benchmarks and advance the state-of-the-art in the MTDA by large margins (e.g. +5.6% on the DomainNet).

关键词： Deep learning computer vision computer network reliability PROM Collaboration pattern recognition Reliability

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Accelerating Neural Field Training via Soft Mining

Accelerating Neural Field Training via Soft Mining

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shakiba Kheradmand Daniel Rebain Gopal Sharma Hossam Isack Abhishek Kar Andrea Tagliasacchi Kwang Moo Yi University of British Columbia Google Research Google DeepMind Simon Fraser University University of Toronto

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present an approach to accelerate Neural Field training by efficiently selecting sampling locations. While Neural Fields have recently become popular, it is often trained by uniformly sampling the training domain, or through handcrafted heuristics. We show that improved convergence and final training quality can be achieved by a soft mining technique based on importance sampling: rather than either considering or ignoring a pixel completely, we weigh the corresponding loss by a scalar. To implement our idea we use Langevin Monte-Carlo sampling. We show that by doing so, regions with higher error are being selected more frequently, leading to more than 2x improvement in convergence speed. The code and related resources for this study are publicly available at project page.

关键词： Training computer vision Monte Carlo methods Codes Fitting Neural radiance field pattern recognition

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Partition-Guided GANs

Partition-Guided GANs

引用

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

作者： Armandpour, Mohammadreza Sadeghian, Ali Li, Chunyuan Zhou, Mingyuan Texas A&M Univ College Stn TX 77843 USA Univ Florida Gainesville FL 32611 USA Microsoft Res Redmond WA USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Despite the success of Generative Adversarial Networks (GANs), their training suffers from several well-known problems, including mode collapse and difficulties learning a disconnected set of manifolds. In this paper, we break down the challenging task of learning complex high dimensional distributions, supporting diverse data samples, to simpler sub-tasks. Our solution relies on designing a partitioner that breaks the space into smaller regions, each having a simpler distribution, and training a different generator for each partition. This is done in an unsupervised manner without requiring any labels. We formulate two desired criteria for the space partitioner that aid the training of our mixture of generators: 1) to produce connected partitions and 2) provide a proxy of distance between partitions and data samples, along with a direction for reducing that distance. These criteria are developed to avoid producing samples from places with non-existent data density, and also facilitate training by providing additional direction to the generators. We develop theoretical constraints for a space partitioner to satisfy the above criteria. Guided by our theoretical analysis, we design an effective neural architecture for the space partitioner that empirically assures these conditions. Experimental results on various standard benchmarks show that the proposed unsupervised model outperforms several recent methods.

关键词： Training Manifolds computer vision computer architecture Generative adversarial networks Generators pattern recognition

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Development and Analysis of computer vision Based SSL Detection Technology for Marine Environment

Development and Analysis of Computer Vision Based SSL Detect...

引用

Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC), Asia-Pacific conference on

作者： Yuxiang Cui Dalian University of Technology Dalian China

ISBN: (数字)9798350393682

ISBN: (纸本)9798350393699

computer vision has been widely used in the field of navigation safety, including ship identification, course prediction, and other applications, as a result of the fast development of pattern recognition and intelligent information processing technology. This has been made possible by the widespread adoption of computer vision technology. The sea sky line (SSL) is a linear interval that differentiates the area of the sky from the surface of the water. It is also an important reference in the perception of the marine environment, which enables vessels that are equipped with visual perception equipment to use visual technology perception equipment to obtain information about the environment around them. When contrasted with the environment of land, the environment of the sea surface is characterized by sudden shifts in the weather and the presence of noticeable waves. Consequently, there are still certain challenges involved in the process of detecting the SSL using computer vision technology. In this study, a comprehensive analysis and summary of the sea-sky line detection algorithms used by several modal cameras is presented.

关键词： computer vision Sea surface Visualization Surface waves Social networking (online) Safety pattern recognition

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Graph-based High-Order Relation Discovery for Fine-grained recognition

Graph-based High-Order Relation Discovery for Fine-grained R...

引用

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

作者： Zhao, Yifan Yan, Ke Huang, Feiyue Li, Jia Beihang Univ State Key Lab Virtual Real Technol & Syst SCSE Beijing Peoples R China Tencent Youtu Lab Shanghai Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Fine-grained object recognition aims to learn effective features that can identify the subtle differences between visually similar objects. Most of the existing works tend to amplify discriminative part regions with attention mechanisms. Besides its unstable performance under complex backgrounds, the intrinsic interrelationship between different semantic features is less explored. Toward this end, we propose an effective graph-based relation discovery approach to build a contextual understanding of high-order relationships. In our approach, a high-dimensional feature bank is first formed and jointly regularized with semantic- and positional-aware high-order constraints, endowing rich attributes to feature representations. Second, to overcome the high-dimension curse, we propose a graph-based semantic grouping strategy to embed this high-order tensor bank into a low-dimensional space. Meanwhile, a group-wise learning strategy is proposed to regularize the features focusing on the cluster embedding center. With the collaborative learning of three modules, our module is able to grasp the stronger contextual details of fine-grained objects. Experimental evidence demonstrates our approach achieves new state-of-the-art on 4 widely-used fine-grained object recognition benchmarks.

关键词： Training Manifolds computer vision Tensors Semantics Focusing Collaborative work

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Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation

Adapt Before Comparison: A New Perspective on Cross-Domain F...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jonas Herzog Zhejiang University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Few-shot segmentation performance declines substantially when facing images from a domain different than the training domain, effectively limiting real-world use cases. To alleviate this, recently cross-domain few-shot segmentation (CD-FSS) has emerged. Works that address this task mainly attempted to learn segmentation on a source domain in a manner that generalizes across domains. Surprisingly, we can outperform these approaches while eliminating the training stage and removing their main segmentation network. We show test-time task-adaption is the key for successful CD-FSS instead. Task-adaption is achieved by appending small networks to the feature pyramid of a conventionally classification-pretrained backbone. To avoid overfitting to the few labeled samples in supervised fine-tuning, consistency across augmented views of input images serves as guidance while learning the parameters of the attached layers. Despite our self-restriction not to use any images other than the few labeled samples at test time, we achieve new state-of-the-art performance in CD-FSS, evidencing the need to rethink approaches for the task. Code is available at https://***/vision-Kek/ABCDFSS.

关键词： Training Image segmentation computer vision Adaptation models Limiting Codes pattern recognition

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Determining Dendrometry Using Drone Scouting, Convolutional Neural Networks and Point Clouds

Determining Dendrometry Using Drone Scouting, Convolutional ...

引用

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

作者： Jensen, Kim Krogh, Oskar Kondrup Jorgensen, Marius Willemoes Lehotsky, Daniel Andersen, Anton Bock Porqueras, Ernest Sondergaard, Jens Aksel S. Gade, Rikke Aalborg Univ Dept Elect Syst Aalborg Denmark TeeJet Technol Aabybro Denmark Aalborg Univ Sect Media Technol Aalborg Denmark

ISBN: (纸本)9781665448994

This paper presents a solution for mapping the location of trees in an orchard and estimating the dendrometric data of the trees. The combined solution consists of a mapping and navigation algorithm, which allows for autonomous data collection at an orchard with a regular rectangular layout, and data processing for tree detection and dendrometric data estimation. The data collection is done using an Intel RealSense D435i camera, which can obtain both RGB and depth data. The paper presents a comparison between the performance of point cloud processing (PCP) and convolutional neural networks (CNNs) on RGB data for tree detection and dendrometric data estimation. The YOLOv3 CNN achieved a mAP50 of 63.53% with 65.5 FPS and a mean error of 20.6 cm in height estimation. Point cloud processing achieved a precision of 76.72% with 2.1 FPS and a mean error of 20.4 cm in height estimation. In conclusion, this work shows that point cloud processing shows comparable results to convolutional neural networks for height estimation, but trades off processing time for better precision in detection.

关键词： Three-dimensional displays Navigation Shape Estimation Vegetation Data collection pattern recognition

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Multi-modal Aerial View Image Challenge: SAR Classification

Multi-modal Aerial View Image Challenge: SAR Classification

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Spencer Low Oliver Nina Dylan Bowald Angel D. Sappa Nathan Inkawhich Peter Bruns Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH Ecuador Computer Vision Center ESPOL Polytechnic University Spain Air Force Research Laboratory Rome NY University of Utah Salt Lake UT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This manuscript delineates the outcomes of the fourth Multi-modal Aerial View Image Challenge - Classification (MAVIC-C). The challenge is aimed at advancing the development of recognition models that leverage Synthetic Aperture Radar (SAR) and Electro-Optical (EO) imagery. Encouraging the integration of data from these two distinct modalities, the challenge seeks to foster the creation of multi-modal approaches that complement characteristics of SAR and EO information. Building upon the precedents set in previous years, the 2021 MAVOC challenge validated the potential of integrating SAR and EO modalities. The subsequent 2022 and 2023 challenges further explored the capabilities of multi-modal frameworks. In its latest iteration, the 2024 challenge presents an enhanced UNIfied COincident Optical and Radar for recognition (UNICORN) dataset alongside a revised competition format, focused on the task of SAR classification. The 2024 challenge evaluates model robustness through out-of-distribution measures, alongside traditional accuracy metrics. The core of this paper is devoted to analyzing the methodologies of the top-performing entries and their performance metrics on a blind test set.

关键词： Measurement Laser radar Image recognition Pipelines Optical imaging Robustness pattern recognition

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Wildfires Detection and Segmentation Using Deep CNNs and vision Transformers 1

引用

26th International conference on pattern recognition, ICPR 2022

作者： Ghali, Rafik Akhloufi, Moulay A. Department of Computer Science Université de Moncton MonctonNB Canada

ISBN: (数字)9783031377426

ISBN: (纸本)9783031377419

Wildfires are an important natural risk which causes enormous damage to the environment. Many researchers are working to improve firefighting using AI. Various vision-based fire detection methods have been proposed to detect fire. However, these techniques are still limited when it comes to identifying the precise fire’s shape as well as small fire areas. For such, we propose deep wildland fire detection and segmentation models based on deep Convolutional Neural Networks (CNNs) and vision Transformers. A novel deep ensemble learning method, which combines EfficientNet-B5 and DenseNet-201 models, is proposed to identify and classify wildfires on aerial images. vision Transformers (TransUNet, MedT, and TransFire) are adopted in segmenting fire pixels and in detecting the precise shape of the fire areas using aerial and ground images. The achieved results are promising and show the potential of using deep CNNs and vision Transformers for forest fire detection and segmentation. © 2023, Springer Nature Switzerland AG.

关键词： Fires

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