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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是181-190 订阅

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Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes

Where We Are and What We're Looking At: Query Based Worldwid...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Brandon Clark Alec Kerrigan Parth Parag Kulkarni Vicente Vivanco Cepeda Mubarak Shah Center for Research in Computer Vision University of Central Florida Orlando USA

Determining the exact latitude and longitude that a photo was taken is a useful and widely applicable task, yet it remains exceptionally difficult despite the accelerated progress of other computer vision tasks. Most previous approaches have opted to learn single representations of query images, which are then classified at different levels of geographic granularity. these approaches fail to exploit the different visual cues that give context to different hierarchies, such as the country, state, and city level. To this end, we introduce an end-to-end transformer-based architecture that exploits the relationship between different geographic levels (which we refer to as hierarchies) and the corresponding visual scene information in an image through hierarchical cross-attention. We achieve this by learning a query for each geographic hierarchy and scene type. Furthermore, we learn a separate representation for different environmental scenes, as different scenes in the same location are often defined by completely different visual features. We achieve state of the art accuracy on 4 standard geo-localization datasets : Im2GPS, Im2GPS3k, YFCC4k, and YFCC26k, as well as qualitatively demonstrate how our method learns different representations for different visual hierarchies and scenes, which has not been demonstrated in the previous methods. Above previous testing datasets mostly consist of iconic landmarks or images taken from social media, which makes the dataset a simple memory task, or makes it biased towards certain places. To address this issue we introduce a much harder testing dataset, Google-World-Streets-15k, comprised of images taken from Google Streetview covering the whole planet and present state of the art results. Our code can be found at https://***/AHKerrigan/GeoGuessNet.

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Effective Video Mirror Detection with Inconsistent Motion Cues

Effective Video Mirror Detection with Inconsistent Motion Cu...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Alex Warren Ke Xu Jiaying Lin Gary K.L. Tam Rynson W.H. Lau Department of Computer Science Swansea University Swansea City University of Hong Kong Hong Kong

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Image-based mirror detection has recently undergone rapid research due to its significance in applications such as robotic navigation, semantic segmentation and scene re-construction. Recently, VMD-Net was proposed as the first video mirror detection technique, by modeling dual correspondences between the inside and outside of the mirror both spatially and temporally. However, this approach is not reliable, as correspondences can occur completely inside or outside of the mirrors. In addition, the proposed dataset VMD-D contains many small mirrors, limiting its applicability to real-world scenarios. To address these problems, we developed a more challenging dataset that includes mirrors of various shapes and sizes at different locations of the frames, providing a better reflection of real-world scenarios. Next, we observed that the motions between the inside and outside of the mirror are often in-consistent. For instance, when moving in front of a mirror, the motion inside the mirror is often much smaller than the motion outside due to increased depth perception. With these observations, we propose modeling inconsistent motion cues to detect mirrors, and a new network with two novel modules. the Motion Attention Module (MAM) ex-plicitly models inconsistent motions around mirrors via optical flow, and the Motion-Guided Edge Detection Module (MEDM) uses motions to guide mirror edge feature learning. Experimental results on our proposed dataset show that our method outperforms state-of-the-arts. the code and dataset are available at ht tps: // gi th ub. com/ AlexAnthonyWarren/MG-VMD.

关键词： Representation learning Shape Image edge detection Semantic segmentation Object detection Reflection pattern recognition

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Automated Blue Whale Photo-Identification Using Local Feature Matching 1

引用

26th International conference on pattern recognition, ICPR 2022

作者： Lalonde, Marc Landry, David Sears, Richard Computer Vision Team Computer Research Institute of Montreal MontrealQC Canada Mingan Island Cetacean Study 285 rue Green St. LambertQC Canada

ISBN: (数字)9783031377310

ISBN: (纸本)9783031377303

Automated photo-identification of blue whale individuals has received little attention over the years compared to humpback and right whales, most likely because of the difficulty of the task: blue whales are identified using the low-contrast pigmentation of their skin (light and dark shades of grey displayed in patches of varying sizes). this paper explores the use of three local feature matching techniques (SIFT, L2-Net and LoFTR) for blue whale photo-identification and compares their performance to that of a baseline method (Pose Invariant Embeddings, or PIE) which has been used for humpback whale identification. We conduct experiments using a rich dataset made of 3129 images of 807 unique individuals acquired over a 40-year period. Results show a large difference in performance between all methods, with LoFTR outperforming the other three by a large margin. Measured top-1 and top-10 accuracies (76.7% and 80.3% respectively) suggest that LoFTR is suitable for the robust matching of images of blue whales. © 2023, Springer Nature Switzerland AG.

关键词： Deep learning

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An Image Quality Assessment Dataset for Portraits

An Image Quality Assessment Dataset for Portraits

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conference on computer vision and pattern recognition (cvpr)

作者： Nicolas Chahine Ana-Stefania Calarasanu Davide Garcia-Civiero théo Cayla Sira Ferradans Jean Ponce DXOMARK Département d'informatique de l'Ecole normale supérieure ENS-PSL CNRS Inria Institute of Mathematical Sciences and Center for Data Science New York University

Year after year, the demand for ever-better smartphone photos continues to grow, in particular in the domain of portrait photography. Manufacturers thus use perceptual quality criteria throughout the development of smartphone cameras. this costly procedure can be partially replaced by automated learning-based methods for image quality assessment (IQA). Due to its subjective nature, it is necessary to estimate and guarantee the consistency of the IQA process, a characteristic lacking in the mean opinion scores (MOS) widely used for crowdsourcing IQA. In addition, existing blind IQA (BIQA) datasets pay little attention to the difficulty of cross-content assessment, which may degrade the quality of annotations. this paper introduces PIQ23, a portrait-specific IQA dataset of 5116 images of 50 predefined scenarios acquired by 100 smartphones, covering a high variety of brands, models, and use cases. the dataset includes individuals of various genders and ethnicities who have given explicit and informed consent for their photographs to be used in public research. It is annotated by pairwise comparisons (PWC) collected from over 30 image quality experts for three image attributes: face detail preservation, face target exposure, and overall image quality. An in-depth statistical analysis of these annotations allows us to evaluate their consistency over PIQ23. Finally, we show through an extensive comparison with existing baselines that semantic information (image context) can be used to improve IQA predictions. the dataset along with the proposed statistical analysis and BIQA algorithms are available: https://***/DXOMARK-Research/PIQ2023

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ClinicYA: An Application for Pill Identification Using Deep Learning and K-means Clustering 26

ClinicYA: An Application for Pill Identification Using Deep ...

引用

26th International computer Science and Engineering conference, ICSEC 2022

作者： Srikamdee, Supawadee Suksawatchon, Ureerat Suksawatchon, Jakkarin Werapan, Worawit Burapha University Faculty of Informatics Chonburi20131 Thailand

ISBN: (纸本)9781665491983

Pill Identification is one of the most important tasks to assure medication safety. With a high-quality smartphone camera, we can create a mobile-based application to identify unknown pills automatically. However, most existing studies can fail to detect and identify pills under unconstrained real-world conditions. To overcome the difficulty in identifying pills in practical usage, we present the design, implementation, and evaluation of a mobile-based application called ClinicYA. the development of ClinicYA involves key processes: a pill recognition model based on the Mask-RCNN algorithm that extracts the shape of pills and a color clustering and matching template in the RGB and HSV color model. the proposed application, ClinicYA, achieves over 99.27% accuracy in the localization and recognition of pill shapes. For color detection, our approach achieves 93.85% accuracy in the HSV color model for single color identification and up to 90.5% in the HSV color model for two color identification. © 2022 ieee.

关键词： computer vision

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Snapshot Spectral Imaging for Face Anti-Spoofing: Addressing Data Challenges with Advanced Processing and Training

Snapshot Spectral Imaging for Face Anti-Spoofing: Addressing...

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ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Hui Li Yaowen Xu Zhaofan Zou Zhixiang He China Telecom Artificial Intelligence Technology Co. Ltd Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Although considerable research progress has been made in the field of face anti-spoofing(FAS), it still faces continuous threats from ultra-realistic face mask attacks. Facing the challenge of highly realistic flexible masks, spectral sensors show great potential in enhancing the safety of FAS systems. However, the application of snapshot spectral imaging (SSI) in FAS is still in its infancy and faces two major challenges: data scarcity and data content differences. To this end, we introduce a data processing and model training method for SSI images. In terms of data processing, we use RandomBorderMask technology and RandomDropChannels strategy to avoid misjudgment of material information, reduce interference from redundant information, and learn from RGB image preprocessing methods to enhance data diversity. Regarding model training, we propose a model integration strategy and semi-supervised learning technology, which combines the prediction results of multiple models and uses pseudo labels to expand the training data to solve the over-fitting problem caused by data scarcity. these innovative methods achieved first-class results in the 5th Face Anti-Spoofing Challenge @cvpr2024, verifying their effectiveness in improving the accuracy and robustness of the FAS system.

关键词： Training Accuracy Face recognition Imaging Interference Predictive models Semisupervised learning

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Boosting Spike Camera Image Reconstruction from a Perspective of Dealing with Spike Fluctuations

Boosting Spike Camera Image Reconstruction from a Perspectiv...

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conference on computer vision and pattern recognition (cvpr)

作者： Rui Zhao Ruiqin Xiong Jing Zhao Jian Zhang Xiaopeng Fan Zhaofei Yu Tiejun Huang School of Computer Science Peking University National Key Laboratory for Multimedia Information Processing Peking University School of Electronic and Computer Engineering Peking University School of Computer Science and Technology Harbin Institute of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

As a bio-inspired vision sensor with ultra-high speed, spike cameras exhibit great potential in recording dynamic scenes with high-speed motion or drastic light changes. Different from traditional cameras, each pixel in spike cam-eras records the arrival of photons continuously by firing binary spikes at an ultra-fine temporal granularity. In this process, multiple factors impact the imaging, including the photons' Poisson arrival, thermal noises from circuits, and quantization effects in spike readout. these factors intro-duce fluctuations to spikes, making the recorded spike in-tervals unstable and unable to reflect accurate light intensi-ties. In this paper, we present an approach to deal with spike fluctuations and boost spike camera image reconstruction. We first analyze the quantization effects and reveal the unbi-ased estimation attribute of the reciprocal of differential of spike firing time (DSFT). Based on this, we propose a spike representation module to use DSFT with multiple orders for fluctuation suppression, where DSFT with higher or-ders indicates spike integration duration between multiple spikes. We also propose a module for inter-moment feature alignment at multiple granularities. the coarser alignment is based on patch-level cross-attention with a local search strategy, and the finer alignment is based on deformable convolution at the pixel level. Experimental results demon-strate the effectiveness of our method on both synthetic and real-captured data. the source code and dataset are avail-able at https://***/ruizhao26/BSF.

关键词： Fluctuations Quantization (signal) Firing Estimation vision sensors Cameras Search problems

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MvAV-pix2pixHD: Multi-view Aerial View Image Translation

MvAV-pix2pixHD: Multi-view Aerial View Image Translation

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ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Jun Yu Keda Lu Shenshen Du Lin Xu Peng Chang Houde Liu Bin Lan Tianyu Liu University of Science and Technology of China Ping An Technology Co. Ltd China PAII Inc. Tsinghua Shenzhen International Graduate School Jianghuai Advance Technology Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-modal aerial view image translation involves converting aerial images from one modality to another while preserving basic details and features. these modalities encompass Synthetic Aperture Radar (SAR), Infrared (IR), Visible Light (RGB), Electro-Optical (EO), and other image types. Recently, various methods have been proposed to tackle this task, but the focus tends to be on paired image research, overlooking the discrepancies found in aerial images of the same location captured at different times and angles, termed incomplete matching or multi-view image translation. Consequently, we propose MvAV-pix2pixHD to address this issue. For multi-view data sampling, we propose two methods: random sampling and time-priority sampling. Additionally, within the pix2pixHD framework, we introduce an inverse generator to ensure the basic semantic features of the generated images and incorporate three robust loss functions to constrain the authenticity of the generated images. We conduct extensive experiments on two multi-view image translation tasks in the Multi-modal Aerial View Imagery Challenge: Translation (MAVIC-T). Experimental results demonstrate the superiority of our proposed method, and we achieved second place in the MAVIC-T competition in the 20th ieee Workshop on Perception Beyond the Visible Spectrum of the cvpr 2024.

关键词： computer vision Image resolution conferences Computational modeling Semantics computer architecture Generators

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Cross Initialization for Face Personalization of Text-to-Image Models

Cross Initialization for Face Personalization of Text-to-Ima...

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conference on computer vision and pattern recognition (cvpr)

作者： Lianyu Pang Jian Yin Haoran Xie Qiping Wang Qing Li Xudong Mao Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Lingnan University East China Normal University The Hong Kong Polytechnic University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recently, there has been a surge in face personalization techniques, benefiting from the advanced capabilities of pretrained text-to-image diffusion models. Among these, a notable method is Textual Inversion, which generates personalized images by inverting given images into textual embeddings. However, methods based on Textual Inversion still struggle with balancing the tradeoff between reconstruction quality and editability. In this study, we examine this issue through the lens of initialization. Upon closely examining traditional initialization methods, we identified a significant disparity between the initial and learned embeddings in terms of both scale and orientation. the scale of the learned embedding can be up to 100 times greater than that of the initial embedding. Such a significant change in the embedding could increase the risk of overfitting, thereby compromising the editability. Driven by this observation, we introduce a novel initialization method, termed Cross Initialization, that significantly narrows the gap between the initial and learned embeddings. this method not only improves both reconstruction and editability but also reduces the optimization steps from 5,000 to 320. Furthermore, we apply a regularization term to keep the learned embedding close to the initial embedding. We show that when combined with Cross Initialization, this regularization term can effectively improve editability. We provide comprehensive empirical evidence to demonstrate the superior performance of our method compared to the baseline methods. Notably, in our experiments, Cross Initialization is the only method that successfully edits an individual's facial expression. Additionally, a fast version of our method allows for capturing an input image in roughly 26 seconds, while surpassing the baseline methods in terms of both reconstruction and editability. Code is available at https://***/lyuPang/CrossInitialization.

关键词： computer vision Face recognition Computational modeling Text to image Diffusion models Surges Image reconstruction

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Detecting 3D Texture on Cultural Heritage Artifacts 1

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26th International conference on pattern recognition, ICPR 2022

作者： Ganapathi, Iyyakutti Iyappan Javed, Sajid Hassan, Taimur Werghi, Naoufel Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates C2PS Khalifa University Abu Dhabi127788 United Arab Emirates KUCARS Khalifa University Abu Dhabi127788 United Arab Emirates

ISBN: (数字)9783031377310

ISBN: (纸本)9783031377303

Textures in 3D meshes represent intrinsic surface properties and are essential for numerous applications, such as retrieval, segmentation, and classification. the computer vision approaches commonly used in the cultural heritage domain are retrieval and classification. Mainly, these two approaches consider an input 3D mesh as a whole, derive features of global shape, and use them to classify or retrieve. In contrast, texture classification requires objects to be classified or retrieved based on their textures, not their shapes. Most existing techniques convert 3D meshes to other domains, while only a few are applied directly to 3D mesh. the objective is to develop an algorithm that captures the surface variations induced by textures. this paper proposes an approach for texture classification directly applied to the 3D mesh to classify the surface into texture and non-texture regions. We employ a hybrid method in which classical features describe each facet locally, and these features are then fed into a deep transformer for binary classification. the proposed technique has been validated using SHREC’18 texture patterns, and the results demonstrate the proposed approach’s effectiveness. © 2023, Springer Nature Switzerland AG.

关键词： Textures

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