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

共 52937 条记录，以下是4491-4500 订阅

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Explicit Knowledge Incorporation for Visual Reasoning

Explicit Knowledge Incorporation for Visual Reasoning

引用

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

作者： Zhang, Yifeng Jiang, Ming Zhao, Qi Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781665445092

Existing explainable and explicit visual reasoning methods only perform reasoning based on visual evidence but do not take into account knowledge beyond what is in the visual scene. To addresses the knowledge gap between visual reasoning methods and the semantic complexity of real-world images, we present the first explicit visual reasoning method that incorporates external knowledge and models high-order relational attention for improved generalizability and explainability. Specifically, we propose a knowledge incorporation network that explicitly creates and includes new graph nodes for entities and predicates from external knowledge bases to enrich the semantics of the scene graph used in explicit reasoning. We then create a novel Graph-Relate module to perform high-order relational attention on the enriched scene graph. By explicitly introducing structured external knowledge and high-order relational attention, our method demonstrates significant generalizability and explainability over the state-of-the-art visual reasoning approaches on the GQA and VQAv2 datasets.

关键词： Knowledge engineering Visualization computer vision Semantics Knowledge based systems Cognition pattern recognition

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A Deep Learning Approach to Face Detection and recognition in Group Photos Using OpenCV 5

A Deep Learning Approach to Face Detection and Recognition i...

引用

5th ieee International conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Siddhartha, Mutyala Sai Sri Kowshik, Bobburi Sai Reddy, Mora Venkata Dhanumjaya Palaniswamy, Suja Pati, Peeta Basa Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350305449

Face detection and face recognition are the most important and frequently used operations in image processing and computer vision domains. There are many applications for face recognition, but some of them require accurate performance in real-time with pose, illumination, and occlusion invariant. In this work, a method has been proposed to detect and recognize a person in various backgrounds, clothes and among the crowd. VGGFace and MTCNN are used in this work for the implementation where it compares a person's face with other images for similar features and provides a similarity score. The proposed method resulted in 97% accuracy. A custom-developed Amrita Students Image Database (ASID) has been used in this work. © 2023 ieee.

关键词： Face recognition

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Automatic Play Segmentation of Hockey Videos

Automatic Play Segmentation of Hockey Videos

引用

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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Brain-computer Interface System Based on Common Spatial patterns for Inner Speech recognition from Electroencephalography Signal by Using a Convolutional Neural Networks

Brain-Computer Interface System Based on Common Spatial Patt...

引用

2024 ieee International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Abdalla, Hussna Elnoor M. Al-Haddad, Syed Abdul Rahman Basri, Hamidon Bin Aris, Ishak Bin Yusofv, Abdul Hanif Khan Bin Neyaz, Hisham University Putra Malaysia Malaysia & Sudan International University Sudan University Putra Malaysia Department of Computer and Communication System Engineering Malaysia University Putra Malaysia Faculty of Medicine and Health Sciences Department of Medicine Malaysia University Putra Malaysia Department of Electrical and Electronic Engineering Malaysia University Putra Faculty of Medicine and Health Sciences Department of Medicine Malaysia

ISBN: (纸本)9781665410205

Inner speech recognition is a modern advancement in Brain computer interfaces (BCI) that facilitates a communication between the computer and the brain in a direct way. It is particularly beneficial for individuals who face communication issues due to speech disability. In this research, an end-to-end brain-computer interface system was developed that uses Electroencephalography (EEG) signals to recognize inner speech straight from the brain. The study utilized an open-access dataset presented on four Spanish words from ten participants. The proposed system involved preprocessing to clean and enhance the signal. Common Spatial pattern (CSP) was utilized to extract features to improve the discrimination between the selected four words. A Convolutional Neural Networks (CNNs) deep learning model was used to enhance the inner speech recognition performance from spatial features. The result shows that the model can decode the inner speech with an average accuracy of 81.40% for the un-seeing dataset and 89.80% for the entire dataset, indicating that the proposed method outperforms previous works that used the same dataset. © 2024 ieee.

关键词： Convolutional neural networks

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Multistage Fusion of Face Matchers

Multistage Fusion of Face Matchers

引用

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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A Decomposition Model for Stereo Matching

A Decomposition Model for Stereo Matching

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

作者： Yao, Chengtang Jia, Yunde Di, Huijun Li, Pengxiang Wu, Yuwei Beijing Inst Technol Beijing Lab Intelligent Informat Technol Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost (time and memory cost) as the resolution increases. In order to reduce the huge cost of stereo matching at the original resolution, our model only runs dense matching at a very low resolution and uses sparse matching at different higher resolutions to recover the disparity of lost details scale-by-scale. After the decomposition of stereo matching, our model iteratively fuses the sparse and dense disparity maps from adjacent scales with an occlusion-aware mask. A refinement network is also applied to improving the fusion result. Compared with high-performance methods like PSMNet and GANet, our method achieves 10 - 100 x speed increase while obtaining comparable disparity estimation results.

关键词： computer vision Costs Fuses Computational modeling Face recognition Pipelines Estimation

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Towards Understanding Personality Expression via Body Motion

Towards Understanding Personality Expression via Body Motion

引用

ieee conference on Virtual Reality and 3D User Interfaces (VR)

作者： Sonlu, Sinan Dogan, Yalim Erguzen, Arcin Ulku Unalan, Musa Ege Demirci, Serkan Durupinar, Funda Gudukbay, Ugur Bilkent Univ Bilkent Turkiye Univ Massachusetts Boston MA USA

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374490;9798350374506

This work addresses the challenge of data scarcity in personality-labeled datasets by introducing personality labels to clips from two open datasets, ZeroEGGS and Bandai, which provide diverse fullbody animations. To this end, we present a user study to annotate short clips from both sets with labels based on the Five-Factor Model (FFM) of personality. We chose features informed by Laban Movement Analysis (LMA) to represent each animation. These features then guided us to select the samples of distinct motion styles to be included in the user study, obtaining high personality variance and keeping the study duration and cost viable. Using the labeled data, we then ran a correlation analysis to find features that indicate high correlation with each personality dimension. Our regression analysis results indicate that highly correlated features are promising in accurate personality estimation. We share our early findings, code, and data publicly.

关键词： Computing methodologies Artificial intelligence computer vision Activity recognition and understanding Computing methodologies computer graphics Animation Motion processing

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Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency

Self-Supervised Video GANs: Learning for Appearance Consiste...

引用

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

作者： Hyun, Sangeek Kim, Jihwan Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea

ISBN: (纸本)9781665445092

A video can be represented by the composition of appearance and motion. Appearance (or content) expresses the information invariant throughout time, and motion describes the time-variant movement. Here, we propose self-supervised approaches for video Generative Adversarial Networks (GANs) to achieve the appearance consistency and motion coherency in videos. Specifically, the dual discriminators for image and video individually learn to solve their own pretext tasks;appearance contrastive learning and temporal structure puzzle. The proposed tasks enable the discriminators to learn representations of appearance and temporal context, and force the generator to synthesize videos with consistent appearance and natural flow of motions. Extensive experiments in facial expression and human action public benchmarks show that our method outperforms the state-of-the-art video GANs. Moreover, consistent improvements regardless of the architecture of video GANs confirm that our framework is generic.

关键词： computer vision Force computer architecture Benchmark testing Generative adversarial networks Generators pattern recognition

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Face recognition Attendance Management System Using Python 1

Face Recognition Attendance Management System Using Python

引用

1st ieee International conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Tiwari, Amit Kr. Dwivedi, Ashish Dubey, Arpit Kushwaha, Ashutosh Dubey, Devesh Kumar Tiwari, Harsh United Institute of Technology Computer Science and Engineering Prayagraj India United Institute of Technology Information Technology Prayagraj India

ISBN: (纸本)9798350356816

Facial recognition systems are incredibly important in today's digital world, spanning various industries. It has several benefits and Plays a crucial part in identifying individuals, authentication, and security. Face recognition is a frequently used technology since it is non-invasive and contactless, even if its accuracy is not as high as that of fingerprint and iris recognition. One noteworthy application is to replace the laborious and timeconsuming manual processes that are prone to proxy attendance difficulties in school, college, and office attendance marking [1]. The four stages of the proposed system are database generation, face identification using the Local Binary pattern Histogram technique, face detection using the Haar-Cascade classifier, and attendance updating. Student photos are used to create the database, while live streaming video in the classroom is used to capture real-time facial. © 2024 ieee.

关键词： Video streaming

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MASKSTR: GUIDE SCENE TEXT recognition MODELS WITH MASKING 49

MASKSTR: GUIDE SCENE TEXT RECOGNITION MODELS WITH MASKING

引用

49th ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wei, Baole He, Minghang Gao, Liangcai Zhou, Duoyou Bai, Xiang Tang, Zhi Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China ByteDance Inc Beijing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Text recognition in information loss scenarios like blurriness, occlusion, and perspective distortion is challenging in real-world applications. To enhance robustness, some studies use extra unlabeled data for encoder pretraining. Others focus on improving decoder context reasoning. However, pretraining methods require abundant unlabeled data and high computing resources, while decoder-based approaches risk over-correction. In this paper, we propose MaskSTR, a dual-branch training framework for STR models, using patch masking to simulate information loss. MaskSTR guides visual representation learning, improving robustness to information loss conditions without extra data or training stages. Furthermore, we introduce Block Masking, a novel and straightforward mask generation method, for further performance enhancement. Experiments demonstrate MaskSTR's effectiveness across CTC, attention, and Transformer decoding methods, achieving significant performance gains and setting new state-of-the-art results.

关键词： Scene text recognition OCR computer vision

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