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

共 52944 条记录，以下是4501-4510 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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Manipulation Detection in Satellite Images Using vision Transformer

Manipulation Detection in Satellite Images Using Vision Tran...

引用

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

作者： Horvath, Janos Baireddy, Sriram Hao, Hanxiang Montserrat, Daniel Mas Delp, Edward J. Purdue Univ Sch Elect & Comp Engn Video & Image Proc Lab VIPER W Lafayette IN 47907 USA

ISBN: (纸本)9781665448994

A growing number of commercial satellite companies provide easily accessible satellite imagery. Overhead imagery is used by numerous industries including agriculture, forestry, natural disaster analysis, and meteorology. Satellite images, just as any other images, can be tampered with image manipulation tools. Manipulation detection methods created for images captured by "consumer cameras" tend to fail when used on satellite images due to the differences in image sensors, image acquisition, and processing. In this paper we propose an unsupervised technique that uses a vision Transformer to detect spliced areas within satellite images. We introduce a new dataset which includes manipulated satellite images that contain spliced objects. We show that our proposed approach performs better than existing unsupervised splicing detection techniques.

关键词： Image sensors computer vision Satellites Splicing conferences Forestry Tools

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Symmetric Parallax Attention for Stereo Image Super-Resolution

Symmetric Parallax Attention for Stereo Image Super-Resoluti...

引用

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

作者： Wang, Yingqian Ying, Xinyi Wang, Longguang Yang, Jungang An, Wei Guo, Yulan Natl Univ Def Technol Zunyi Guizhou Peoples R China

ISBN: (纸本)9781665448994

Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used. Since stereo images are highly symmetric under epipolar constraint, in this paper, we improve the performance of stereo image SR by exploiting symmetry cues in stereo image pairs. Specifically, we propose a symmetric bi-directional parallax attention module (biPAM) and an inline occlusion handling scheme to effectively interact cross-view information. Then, we design a Siamese network equipped with a biPAM to super-resolve both sides of views in a highly symmetric manner. Finally, we design several illuminance-robust losses to enhance stereo consistency. Experiments on four public datasets demonstrate the superior performance of our method.

关键词： Training computer vision conferences Superresolution Estimation Bidirectional control Robustness

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Deep Learning Approaches for Accurate Sentiment Analysis of Online Consumer Feedback

Deep Learning Approaches for Accurate Sentiment Analysis of ...

引用

2023 International conference on computer Communication and Informatics, ICCCI 2023

作者： Ganesan, Swathi Somasiri, Nalinda Colombage, Chandima York St. John University Department of Computer Science LondonEC1A 4JT United Kingdom

ISBN: (纸本)9798350348217

Over the earlier time, a category of machine learning, called deep learning, has attained significant achievements in several computer vision tasks such as image classification, object detection, semantic segmentation, pattern recognition and image classification generation. Deep learning objectives at finding various levels of dispersed representations, which have been proven to be discriminatively effective in many tasks. Distributed statement depicts similar information highlights across different adaptable and reliant layers. Each layer characterizes the data with a similar degree of exactness, however adapted to the degree of scale. The implementation of deep learning techniques depends greatly on the variety of data interpretation (or features) on which they are used. Artificial intelligence plans to understand interpretations of information regularly by changing over it or isolating components as of it, which creates it simpler to play out an undertaking like order or extrapolation. © 2023 ieee.

关键词： Image classification

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A Decomposition Model for Stereo Matching

A Decomposition Model for Stereo Matching

引用

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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SRNF-PAR: Strawberry and Recurrent Neural Framework-based Physiotherapy Activity recognition 11

SRNF-PAR: Strawberry and Recurrent Neural Framework-based Ph...

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11th International conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Deotale, Disha Verma, Madhushi Suresh, P. Bharadwaj, Arpit Cse Mit School of Computing Mit Art Design and Technology University Pune India School of Computer Science Enginering and Technology Bennett University Greater Noida India Cidirie Muthoot Institute of Technology and Science Kochi India

ISBN: (纸本)9798350350357

Person activity recognition is now a research area in computer vision. The various applications of this area include healthcare, sports and suspicious activity detection. This study focused on physiotherapy exercise activity. The video dataset of physiotherapy is limited;therefore, it was collected from YouTube for shoulder therapy. The exercise has many activities thing is available to understand it all, here more than one activity in sequence wants to do with some activity repetition. Therefore, the deep learning-based algorithm such as in recurrent neural networks the long short-term memory technique will help to determine the exercise with the help of sub-activity order and the other parameter will identify which activity is repeated and the duration of exercise activity with the pose of the person to perform the activity such as static, dynamic and transition. All parameters were obtained using the novel approach of a strawberry-based recurrent neural network for physiotherapy activity recognition (SBRNN-PAR). This algorithm will classify the sub-activity, recognize the exercise with repeated counts and identify the human pose to perform the activity. The idea for a model shows a higher performance in evaluation. © 2024 ieee.

关键词： computer vision

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Enhancing Occluded Face recognition Through Device-Edge Collaboration and Cross-Domain Feature Fusion with Heatmaps 3

Enhancing Occluded Face Recognition Through Device-Edge Coll...

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3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Prasanth, Ashwin Annie Micheal, A. Department of Computer Science & Engineering Sathyabama Institute of Science & Technology Chennai India

ISBN: (纸本)9798331527549

Face recognition under occlusion presents a persistent challenge in computer vision, primarily due to difficulties in capturing and effectively integrating visible and obscured facial features. This paper introduces a novel device-edge collaborative framework that advances occluded face recognition through an innovative cross-domain feature fusion technique. Our method employs a software-generated, occlusion-aware heatmap created from standard visible-light images, which emphasizes key facial landmarks even when regions of the face are partially covered. This heatmap provides a complementary data layer that enhances traditional visible facial features, allowing the cross-domain feature fusion process to synthesize a more comprehensive set of features for reliable recognition under occlusion. The combined feature set leverages both visible and simulated heatmap information to improve model robustness, even in complex occlusion scenarios. Additionally, a delay-optimized task scheduling method is applied on the edge, ensuring efficient resource allocation, faster processing, and minimal latency. Although this project is ongoing, preliminary evaluations indicate the potential effectiveness of this approach, with expected outcomes to include high recognition accuracy and reduced latency. This framework provides a scalable solution for occluded face recognition by integrating complementary data sources without requiring specialized hardware. © 2025 ieee.

关键词： Face recognition

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CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation

CGA-Net: Category Guided Aggregation for Point Cloud Semanti...

引用

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

作者： Lu, Tao Wang, Limin Wu, Gangshan Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781665445092

Previous point cloud semantic segmentation networks use the same process to aggregate features from neighbors of the same category and different categories. However, the joint area between two objects usually only occupies a small percentage in the whole scene. Thus the networks are well-trained for aggregating features from the same category point while not fully trained on aggregating points of different categories. To address this issue, this paper proposes to utilize different aggregation strategies between the same category and different categories. Specifically, it presents a customized module, termed as Category Guided Aggregation (CGA), where it first identifies whether the neighbors belong to the same category with the center point or not, and then handles the two types of neighbors with two carefully-designed modules. Our CGA presents a general network module and could be leveraged in any existing semantic segmentation network. Experiments on three different backbones demonstrate the effectiveness of our method.

关键词： computer vision Aggregates Semantics computer architecture pattern recognition

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Collation of Face recognition Techniques: Image Registration and Dimension Reduction Approaches 3

Collation of Face Recognition Techniques: Image Registration...

引用

3rd International conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Gupta, Gaurav Goel, Anuj Kumar Chandigarh University Apex Institute of Technology Computer Science Engineering Department Punjab Mohali India Chandigarh University Chandigarh University Electronics and Communication Engineering Department Punjab Mohali India

ISBN: (纸本)9798350342338

Faces must be present in images for intelligent vision-based human computer interaction to work. Since many years ago, face recognition research has been ongoing and significant. This procedure entails facial tracking, expression recognition, and many other things. It is crucial to first register the positions of the photos in this process. However, maintaining such a database is highly challenging because of pose invariance, illumination invariance, shift invariance, scale and noise invariance. The Image Registration Algorithm will be used to register all of the unknown photos that are currently in the database. With the use of a reference, this registration technique tries to align a pattern image. This algorithm will produce features that are independent of translation, scaling and rotation. High dimension space, which must be decreased using a dimension reduction algorithm, is the primary challenge in face recognition. This low dimension subspace can be produced using dimension reduction methods like as Linear Discriminant Analysis, Principal Component Analysis and Locality Preserving Projection. In order to find the needed image from the given database, this study will first minimize the dimensions of every image that is now there. © 2023 ieee.

关键词： Dimensions Eigen value Laplacian Faces Projection recognition

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Grip Points Generation on Motorcycle Wire Harnesses Main Branch Based on computer vision and Clustering 10

Grip Points Generation on Motorcycle Wire Harnesses Main Bra...

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10th International conference on Automation, Robotics, and Applications, ICARA 2024

作者： Espana, Sofia Sanchez, Juan Saldarriaga, Carlos Puruncajas, Bryan Seo, Sungwon Moon, Hyungpil Yumbla, Francisco Polytechnic University Escuela Superior Politecnica del Litoral Faculty of Mechanical and Production Sciences Engineering FIMCP ESPOL Guayaquil Ecuador Sungkyunkwan University Department of Mechanical Engineering Jangan-gu Suwon2066 Korea Republic of

ISBN: (纸本)9798350394245

The motorcycle industry has come a long way in automating the assembly processes, yet the automation of electrical wiring still presents a considerable challenge. Due to their complex and non-linear nature, wires prove difficult to automate their manipulation. This paper proposes a solution that utilizes a cable recognition system powered by computer vision and unsupervised machine learning (clustering). By treating each pixel of the cable as a point in Cartesian space and grouping them, we can obtain a trajectory and build a precise map of the cable. With this knowledge, handlers can identify and manage their main branch from any point on the wire harness, which is critical when manipulating and routing wire harnesses. Our proposed solution enables us to leverage industrial robots' high precision and repeatability, making it possible to automate even the most challenging aspect of motorcycle assembly. © 2024 ieee.

关键词： Cables

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