Suchergebnisse - 2018 IEEE/CVF Conference on Computer Vision AND Pattern Recognition*

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)

Autoren: Ramesh Manza Bharti Gawali Pravin Yannawar et al.

Resource Type: eBook.

Schlagworte: Artificial intelligence--Congresses, Computer vision--Congresses

Categories: COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Image Processing, MATHEMATICS / Probability & Statistics / General

Identification and Recognition of Animals from Biometric Markers Using Computer Vision Approaches: A Review.

Autoren: CIHAN, Pınar SAYGILI, Ahmet OZMEN, Nihat Eren et al.

Quelle: Kafkas Üniversitesi Veteriner Fakültesi Dergisi. Nov/Dec2023, Vol. 29 Issue 6, p581-593. 13p.

Schlagworte: Identification of animals, Computer vision, Animal tracks, Biometry, Theft prevention, Identification

A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications.

Autoren: Zhang, Yumin Wang, Yanyong

Quelle: EURASIP Journal on Image & Video Processing; 8/13/2025, Vol. 2025 Issue 1, p1-65, 65p

Schlagwörter: HUMAN activity recognition, COMPUTER vision, DEPTH maps (Digital image processing), ALGORITHMS, TRANSFORMER models, RECURRENT neural networks, DEEP learning, CONVOLUTIONAL neural networks

Point rotation invariant features and attention fusion network for point cloud registration of 3D shapes.

Autoren: Liu, Zeyang Lu, Zhiguo Shan, Yancong

Quelle: Scientific Reports; 4/29/2025, Vol. 15 Issue 1, p1-16, 16p

Schlagwörter: PATTERN recognition systems, POINT cloud, FEATURE extraction, COMPUTER vision, ARTIFICIAL intelligence, IMAGE registration

Personalized clothing recommendation framework based on the fusion of sports biomechanics and computer vision.

Autoren: Liu, Xiaoyang Sun, Yan

Quelle: Molecular & Cellular Biomechanics; 2025, Vol. 22 Issue 5, p1-26, 26p

Schlagwörter: SPORTS biomechanics, COMPUTER vision, RECOMMENDER systems, CUSTOM-made clothing, ARTIFICIAL neural networks, MULTISENSOR data fusion, PRESSURE sensors

Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features

Autoren: Wimmer, Thomas Wonka, Peter Ovsjanikov, Maks et al.

Weitere Verfasser: Wimmer, Thomas Wonka, Peter Ovsjanikov, Maks et al.

Quelle: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition
https://hal.science/hal-04550189
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2024, Seattle, WA, United States
https://cvpr.thecvf.com/

Schlagwörter: 3D Keypoint Detection, Shape Analysis, 3D Semantic Segmentation, Deep Neural Networks, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]

Geographisches Schlagwort: Seattle, WA, United States

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2311.18113; info:eu-repo/grantAgreement//758800/EU/Exploring Relations in Structured Data with Functional Maps/EXPROTEA; ARXIV: 2311.18113

Verfügbarkeit: https://hal.science/hal-04550189
https://hal.science/hal-04550189v1/document
https://hal.science/hal-04550189v1/file/wimmer-b2-3d.pdf

PoNQ: a Neural QEM-based Mesh Representation

Autoren: Maruani, Nissim Ovsjanikov, Maks Alliez, Pierre et al.

Weitere Verfasser: Maruani, Nissim Ovsjanikov, Maks Alliez, Pierre et al.

Quelle: CVPR 2024 - IEEE/CVF Conference on Computer Vision and Pattern Recognition ; https://hal.science/hal-04706248 ; CVPR 2024 - IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2024, Seattle, United States

Schlagwörter: [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]

Geographisches Schlagwort: Seattle, United States

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2403.12870; info:eu-repo/grantAgreement//758800/EU/Exploring Relations in Structured Data with Functional Maps/EXPROTEA; info:eu-repo/grantAgreement//101087347/EU/Universal Geometric Transfer Learning/VEGA; ARXIV: 2403.12870

Verfügbarkeit: https://hal.science/hal-04706248
https://hal.science/hal-04706248v1/document
https://hal.science/hal-04706248v1/file/main.pdf

Dual-stage temporal perception network for continuous sign language recognition.

Autoren: Huang, Zhigang Xue, Wanli Zhou, Yuxi et al.

Quelle: Visual Computer; Feb2025, Vol. 41 Issue 3, p1971-1986, 16p

Schlagwörter: PATTERN recognition systems, ARTIFICIAL intelligence, VIDEO excerpts, SIGN language, TIME-varying networks

SideCow-VSS: A Video Semantic Segmentation Dataset and Benchmark for Intelligent Monitoring of Dairy Cows Health in Smart Ranch Environments.

Autoren: Yao, Lei Liu, Jin Hong, Weinan et al.

Quelle: Veterinary Sciences; Nov2025, Vol. 12 Issue 11, p1104, 13p

Schlagwörter: DAIRY cattle, COMPUTER vision, AGRICULTURAL technology, DATA quality, PRECISION farming, OBJECT recognition (Computer vision), PATIENT monitoring, ARTIFICIAL intelligence

Deep Learning Performance in Analyzing Nailfold Videocapillaroscopy Images in Systemic Sclerosis.

Autoren: Yayla, Müçteba Enes Aydın, Ayhan Kılıçaslan, Mahmut et al.

Quelle: Diagnostics (2075-4418); Nov2025, Vol. 17 Issue 22, p2912, 12p

Schlagwörter: DEEP learning, SYSTEMIC scleroderma, IMAGE recognition (Computer vision), COMPUTER-assisted image analysis (Medicine), RHEUMATOLOGY, ARTIFICIAL intelligence

Subspace-PnP: A Geometric Constraint Loss for Mutual Assistance of Depth and Optical Flow Estimation.

Autoren: Chi, Cheng Hao, Tianyu Wang, Qingjie et al.

Quelle: International Journal of Computer Vision; Dec2022, Vol. 130 Issue 12, p3054-3069, 16p

Schlagwörter: OPTICAL flow, PATTERN recognition systems, COMPUTER vision

Semi-supervised learning made simple with self-supervised clustering

Autoren: Fini, Enrico Astolfi, Pietro Alahari, Karteek et al.

Weitere Verfasser: Fini, Enrico Astolfi, Pietro Alahari, Karteek et al.

Quelle: CVPR 2023 – IEEE/CVF Conference on Computer Vision and Pattern Recognition ; https://inria.hal.science/hal-04073630 ; CVPR 2023 – IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2023, Vancouver, Canada. pp.3187-3197, ⟨10.1109/CVPR52729.2023.00311⟩

Schlagwörter: [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]

Geographisches Schlagwort: Vancouver

Time: Vancouver, Canada

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2306.07483; info:eu-repo/grantAgreement//871245/EU/Socially Pertinent Robots in Gerontological Healthcare/SPRING; info:eu-repo/grantAgreement//714381/EU/Large-Scale Learning with Deep Kernel Machines/SOLARIS; ARXIV: 2306.07483

Verfügbarkeit: https://inria.hal.science/hal-04073630
https://inria.hal.science/hal-04073630v1/document
https://inria.hal.science/hal-04073630v1/file/fini23.pdf
https://doi.org/10.1109/CVPR52729.2023.00311

DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images

Autoren: Demir, I. Koperski, K. Lindenbaum, D. et al.

Weitere Verfasser: Demir, I. Koperski, K. Lindenbaum, D. et al.

Quelle: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). :172-17209

Schlagwörter: FOS: Computer and information sciences, 13. Climate action, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Life Science, 02 engineering and technology

Dateibeschreibung: application/pdf

Zugangs-URL: https://dspace.mit.edu/bitstream/1721.1/125668/2/Demir_DeepGlobe_2018_A_CVPR_2018_paper.pdf
http://arxiv.org/abs/1805.06561
http://export.arxiv.org/pdf/1805.06561
http://ui.adsabs.harvard.edu/abs/2018arXiv180506561D/abstract
https://ieeexplore.ieee.org/document/8575485
https://doi.ieeecomputersociety.org/10.1109/CVPRW.2018.00031
https://www.computer.org/csdl/proceedings-article/cvprw/2018/610000a172/17D45WrVg0C
https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w4/Demir_DeepGlobe_2018_A_CVPR_2018_paper.pdf

A pyramid Gaussian pooling based CNN and transformer hybrid network for smoke segmentation.

Autoren: Wang, Guiqian Yuan, Feiniu Li, Hongdi et al.

Quelle: IET Image Processing (Wiley-Blackwell); 10/16/2024, Vol. 18 Issue 12, p3206-3217, 12p

Schlagwörter: CONVOLUTIONAL neural networks, OBJECT recognition (Computer vision), TRANSFORMER models, IMAGE processing, IMAGE segmentation

Fake it till you make it: Learning transferable representations from synthetic ImageNet clones

Autoren: Sariyildiz, Mert Bulent Alahari, Karteek Larlus, Diane et al.

Weitere Verfasser: Sariyildiz, Mert Bulent Alahari, Karteek Larlus, Diane et al.

Quelle: CVPR 2023 - IEEE/CVF Conference on Computer Vision and Pattern Recognition ; https://inria.hal.science/hal-03916262 ; CVPR 2023 - IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2023, Vancouver, Canada. pp.8011-8021, ⟨10.1109/CVPR52729.2023.00774⟩

Schlagwörter: [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]

Geographisches Schlagwort: Vancouver

Time: Vancouver, Canada

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2212.08420; ARXIV: 2212.08420

Verfügbarkeit: https://inria.hal.science/hal-03916262
https://inria.hal.science/hal-03916262v2/document
https://inria.hal.science/hal-03916262v2/file/sariyildiz23a.pdf
https://doi.org/10.1109/CVPR52729.2023.00774

DiverseNet: When One Right Answer is not Enough

Autoren: Firman, M. Campbell, N. D. F. Agapito, L. et al.

Quelle: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. (pp. pp. 5598-5607). IEEE: Salt Late City, UT, USA. (2018)
UCL Discovery

Schlagwörter: FOS: Computer and information sciences, 03 medical and health sciences, 0302 clinical medicine, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, Training, Task analysis, Aerospace electronics, Three-dimensional displays, Supervised learning, Two dimensional displays, Training data, 02 engineering and technology

Dateibeschreibung: text

Zugangs-URL: http://arxiv.org/pdf/2008.10634
http://arxiv.org/abs/2008.10634
https://openaccess.thecvf.com/content_cvpr_2018/papers/Firman_DiverseNet_When_One_CVPR_2018_paper.pdf
https://openaccess.thecvf.com/content_cvpr_2018/html/Firman_DiverseNet_When_One_CVPR_2018_paper.html
https://dblp.uni-trier.de/db/conf/cvpr/cvpr2018.html#FirmanCAB18
https://www.arxiv-vanity.com/papers/2008.10634/
https://researchportal.bath.ac.uk/en/publications/diversenet-when-one-right-answer-is-not-enough
https://arxiv.org/abs/2008.10634
https://discovery.ucl.ac.uk/id/eprint/10074121/1/cvpr18_diversenet.pdf
https://discovery.ucl.ac.uk/id/eprint/10074121/

DAEF-YOLO Model for Individual and Behavior Recognition of Sanhua Geese in Precision Farming Applications.

Autoren: Sun, Tianyuan Zhang, Shujuan Ren, Rui et al.

Quelle: Animals (2076-2615); Oct2025, Vol. 15 Issue 20, p3058, 28p

Schlagwörter: PRECISION farming, GEESE, ARTIFICIAL intelligence, OBJECT recognition (Computer vision), AUTOMATIC tracking, HUMAN activity recognition

VM-RTDETR: Advancing DETR with Vision State-Space Duality and Multi-Scale Fusion for Robust Pig Detection.

Autoren: Hao, Wangli Xu, Shu-Ai Shu, Hao et al.

Quelle: Animals (2076-2615); Nov2025, Vol. 15 Issue 22, p3328, 23p

Schlagwörter: OBJECT recognition (Computer vision), COMPUTER vision, ANIMAL health, SWINE breeds, AGRICULTURAL technology