Results 21 to 30 of about 5,390 (262)

Person Re-Identification by Siamese Network

open access: yesInteligencia Artificial, 2023
Re-Identification of person aims at retrieval of person across multiple non overlapping camera. There was a huge gain in the computer vision community with the advancement of deep learning features and also the number of surveillance in videos increased. The challenges faced by person re-identification is low resolution images, pose variation etc., and
Newlin Shebiah Russel   +3 more
openaire   +2 more sources

Siamese networks for large-scale author identification [PDF]

open access: yesComputer Speech & Language, 2021
28 pages.
Chakaveh Saedi, Mark Dras
openaire   +2 more sources

Using a lightweight Siamese neural network for generating a feature vector in a vascular authentication system

open access: yesКомпьютерная оптика, 2023
The article analyzes the possibility of using a Siamese convolutional neural network to solve the problem of vascular authentication on an embedded hardware platform with limited computing resources (Orange Pi One).
D.E. Prozorov, A.V. Zemtsov
doaj   +1 more source

Study on the evolution of Chinese characters based on few-shot learning: From oracle bone inscriptions to regular script.

open access: yesPLoS ONE, 2022
Oracle bone inscriptions (OBIs) are ancient Chinese scripts originated in the Shang Dynasty of China, and now less than half of the existing OBIs are well deciphered.
Mengru Wang   +6 more
doaj   +1 more source

Siamese network features for image matching [PDF]

open access: yes2016 23rd International Conference on Pattern Recognition (ICPR), 2016
Finding matching images across large datasets plays a key role in many computer vision applications such as structure-from-motion (SfM), multi-view 3D reconstruction, image retrieval, and image-based localisation. In this paper, we propose finding matching and non-matching pairs of images by representing them with neural network based feature vectors ...
Kannala, Juho   +3 more
openaire   +4 more sources

Distilled Siamese Networks for Visual Tracking

open access: yesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from high memory costs, which restrict their applicability to mobile devices with tight memory budgets. To address this issue, we propose a distilled Siamese tracking framework to
Jianbing Shen   +5 more
openaire   +4 more sources

Cow Face Recognition for a Small Sample Based on Siamese DB Capsule Network

open access: yesIEEE Access, 2022
Dairy cow face recognition using Neural Networks has several hurdles. For example, there are only a few instances of each individual. The positions and angles of the individuals in the image fluctuate considerably, the differences between individuals are
Feng Xu, Jing Gao, Xin Pan
doaj   +1 more source

SpeakerNet for Cross-lingual Text-Independent Speaker Verification

open access: yesArchives of Acoustics, 2020
Biometrics provide an alternative to passwords and pins for authentication. The emergence of machine learning algorithms provides an easy and economical solution to authentication problems.
Hafsa HABIB   +4 more
doaj   +1 more source

A strong feature representation for siamese network tracker [PDF]

open access: yesMultimedia Tools and Applications, 2020
Object tracking has important application in assistive technologies for personalized monitoring. Recent trackers choosing AlexNet as their backbone to extract features have gained great success. However, AlexNet is too shallow to form a strong feature representation, the tracker based on the Siamese network have an accuracy gap compared with state-of ...
Zhipeng Zhou, Rui Zhang, Dong Yin
openaire   +2 more sources

Developing a Siamese Network for Intrusion Detection Systems [PDF]

open access: yesProceedings of the 1st Workshop on Machine Learning and Systems, 2021
Machine Learning (ML) for developing Intrusion Detection Systems (IDS) is a fast-evolving research area that has many unsolved domain challenges. Current IDS models face two challenges that limit their performance and robustness. Firstly, they require large datasets to train and their performance is highly dependent on the dataset size.
Hanan Hindy   +4 more
openaire   +4 more sources

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