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Author | Pau Torras; Arnau Baro; Alicia Fornes; Lei Kang | ||||
Title | Improving Handwritten Music Recognition through Language Model Integration | Type | Conference Article | ||
Year | 2022 | Publication | 4th International Workshop on Reading Music Systems (WoRMS2022) | Abbreviated Journal | |
Volume | Issue | Pages | 42-46 | ||
Keywords | optical music recognition; historical sources; diversity; music theory; digital humanities | ||||
Abstract | Handwritten Music Recognition, especially in the historical domain, is an inherently challenging endeavour; paper degradation artefacts and the ambiguous nature of handwriting make recognising such scores an error-prone process, even for the current state-of-the-art Sequence to Sequence models. In this work we propose a way of reducing the production of statistically implausible output sequences by fusing a Language Model into a recognition Sequence to Sequence model. The idea is leveraging visually-conditioned and context-conditioned output distributions in order to automatically find and correct any mistakes that would otherwise break context significantly. We have found this approach to improve recognition results to 25.15 SER (%) from a previous best of 31.79 SER (%) in the literature. | ||||
Address | November 18, 2022 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WoRMS | ||
Notes | DAG; 600.121; 600.162; 602.230 | Approved | no | ||
Call Number | Admin @ si @ TBF2022 | Serial | 3735 | ||
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Author | Julio C. S. Jacques Junior; Yagmur Gucluturk; Marc Perez; Umut Guçlu; Carlos Andujar; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Marcel A. J. van Gerven; Rob van Lier; Sergio Escalera | ||||
Title | First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis | Type | Journal Article | ||
Year | 2022 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
Volume | 13 | Issue | 1 | Pages | 75-95 |
Keywords | Personality computing; first impressions; person perception; big-five; subjective bias; computer vision; machine learning; nonverbal signals; facial expression; gesture; speech analysis; multi-modal recognition | ||||
Abstract | Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed. | ||||
Address | 1 Jan.-March 2022 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | HuPBA | Approved | no | ||
Call Number | Admin @ si @ JGP2022 | Serial | 3724 | ||
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Author | Patricia Suarez; Angel Sappa; Dario Carpio; Henry Velesaca; Francisca Burgos; Patricia Urdiales | ||||
Title | Deep Learning Based Shrimp Classification | Type | Conference Article | ||
Year | 2022 | Publication | 17th International Symposium on Visual Computing | Abbreviated Journal | |
Volume | 13598 | Issue | Pages | 36–45 | |
Keywords | Pigmentation; Color space; Light weight network | ||||
Abstract | This work proposes a novel approach based on deep learning to address the classification of shrimp (Pennaeus vannamei) into two classes, according to their level of pigmentation accepted by shrimp commerce. The main goal of this actual study is to support the shrimp industry in terms of price and process. An efficient CNN architecture is proposed to perform image classification through a program that could be set other in mobile devices or in fixed support in the shrimp supply chain. The proposed approach is a lightweight model that uses HSV color space shrimp images. A simple pipeline shows the most important stages performed to determine a pattern that identifies the class to which they belong based on their pigmentation. For the experiments, a database acquired with mobile devices of various brands and models has been used to capture images of shrimp. The results obtained with the images in the RGB and HSV color space allow for testing the effectiveness of the proposed model. | ||||
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Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ISVC | ||
Notes | MSIAU; no proj | Approved | no | ||
Call Number | Admin @ si @ SAC2022 | Serial | 3772 | ||
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Author | Diego Velazquez; Pau Rodriguez; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
Title | A Closer Look at Embedding Propagation for Manifold Smoothing | Type | Journal Article | ||
Year | 2022 | Publication | Journal of Machine Learning Research | Abbreviated Journal | JMLR |
Volume | 23 | Issue | 252 | Pages | 1-27 |
Keywords | Regularization; emi-supervised learning; self-supervised learning; adversarial robustness; few-shot classification | ||||
Abstract | Supervised training of neural networks requires a large amount of manually annotated data and the resulting networks tend to be sensitive to out-of-distribution (OOD) data.
Self- and semi-supervised training schemes reduce the amount of annotated data required during the training process. However, OOD generalization remains a major challenge for most methods. Strategies that promote smoother decision boundaries play an important role in out-of-distribution generalization. For example, embedding propagation (EP) for manifold smoothing has recently shown to considerably improve the OOD performance for few-shot classification. EP achieves smoother class manifolds by building a graph from sample embeddings and propagating information through the nodes in an unsupervised manner. In this work, we extend the original EP paper providing additional evidence and experiments showing that it attains smoother class embedding manifolds and improves results in settings beyond few-shot classification. Concretely, we show that EP improves the robustness of neural networks against multiple adversarial attacks as well as semi- and self-supervised learning performance. |
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Address | 9/2022 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ VRG2022 | Serial | 3762 | ||
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Author | Josep Brugues Pujolras; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | A Multilingual Approach to Scene Text Visual Question Answering | Type | Conference Article | ||
Year | 2022 | Publication | Document Analysis Systems.15th IAPR International Workshop, (DAS2022) | Abbreviated Journal | |
Volume | Issue | Pages | 65-79 | ||
Keywords | Scene text; Visual question answering; Multilingual word embeddings; Vision and language; Deep learning | ||||
Abstract | Scene Text Visual Question Answering (ST-VQA) has recently emerged as a hot research topic in Computer Vision. Current ST-VQA models have a big potential for many types of applications but lack the ability to perform well on more than one language at a time due to the lack of multilingual data, as well as the use of monolingual word embeddings for training. In this work, we explore the possibility to obtain bilingual and multilingual VQA models. In that regard, we use an already established VQA model that uses monolingual word embeddings as part of its pipeline and substitute them by FastText and BPEmb multilingual word embeddings that have been aligned to English. Our experiments demonstrate that it is possible to obtain bilingual and multilingual VQA models with a minimal loss in performance in languages not used during training, as well as a multilingual model trained in multiple languages that match the performance of the respective monolingual baselines. | ||||
Address | La Rochelle, France; May 22–25, 2022 | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 611.004; 600.155; 601.002 | Approved | no | ||
Call Number | Admin @ si @ BGK2022b | Serial | 3695 | ||
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Author | Nil Ballus; Bhalaji Nagarajan; Petia Radeva | ||||
Title | Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition | Type | Conference Article | ||
Year | 2022 | Publication | 10th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | 13256 | Issue | Pages | ||
Keywords | Self-supervised; Contrastive learning; Food recognition | ||||
Abstract | Self-supervised Learning has been showing upbeat performance in several computer vision tasks. The popular contrastive methods make use of a Siamese architecture with different loss functions. In this work, we go deeper into two very recent state of the art frameworks, namely, SimSiam and Barlow Twins. Inspired by them, we propose a new self-supervised learning method we call Opt-SSL that combines both image and feature contrasting. We validate the proposed method on the food recognition task, showing that our proposed framework enables the self-learning networks to learn better visual representations. | ||||
Address | Aveiro; Portugal; May 2022 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | IbPRIA | ||
Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ BNR2022 | Serial | 3782 | ||
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Author | Joakim Bruslund Haurum; Meysam Madadi; Sergio Escalera; Thomas B. Moeslund | ||||
Title | Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification | Type | Journal Article | ||
Year | 2022 | Publication | Automation in Construction | Abbreviated Journal | AC |
Volume | 144 | Issue | Pages | 104614 | |
Keywords | Sewer Defect Classification; Vision Transformers; Sinkhorn-Knopp; Convolutional Neural Networks; Closed-Circuit Television; Sewer Inspection | ||||
Abstract | A crucial part of image classification consists of capturing non-local spatial semantics of image content. This paper describes the multi-scale hybrid vision transformer (MSHViT), an extension of the classical convolutional neural network (CNN) backbone, for multi-label sewer defect classification. To better model spatial semantics in the images, features are aggregated at different scales non-locally through the use of a lightweight vision transformer, and a smaller set of tokens was produced through a novel Sinkhorn clustering-based tokenizer using distinct cluster centers. The proposed MSHViT and Sinkhorn tokenizer were evaluated on the Sewer-ML multi-label sewer defect classification dataset, showing consistent performance improvements of up to 2.53 percentage points. | ||||
Address | Dec 2022 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | HuPBA | Approved | no | ||
Call Number | Admin @ si @ BME2022c | Serial | 3780 | ||
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Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera; Vassilis Athitsos; Mohammad Sabokrou | ||||
Title | All You Need In Sign Language Production | Type | Miscellaneous | ||
Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Sign Language Production; Sign Language Recog- nition; Sign Language Translation; Deep Learning; Survey; Deaf | ||||
Abstract | Sign Language is the dominant form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental.
To this end, sign language recognition and production are two necessary parts for making such a two-way system. Signlanguage recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. To have more realistic perspectives to sign language, we present an introduction to the Deaf culture, Deaf centers, psychological perspective of sign language, the main differences between spoken language and sign language. Furthermore, we present the fundamental components of a bi-directional sign language translation system, discussing the main challenges in this area. Also, the backbone architectures and methods in SLP are briefly introduced and the proposed taxonomy on SLP is presented. Finally, a general framework for SLP and performance evaluation, and also a discussion on the recent developments, advantages, and limitations in SLP, commenting on possible lines for future research are presented. |
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | HuPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ RKE2022c | Serial | 3698 | ||
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Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
Title | A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution | Type | Journal Article | ||
Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 22 | Issue | 6 | Pages | 2254 |
Keywords | Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images | ||||
Abstract | This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online. | ||||
Address | |||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | MSIAU; | Approved | no | ||
Call Number | Admin @ si @ RSV2022b | Serial | 3688 | ||
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Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla | ||||
Title | Multi-Image Super-Resolution for Thermal Images | Type | Conference Article | ||
Year | 2022 | Publication | 17th International Conference on Computer Vision Theory and Applications (VISAPP 2022) | Abbreviated Journal | |
Volume | 4 | Issue | Pages | 635-642 | |
Keywords | Thermal Images; Multi-view; Multi-frame; Super-Resolution; Deep Learning; Attention Block | ||||
Abstract | This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses two
attention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches. |
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Address | Online; Feb 6-8, 2022 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | VISAPP | ||
Notes | MSIAU; 601.349 | Approved | no | ||
Call Number | Admin @ si @ RSV2022a | Serial | 3690 | ||
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Author | Aneesh Rangnekar; Zachary Mulhollan; Anthony Vodacek; Matthew Hoffman; Angel Sappa; Erik Blasch; Jun Yu; Liwen Zhang; Shenshen Du; Hao Chang; Keda Lu; Zhong Zhang; Fang Gao; Ye Yu; Feng Shuang; Lei Wang; Qiang Ling; Pranjay Shyam; Kuk-Jin Yoon; Kyung-Soo Kim | ||||
Title | Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022 | Type | Conference Article | ||
Year | 2022 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | Abbreviated Journal | |
Volume | Issue | Pages | 390-398 | ||
Keywords | Training; Computer visio; Conferences; Training data; Object detection; Semisupervised learning; Transformers | ||||
Abstract | This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the Computer Vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. | ||||
Address | New Orleans; USA; June 2022 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | MSIAU; no menciona | Approved | no | ||
Call Number | Admin @ si @ RMV2022 | Serial | 3774 | ||
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Author | Bojana Gajic; Ariel Amato; Ramon Baldrich; Joost Van de Weijer; Carlo Gatta | ||||
Title | Area Under the ROC Curve Maximization for Metric Learning | Type | Conference Article | ||
Year | 2022 | Publication | CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition) | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition | ||||
Abstract | Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification. | ||||
Address | New Orleans, USA; 20 June 2022 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | CIC; LAMP; | Approved | no | ||
Call Number | Admin @ si @ GAB2022 | Serial | 3700 | ||
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Author | Kai Wang; Xialei Liu; Andrew Bagdanov; Luis Herranz; Shangling Jui; Joost Van de Weijer | ||||
Title | Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition | Type | Conference Article | ||
Year | 2022 | Publication | CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) | Abbreviated Journal | |
Volume | Issue | Pages | 3728-3738 | ||
Keywords | Training; Computer vision; Image recognition; Upper bound; Conferences; Pattern recognition; Task analysis | ||||
Abstract | In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more gener-
alizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively. |
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Address | New Orleans, USA; 20 June 2022 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | LAMP; 600.147 | Approved | no | ||
Call Number | Admin @ si @ WLB2022 | Serial | 3686 | ||
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Author | Mohamed Ramzy Ibrahim; Robert Benavente; Felipe Lumbreras; Daniel Ponsa | ||||
Title | 3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D Residual in Residual Dense Blocks | Type | Conference Article | ||
Year | 2022 | Publication | CVPR 2022 Workshop on IEEE Perception Beyond the Visible Spectrum workshop series (PBVS, 18th Edition) | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Training; Solid modeling; Three-dimensional displays; PSNR; Convolution; Superresolution; Pattern recognition | ||||
Abstract | The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively. | ||||
Address | New Orleans, USA; 19 June 2022 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | MSIAU; 600.130 | Approved | no | ||
Call Number | Admin @ si @ IBL2022 | Serial | 3693 | ||
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Author | Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes | ||||
Title | Lost in Transcription of Graphic Signs in Ciphers | Type | Conference Article | ||
Year | 2022 | Publication | International Conference on Historical Cryptology (HistoCrypt 2022) | Abbreviated Journal | |
Volume | Issue | Pages | 153-158 | ||
Keywords | transcription of ciphers; hand-written text recognition of symbols; graphic signs | ||||
Abstract | Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. | ||||
Address | Amsterdam, Netherlands, June 20-22, 2022 | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | HystoCrypt | ||
Notes | DAG; 600.121; 600.162; 602.230; 600.140 | Approved | no | ||
Call Number | Admin @ si @ MBS2022 | Serial | 3731 | ||
Permanent link to this record |