| Records |
| Author |
Ana Garcia Rodriguez; Yael Tudela; Henry Cordova; S. Carballal; I. Ordas; L. Moreira; E. Vaquero; O. Ortiz; L. Rivero; F. Javier Sanchez; Miriam Cuatrecasas; Maria Pellise; Jorge Bernal; Gloria Fernandez Esparrach |
| Title |
First in Vivo Computer-Aided Diagnosis of Colorectal Polyps using White Light Endoscopy |
Type |
Journal Article |
| Year |
2022 |
Publication |
Endoscopy |
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END |
| Volume |
54 |
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2022/04/14 |
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Georg Thieme Verlag KG |
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ISE |
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no |
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Admin @ si @ GTC2022a |
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3746 |
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| Author |
Henry Velesaca; Patricia Suarez; Raul Mira; Angel Sappa |
| Title |
Computer Vision based Food Grain Classification: a Comprehensive Survey |
Type |
Journal Article |
| Year |
2021 |
Publication |
Computers and Electronics in Agriculture |
Abbreviated Journal |
CEA |
| Volume |
187 |
Issue |
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Pages |
106287 |
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| Abstract |
This manuscript presents a comprehensive survey on recent computer vision based food grain classification techniques. It includes state-of-the-art approaches intended for different grain varieties. The approaches proposed in the literature are analyzed according to the processing stages considered in the classification pipeline, making it easier to identify common techniques and comparisons. Additionally, the type of images considered by each approach (i.e., images from the: visible, infrared, multispectral, hyperspectral bands) together with the strategy used to generate ground truth data (i.e., real and synthetic images) are reviewed. Finally, conclusions highlighting future needs and challenges are presented. |
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| Notes |
MSIAU; 600.130; 600.122 |
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no |
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Admin @ si @ VSM2021 |
Serial |
3576 |
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| Author |
Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca |
| Title |
Towards Visual Personality Questionnaires based on Deep Learning and Social Media |
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Conference Article |
| Year |
2019 |
Publication |
21st International Conference on Social Influence and Social Psychology |
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April 2019; Tokio; Japan |
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ICSISP |
| Notes |
ISE; 600.119 |
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no |
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Admin @ si @ RGG2020 |
Serial |
3554 |
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| Author |
Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes |
| Title |
Learning Graph Edit Distance by Graph NeuralNetworks |
Type |
Miscellaneous |
| Year |
2020 |
Publication |
Arxiv |
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The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. |
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| Notes |
DAG; 600.121; 600.140; 601.302 |
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no |
| Call Number |
Admin @ si @ RFL2020 |
Serial |
3555 |
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| Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
| Title |
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition |
Type |
Journal Article |
| Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
129 |
Issue |
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Pages |
108766 |
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| Abstract |
The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios. |
| Address |
Sept. 2022 |
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| Notes |
DAG; 600.121; 600.162 |
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no |
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Admin @ si @ KRR2022 |
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3556 |
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| Author |
Klara Janousckova; Jiri Matas; Lluis Gomez; Dimosthenis Karatzas |
| Title |
Text Recognition – Real World Data and Where to Find Them |
Type |
Conference Article |
| Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
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| Volume |
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Issue |
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Pages |
4489-4496 |
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| Abstract |
We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as “pseudo ground truth” (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets 1 1 Acknowledgements. The authors were supported by Czech Technical University student grant SGS20/171/0HK3/3TJ13, the MEYS VVV project CZ.02.1.01/0.010.0J16 019/0000765 Research Center for Informatics, the Spanish Research project TIN2017-89779-P and the CERCA Programme / Generalitat de Catalunya. |
| Address |
Virtual; January 2021 |
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ICPR |
| Notes |
DAG; 600.121; 600.129 |
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no |
| Call Number |
Admin @ si @ JMG2020 |
Serial |
3557 |
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| Author |
Minesh Mathew; Ruben Tito; Dimosthenis Karatzas; R.Manmatha; C.V. Jawahar |
| Title |
Document Visual Question Answering Challenge 2020 |
Type |
Conference Article |
| Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition – Short paper |
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This paper presents results of Document Visual Question Answering Challenge organized as part of “Text and Documents in the Deep Learning Era” workshop, in CVPR 2020. The challenge introduces a new problem – Visual Question Answering on document images. The challenge comprised two tasks. The first task concerns with asking questions on a single document image. On the other hand, the second task is set as a retrieval task where the question is posed over a collection of images. For the task 1 a new dataset is introduced comprising 50,000 questions-answer(s) pairs defined over 12,767 document images. For task 2 another dataset has been created comprising 20 questions over 14,362 document images which share the same document template. |
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CVPR |
| Notes |
DAG; 600.121 |
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no |
| Call Number |
Admin @ si @ MTK2020 |
Serial |
3558 |
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| Author |
Ozge Mercanoglu Sincan; Julio C. S. Jacques Junior; Sergio Escalera; Hacer Yalim Keles |
| Title |
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research |
Type |
Conference Article |
| Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
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Issue |
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Pages |
3467-3476 |
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The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories. |
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Virtual; June 2021 |
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CVPRW |
| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MJE2021 |
Serial |
3560 |
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| Author |
Shiqi Yang; Kai Wang; Luis Herranz; Joost Van de Weijer |
| Title |
On Implicit Attribute Localization for Generalized Zero-Shot Learning |
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Journal Article |
| Year |
2021 |
Publication |
IEEE Signal Processing Letters |
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28 |
Issue |
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Pages |
872 - 876 |
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Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions. Since attributes are often related to specific parts of objects, many recent works focus on discovering discriminative regions. However, these methods usually require additional complex part detection modules or attention mechanisms. In this paper, 1) we show that common ZSL backbones (without explicit attention nor part detection) can implicitly localize attributes, yet this property is not exploited. 2) Exploiting it, we then propose SELAR, a simple method that further encourages attribute localization, surprisingly achieving very competitive generalized ZSL (GZSL) performance when compared with more complex state-of-the-art methods. Our findings provide useful insight for designing future GZSL methods, and SELAR provides an easy to implement yet strong baseline. |
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LAMP; 600.120 |
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no |
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YWH2021 |
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3563 |
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Domicele Jonauskaite; Lucia Camenzind; C. Alejandro Parraga; Cecile N Diouf; Mathieu Mercapide Ducommun; Lauriane Müller; Melanie Norberg; Christine Mohr |
| Title |
Colour-emotion associations in individuals with red-green colour blindness |
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Journal Article |
| Year |
2021 |
Publication |
PeerJ |
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9 |
Issue |
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e11180 |
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Affect; Chromotherapy; Colour cognition; Colour vision deficiency; Cross-modal correspondences; Daltonism; Deuteranopia; Dichromatic; Emotion; Protanopia. |
| Abstract |
Colours and emotions are associated in languages and traditions. Some of us may convey sadness by saying feeling blue or by wearing black clothes at funerals. The first example is a conceptual experience of colour and the second example is an immediate perceptual experience of colour. To investigate whether one or the other type of experience more strongly drives colour-emotion associations, we tested 64 congenitally red-green colour-blind men and 66 non-colour-blind men. All participants associated 12 colours, presented as terms or patches, with 20 emotion concepts, and rated intensities of the associated emotions. We found that colour-blind and non-colour-blind men associated similar emotions with colours, irrespective of whether colours were conveyed via terms (r = .82) or patches (r = .80). The colour-emotion associations and the emotion intensities were not modulated by participants' severity of colour blindness. Hinting at some additional, although minor, role of actual colour perception, the consistencies in associations for colour terms and patches were higher in non-colour-blind than colour-blind men. Together, these results suggest that colour-emotion associations in adults do not require immediate perceptual colour experiences, as conceptual experiences are sufficient. |
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CIC; LAMP; 600.120; 600.128 |
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no |
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Admin @ si @ JCP2021 |
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3564 |
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| Author |
Marc Masana; Tinne Tuytelaars; Joost Van de Weijer |
| Title |
Ternary Feature Masks: zero-forgetting for task-incremental learning |
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Conference Article |
| Year |
2021 |
Publication |
34th IEEE Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
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3565-3574 |
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We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue -- and show experimentally -- that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters for each new task; with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches. |
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Virtual; June 2021 |
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CVPRW |
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LAMP; 600.120 |
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no |
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Admin @ si @ MTW2021 |
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3565 |
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| Author |
Sudeep Katakol; Luis Herranz; Fei Yang; Marta Mrak |
| Title |
DANICE: Domain adaptation without forgetting in neural image compression |
Type |
Conference Article |
| Year |
2021 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops |
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1921-1925 |
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Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC. |
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Virtual; June 2021 |
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CVPRW |
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LAMP; 600.120; 600.141; 601.379 |
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no |
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Admin @ si @ KHY2021 |
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3568 |
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| Author |
Fei Yang; Luis Herranz; Yongmei Cheng; Mikhail Mozerov |
| Title |
Slimmable compressive autoencoders for practical neural image compression |
Type |
Conference Article |
| Year |
2021 |
Publication |
34th IEEE Conference on Computer Vision and Pattern Recognition |
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4996-5005 |
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Neural image compression leverages deep neural networks to outperform traditional image codecs in rate-distortion performance. However, the resulting models are also heavy, computationally demanding and generally optimized for a single rate, limiting their practical use. Focusing on practical image compression, we propose slimmable compressive autoencoders (SlimCAEs), where rate (R) and distortion (D) are jointly optimized for different capacities. Once trained, encoders and decoders can be executed at different capacities, leading to different rates and complexities. We show that a successful implementation of SlimCAEs requires suitable capacity-specific RD tradeoffs. Our experiments show that SlimCAEs are highly flexible models that provide excellent rate-distortion performance, variable rate, and dynamic adjustment of memory, computational cost and latency, thus addressing the main requirements of practical image compression. |
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Virtual; June 2021 |
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CVPR |
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LAMP; 600.120 |
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no |
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Admin @ si @ YHC2021 |
Serial |
3569 |
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| Author |
Arturo Fuentes; F. Javier Sanchez; Thomas Voncina; Jorge Bernal |
| Title |
LAMV: Learning to Predict Where Spectators Look in Live Music Performances |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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5 |
Issue |
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500-507 |
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The advent of artificial intelligence has supposed an evolution on how different daily work tasks are performed. The analysis of cultural content has seen a huge boost by the development of computer-assisted methods that allows easy and transparent data access. In our case, we deal with the automation of the production of live shows, like music concerts, aiming to develop a system that can indicate the producer which camera to show based on what each of them is showing. In this context, we consider that is essential to understand where spectators look and what they are interested in so the computational method can learn from this information. The work that we present here shows the results of a first preliminary study in which we compare areas of interest defined by human beings and those indicated by an automatic system. Our system is based on the extraction of motion textures from dynamic Spatio-Temporal Volumes (STV) and then analyzing the patterns by means of texture analysis techniques. We validate our approach over several video sequences that have been labeled by 16 different experts. Our method is able to match those relevant areas identified by the experts, achieving recall scores higher than 80% when a distance of 80 pixels between method and ground truth is considered. Current performance shows promise when detecting abnormal peaks and movement trends. |
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Virtual; February 2021 |
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VISIGRAPP |
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MV; ISE; 600.119; |
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no |
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Admin @ si @ FSV2021 |
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3570 |
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| Author |
Adria Molina; Pau Riba; Lluis Gomez; Oriol Ramos Terrades; Josep Llados |
| Title |
Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach |
Type |
Conference Article |
| Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
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12822 |
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306-320 |
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| Abstract |
This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods. |
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Lausanne; Suissa; September 2021 |
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| Notes |
DAG; 600.121; 600.140; 110.312 |
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no |
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Admin @ si @ MRG2021b |
Serial |
3571 |
| Permanent link to this record |
