Records |
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 |
Abbreviated Journal |
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Volume |
12822 |
Issue |
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Pages |
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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DAG; 600.121; 600.140; 110.312 |
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no |
Call Number |
Admin @ si @ MRG2021b |
Serial |
3571 |
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Author |
Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Zahra Zahra Raisi-Estabragh; Steffen E. Petersen; Polyxeni Gkontra; Karim Lekadir; Gloria Menegaz; Petia Radeva |
Title |
A new scheme for the assessment of the robustness of Explainable Methods Applied to Brain Age estimation |
Type |
Conference Article |
Year |
2021 |
Publication |
34th International Symposium on Computer-Based Medical Systems |
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Pages |
492-497 |
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Deep learning methods show great promise in a range of settings including the biomedical field. Explainability of these models is important in these fields for building end-user trust and to facilitate their confident deployment. Although several Machine Learning Interpretability tools have been proposed so far, there is currently no recognized evaluation standard to transfer the explainability results into a quantitative score. Several measures have been proposed as proxies for quantitative assessment of explainability methods. However, the robustness of the list of significant features provided by the explainability methods has not been addressed. In this work, we propose a new proxy for assessing the robustness of the list of significant features provided by two explainability methods. Our validation is defined at functionality-grounded level based on the ranked correlation statistical index and demonstrates its successful application in the framework of brain aging estimation. We assessed our proxy to estimate brain age using neuroscience data. Our results indicate small variability and high robustness in the considered explainability methods using this new proxy. |
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CBMS |
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MILAB; no proj |
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no |
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Admin @ si @ SBZ2021 |
Serial |
3629 |
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Author |
Ajian Liu; Chenxu Zhao; Zitong Yu; Anyang Su; Xing Liu; Zijian Kong; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zhen Lei; Guodong Guo |
Title |
3D High-Fidelity Mask Face Presentation Attack Detection Challenge |
Type |
Conference Article |
Year |
2021 |
Publication |
IEEE/CVF International Conference on Computer Vision Workshops |
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Pages |
814-823 |
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The threat of 3D mask to face recognition systems is increasing serious, and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of total amount of 54,600 videos which are recorded from 75 subjects with 225 realistic masks under 7 new kinds of sensors. Based on this dataset and Protocol 3 which evaluates both the discrimination and generalization ability of the algorithm under the open set scenarios, we organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask based attack detection. It attracted more than 200 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including the introduction of the dataset used, the definition of the protocol, the calculation of the evaluation criteria, and the summary and publication of the competition results. Finally, we focus on introducing and analyzing the top ranked algorithms, the conclusion summary, and the research ideas for mask attack detection provided by this competition. |
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Virtual; October 2021 |
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ICCVW |
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HUPBA; no proj |
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no |
Call Number |
Admin @ si @ LZY2021 |
Serial |
3646 |
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Author |
Ajian Liu; Zichang Tan; Jun Wan; Sergio Escalera; Guodong Guo; Stan Z. Li |
Title |
CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-Ethnicity Face Anti-Spoofing |
Type |
Conference Article |
Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
1178-1186 |
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Abstract |
The issue of ethnic bias has proven to affect the performance of face recognition in previous works, while it still remains to be vacant in face anti-spoofing. Therefore, in order to study the ethnic bias for face anti-spoofing, we introduce the largest CASIA-SURF Cross-ethnicity Face Anti-spoofing (CeFA) dataset, covering 3 ethnicities, 3 modalities, 1,607 subjects, and 2D plus 3D attack types. Five protocols are introduced to measure the affect under varied evaluation conditions, such as cross-ethnicity, unknown spoofs or both of them. As our knowledge, CASIA-SURF CeFA is the first dataset including explicit ethnic labels in current released datasets. Then, we propose a novel multi-modal fusion method as a strong baseline to alleviate the ethnic bias, which employs a partially shared fusion strategy to learn complementary information from multiple modalities. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability for other existing datasets, i.e., CASIA-SURF, OULU-NPU and SiW datasets. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2020?authuser=0. |
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Virtual; January 2021 |
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WACV |
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HUPBA; no proj |
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no |
Call Number |
Admin @ si @ LTW2021 |
Serial |
3661 |
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Author |
Akhil Gurram; Ahmet Faruk Tuna; Fengyi Shen; Onay Urfalioglu; Antonio Lopez |
Title |
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision |
Type |
Journal Article |
Year |
2021 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
Volume |
23 |
Issue |
8 |
Pages |
12738-12751 |
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Abstract |
Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences. |
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Notes |
ADAS; 600.118 |
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no |
Call Number |
Admin @ si @ GTS2021 |
Serial |
3598 |
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Author |
Albert Rial-Farras; Meysam Madadi; Sergio Escalera |
Title |
UV-based reconstruction of 3D garments from a single RGB image |
Type |
Conference Article |
Year |
2021 |
Publication |
16th IEEE International Conference on Automatic Face and Gesture Recognition |
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1-8 |
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Garments are highly detailed and dynamic objects made up of particles that interact with each other and with other objects, making the task of 2D to 3D garment reconstruction extremely challenging. Therefore, having a lightweight 3D representation capable of modelling fine details is of great importance. This work presents a deep learning framework based on Generative Adversarial Networks (GANs) to reconstruct 3D garment models from a single RGB image. It has the peculiarity of using UV maps to represent 3D data, a lightweight representation capable of dealing with high-resolution details and wrinkles. With this model and kind of 3D representation, we achieve state-of-the-art results on the CLOTH3D++ dataset, generating good quality and realistic garment reconstructions regardless of the garment topology and shape, human pose, occlusions and lightning. |
Address |
Virtual; December 2021 |
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FG |
Notes |
HUPBA; no proj |
Approved |
no |
Call Number |
Admin @ si @ RME2021 |
Serial |
3639 |
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Author |
Albert Suso; Pau Riba; Oriol Ramos Terrades; Josep Llados |
Title |
A Self-supervised Inverse Graphics Approach for Sketch Parametrization |
Type |
Conference Article |
Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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Volume |
12916 |
Issue |
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Pages |
28-42 |
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Abstract |
The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets. |
Address |
Lausanne; Suissa; September 2021 |
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ICDAR |
Notes |
DAG; 600.121 |
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no |
Call Number |
Admin @ si @ SRR2021 |
Serial |
3675 |
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Author |
Albin Soutif; Marc Masana; Joost Van de Weijer; Bartlomiej Twardowski |
Title |
On the importance of cross-task features for class-incremental learning |
Type |
Conference Article |
Year |
2021 |
Publication |
Theory and Foundation of continual learning workshop of ICML |
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In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks. The main difference with task-incremental learning, where a task-ID is available at inference time, is that the learner also needs to perform crosstask discrimination, i.e. distinguish between classes that have not been seen together. Approaches to tackle this problem are numerous and mostly make use of an external memory (buffer) of non-negligible size. In this paper, we ablate the learning of crosstask features and study its influence on the performance of basic replay strategies used for class-IL. We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance. Our experimental results show that future algorithms for class-incremental learning should not only prevent forgetting, but also aim to improve the quality of the cross-task features. This is especially important when the number of classes per task is small. |
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Virtual; July 2021 |
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ICMLW |
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LAMP |
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no |
Call Number |
Admin @ si @ SMW2021 |
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3588 |
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Author |
Alejandro Cartas; Petia Radeva; Mariella Dimiccoli |
Title |
Modeling long-term interactions to enhance action recognition |
Type |
Conference Article |
Year |
2021 |
Publication |
25th International Conference on Pattern Recognition |
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Pages |
10351-10358 |
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In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical LongShort-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks,without relying on motion information |
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January 2021 |
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ICPR |
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MILAB; |
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no |
Call Number |
Admin @ si @ CRD2021 |
Serial |
3626 |
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Author |
Alina Matei; Andreea Glavan; Petia Radeva; Estefania Talavera |
Title |
Towards Eating Habits Discovery in Egocentric Photo-Streams |
Type |
Journal Article |
Year |
2021 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
Volume |
9 |
Issue |
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Pages |
17495-17506 |
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Abstract |
Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioral pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals. |
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MILAB; no proj |
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no |
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Admin @ si @ MGR2021 |
Serial |
3637 |
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Author |
AN Ruchai; VI Kober; KA Dorofeev; VN Karnaukhov; Mikhail Mozerov |
Title |
Classification of breast abnormalities using a deep convolutional neural network and transfer learning |
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Journal Article |
Year |
2021 |
Publication |
Journal of Communications Technology and Electronics |
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Volume |
66 |
Issue |
6 |
Pages |
778–783 |
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Abstract |
A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database. |
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LAMP; |
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no |
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Admin @ si @ RKD2022 |
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3680 |
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Author |
Andreea Glavan; Alina Matei; Petia Radeva; Estefania Talavera |
Title |
Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams |
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Journal Article |
Year |
2021 |
Publication |
Expert Systems with Applications |
Abbreviated Journal |
ESWA |
Volume |
171 |
Issue |
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Pages |
114506 |
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Nutrition and social interactions are both key aspects of the daily lives of humans. In this work, we propose a system to evaluate the influence of social interaction in the nutritional habits of a person from a first-person perspective. In order to detect the routine of an individual, we construct a nutritional behaviour pattern discovery model, which outputs routines over a number of days. Our method evaluates similarity of routines with respect to visited food-related scenes over the collected days, making use of Dynamic Time Warping, as well as considering social engagement and its correlation with food-related activities. The nutritional and social descriptors of the collected days are evaluated and encoded using an LSTM Autoencoder. Later, the obtained latent space is clustered to find similar days unaffected by outliers using the Isolation Forest method. Moreover, we introduce a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100 k egocentric images gathered by 7 users. Several different visualizations are evaluated for the understanding of the findings. Our results demonstrate good performance and applicability of our proposed model for social-related nutritional behaviour understanding. At the end, relevant applications of the model are discussed by analysing the discovered routine of particular individuals. |
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MILAB; no proj |
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no |
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Admin @ si @ GMR2021 |
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3634 |
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Author |
Andres Mafla; Rafael S. Rezende; Lluis Gomez; Diana Larlus; Dimosthenis Karatzas |
Title |
StacMR: Scene-Text Aware Cross-Modal Retrieval |
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Conference Article |
Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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2219-2229 |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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no |
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Admin @ si @ MRG2021a |
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3492 |
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Author |
Andres Mafla; Ruben Tito; Sounak Dey; Lluis Gomez; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas |
Title |
Real-time Lexicon-free Scene Text Retrieval |
Type |
Journal Article |
Year |
2021 |
Publication |
Pattern Recognition |
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PR |
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110 |
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107656 |
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In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos. |
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DAG; 600.121; 600.129; 601.338 |
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no |
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Admin @ si @ MTD2021 |
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3493 |
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Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
Title |
Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval |
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Conference Article |
Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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4022-4032 |
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Address |
Virtual; January 2021 |
Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
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Editor |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
WACV |
Notes |
DAG; 600.121 |
Approved |
no |
Call Number |
Admin @ si @ MDB2021 |
Serial |
3491 |
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