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Author | Giovanni Maria Farinella; Petia Radeva; Jose Braz | ||||
Title | Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications | Type | Book Whole | ||
Year | 2020 | Publication | Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications; VISIGRAPP 2020 | Abbreviated Journal | |
Volume | 5 | Issue | Pages | ||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ FRB2020b | Serial | 3547 | ||
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Author | Carola Figueroa Flores; David Berga; Joost Van de Weijer; Bogdan Raducanu | ||||
Title | Saliency for free: Saliency prediction as a side-effect of object recognition | Type | Journal Article | ||
Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 150 | Issue | Pages | 1-7 | |
Keywords | Saliency maps; Unsupervised learning; Object recognition | ||||
Abstract | Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects instead of the background. So far, computational methods for saliency estimation required the explicit generation of a saliency map, process which is usually achieved via eyetracking experiments on still images. This is a tedious process that needs to be repeated for each new dataset. In the current paper, we demonstrate that is possible to automatically generate saliency maps without ground-truth. In our approach, saliency maps are learned as a side effect of object recognition. Extensive experiments carried out on both real and synthetic datasets demonstrated that our approach is able to generate accurate saliency maps, achieving competitive results when compared with supervised methods. | ||||
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Notes | LAMP; 600.147; 600.120 | Approved | no | ||
Call Number | Admin @ si @ FBW2021 | Serial | 3559 | ||
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Author | Idoia Ruiz; Lorenzo Porzi; Samuel Rota Bulo; Peter Kontschieder; Joan Serrat | ||||
Title | Weakly Supervised Multi-Object Tracking and Segmentation | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 125-133 | ||
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Abstract | We introduce the problem of weakly supervised MultiObject Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively. |
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Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | WACVW | ||
Notes | ADAS; 600.118; 600.124 | Approved | no | ||
Call Number | Admin @ si @ RPR2021 | Serial | 3548 | ||
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Author | Idoia Ruiz; Joan Serrat | ||||
Title | Rank-based ordinal classification | Type | Conference Article | ||
Year | 2020 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 8069-8076 | ||
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Abstract | Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class labels. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1. . . C, being C the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to least likely. This is thanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing
that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, i.e. most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset. |
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Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | ICPR | ||
Notes | ADAS; 600.118; 600.124 | Approved | no | ||
Call Number | Admin @ si @ RuS2020 | Serial | 3549 | ||
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Author | Guillem Cucurull; Pau Rodriguez; Vacit Oguz Yazici; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
Title | Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks | Type | Miscellaneous | ||
Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | arXiv:1802.06757
Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. To sense the whys of certain social user’s demands and cultural-driven interests, however, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited since this process has been typically been text-based. Following this trend on visual-based social analysis, we present a novel methodology based on Deep Learning to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So the key contribution here is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between those posted images and their accompanying texts, which can be successfully modeled using deep neural networks for personality estimation. The experimental results are consistent with previous cyber-psychology results based on texts or images. In addition, classification results on some traits show that some patterns emerge in the set of images corresponding to a specific text, in essence to those representing an abstract concept. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts. |
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Notes | ISE; 600.098; 600.119 | Approved | no | ||
Call Number | Admin @ si @ CRY2018 | Serial | 3550 | ||
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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 | Abbreviated Journal | END |
Volume | 54 | Issue | Pages | ||
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Address | 2022/04/14 | ||||
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Publisher | Georg Thieme Verlag KG | Place of Publication ![]() |
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ GTC2022a | Serial | 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 | 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 | Approved | no | ||
Call Number | 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 | Type | Conference Article | ||
Year | 2019 | Publication | 21st International Conference on Social Influence and Social Psychology | Abbreviated Journal | |
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Address | April 2019; Tokio; Japan | ||||
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Area | Expedition | Conference | ICSISP | ||
Notes | ISE; 600.119 | Approved | no | ||
Call Number | 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 | Abbreviated Journal | |
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Abstract | 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 | Approved | 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 | 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 | Approved | no | ||
Call Number | Admin @ si @ KRR2022 | Serial | 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 | Abbreviated Journal | |
Volume | Issue | 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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Area | Expedition | Conference | ICPR | ||
Notes | DAG; 600.121; 600.129 | Approved | 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 | Abbreviated Journal | |
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Abstract | 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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Area | Expedition | Conference | CVPR | ||
Notes | DAG; 600.121 | Approved | 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 | Abbreviated Journal | |
Volume | Issue | Pages | 3467-3476 | ||
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Abstract | 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. | ||||
Address | Virtual; June 2021 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ MJE2021 | Serial | 3560 | ||
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Author | Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan C. Moure | ||||
Title | 3D Perception With Slanted Stixels on GPU | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Parallel and Distributed Systems | Abbreviated Journal | TPDS |
Volume | 32 | Issue | 10 | Pages | 2434-2447 |
Keywords | Daniel Hernandez-Juarez; Antonio Espinosa; David Vazquez; Antonio M. Lopez; Juan C. Moure | ||||
Abstract | This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 × 1024 image sizes and 4 × 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier. | ||||
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Notes | ADAS; 600.124; 600.118 | Approved | no | ||
Call Number | Admin @ si @ HEV2021 | Serial | 3561 | ||
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Author | Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez | ||||
Title | Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches | Type | Journal Article | ||
Year | 2021 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 21 | Issue | 9 | Pages | 3185 |
Keywords | co-training; multi-modality; vision-based object detection; ADAS; self-driving | ||||
Abstract | Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ GVL2021 | Serial | 3562 | ||
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