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| Author | Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski | ||||
| Title | Kornia: an Open Source Differentiable Computer Vision Library for PyTorch | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
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Address  |
Aspen; Colorado; USA; March 2020 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | MSIAU; 600.122; 600.130 | Approved | no | ||
| Call Number | Admin @ si @ RMP2020 | Serial | 3291 | ||
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| Author | Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
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| Abstract | Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval. | ||||
| Address |
Aspen; Colorado; USA; March 2020 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ MDB2020 | Serial | 3334 | ||
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| Author | Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Exploring Hate Speech Detection in Multimodal Publications | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
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| Abstract | In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research. | ||||
| Address |
Aspen; March 2020 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GGG2020a | Serial | 3280 | ||
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| Author | Xavier Soria; Edgar Riba; Angel Sappa | ||||
| Title | Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
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| Abstract | This paper proposes a Deep Learning based edge detector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed approach generates thin edge-maps that are plausible for human eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contribution, a large dataset with carefully annotated edges has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing improvements with the proposed method when F-measure of ODS and OIS are considered. | ||||
| Address |
Aspen; USA; March 2020 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SRS2020 | Serial | 3434 | ||
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| Author | Debora Gil; Oriol Ramos Terrades; Raquel Perez | ||||
| Title | Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution | Type | Conference Article | ||
| Year | 2020 | Publication | Women in Geometry and Topology | Abbreviated Journal | |
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| Address |
Barcelona; September 2019 | ||||
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| Notes | IAM; DAG; 600.139; 600.145; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GRP2020 | Serial | 3473 | ||
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| Author | Yaxing Wang | ||||
| Title | Transferring and Learning Representations for Image Generation and Translation | Type | Book Whole | ||
| Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Image generation is arguably one of the most attractive, compelling, and challenging tasks in computer vision. Among the methods which perform image generation, generative adversarial networks (GANs) play a key role. The most common image generation models based on GANs can be divided into two main approaches. The first one, called simply image generation takes random noise as an input and synthesizes an image which follows the same distribution as the images in the training set. The second class, which is called image-to-image translation, aims to map an image from a source domain to one that is indistinguishable from those in the target domain. Image-to-image translation methods can further be divided into paired and unpaired image-to-image translation based on whether they require paired data or not. In this thesis, we aim to address some challenges of both image generation and image-to-image generation.GANs highly rely upon having access to vast quantities of data, and fail to generate realistic images from random noise when applied to domains with few images. To address this problem, we aim to transfer knowledge from a model trained on a large dataset (source domain) to the one learned on limited data (target domain). We find that both GANs andconditional GANs can benefit from models trained on large datasets. Our experiments show that transferring the discriminator is more important than the generator. Using both the generator and discriminator results in the best performance. We found, however, that this method suffers from overfitting, since we update all parameters to adapt to the target data. We propose a novel architecture, which is tailored to address knowledge transfer to very small target domains. Our approach effectively exploreswhich part of the latent space is more related to the target domain. Additionally, the proposed method is able to transfer knowledge from multiple pretrained GANs. Although image-to-image translation has achieved outstanding performance, it still facesseveral problems. First, for translation between complex domains (such as translations between different modalities) image-to-image translation methods require paired data. We show that when only some of the pairwise translations have been seen (i.e. during training), we can infer the remaining unseen translations (where training pairs are not available). We propose a new approach where we align multiple encoders and decoders in such a way that the desired translation can be obtained by simply cascadingthe source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen). Second, we address the issue of bias in image-to-image translation. Biased datasets unavoidably contain undesired changes, which are dueto the fact that the target dataset has a particular underlying visual distribution. We use carefully designed semantic constraints to reduce the effects of the bias. The semantic constraint aims to enforce the preservation of desired image properties. Finally, current approaches fail to generate diverse outputs or perform scalable image transfer in a single model. To alleviate this problem, we propose a scalable and diverse image-to-image translation. We employ random noise to control the diversity. The scalabitlity is determined by conditioning the domain label.computer vision, deep learning, imitation learning, adversarial generative networks, image generation, image-to-image translation. | ||||
| Address |
January 2020 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Abel Gonzalez;Luis Herranz | |
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| ISSN | ISBN | 978-84-121011-5-7 | Medium | ||
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| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ Wan2020 | Serial | 3397 | ||
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| Author | Sangeeth Reddy; Minesh Mathew; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar | ||||
| Title | RoadText-1K: Text Detection and Recognition Dataset for Driving Videos | Type | Conference Article | ||
| Year | 2020 | Publication | IEEE International Conference on Robotics and Automation | Abbreviated Journal | |
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| Abstract | Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/ projects/cvit-projects/roadtext-1k |
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| Address |
Paris; Francia; ??? | ||||
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| Area | Expedition | Conference | ICRA | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RMG2020 | Serial | 3400 | ||
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| Author | Fernando Vilariño | ||||
| Title | Unveiling the Social Impact of AI | Type | Conference Article | ||
| Year | 2020 | Publication | Workshop at Digital Living Lab Days Conference | Abbreviated Journal | |
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| Address |
September 2020 | ||||
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| Notes | MV; DAG; 600.121; 600.140;SIAI | Approved | no | ||
| Call Number | Admin @ si @ Vil2020 | Serial | 3459 | ||
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| Author | Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez | ||||
| Title | Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem | Type | Journal | ||
| Year | 2020 | Publication | Mathematics | Abbreviated Journal | MATH |
| Volume | 20 | Issue | 8(9) | Pages | 1595 |
| Keywords | STEM education; Project-based learning; Coding; software tool | ||||
| Abstract | In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view. |
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September 2020 | ||||
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| Notes | IAM; ISE | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3722 | ||
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| Author | David Berga; Xavier Otazu | ||||
| Title | Computations of top-down attention by modulating V1 dynamics | Type | Conference Article | ||
| Year | 2020 | Publication | Computational and Mathematical Models in Vision | Abbreviated Journal | |
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| Address |
St. Pete Beach; Florida; May 2020 | ||||
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| Area | Expedition | Conference | MODVIS | ||
| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ BeO2020a | Serial | 3376 | ||
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| Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Thermal Image Super-resolution: A Novel Architecture and Dataset | Type | Conference Article | ||
| Year | 2020 | Publication | 15th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 111-119 | ||
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| Abstract | This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem.
The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are available. |
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| Address |
Valletta; Malta; February 2020 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ RSV2020 | Serial | 3432 | ||
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| Author | Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca | ||||
| Title | Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem | Type | Conference Article | ||
| Year | 2020 | Publication | 15th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
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| Abstract | This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The transfer learning consists of first training the network using pairs of images from the virtual-world scenario
considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose estimation accuracy using the proposed model, as well as further improvements when the transfer learning strategy (synthetic-world data transfer learning real-world data) is considered to tackle the limitation on the training due to the reduced number of pairs of real-images on most of the public data sets. |
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| Address |
Valletta; Malta; February 2020 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ CSV2020 | Serial | 3433 | ||
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| Author | Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva | ||||
| Title | Uncertainty Modeling and Deep Learning Applied to Food Image Analysis | Type | Conference Article | ||
| Year | 2020 | Publication | 13th International Joint Conference on Biomedical Engineering Systems and Technologies | Abbreviated Journal | |
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| Abstract | Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis. | ||||
| Address |
Villetta; Malta; February 2020 | ||||
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| Area | Expedition | Conference | BIODEVICES | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ ANK2020 | Serial | 3526 | ||
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| Author | Xialei Liu; Chenshen Wu; Mikel Menta; Luis Herranz; Bogdan Raducanu; Andrew Bagdanov; Shangling Jui; Joost Van de Weijer | ||||
| Title | Generative Feature Replay for Class-Incremental Learning | Type | Conference Article | ||
| Year | 2020 | Publication | CLVISION – Workshop on Continual Learning in Computer Vision | Abbreviated Journal | |
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| Abstract | Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We consider a class-incremental setting which means that the task-ID is unknown at inference time. The imbalance between old and new classes typically results in a bias of the network towards the newest ones. This imbalance problem can either be addressed by storing exemplars from previous tasks, or by using image replay methods. However, the latter can only be applied to toy datasets since image generation for complex datasets is a hard problem.
We propose a solution to the imbalance problem based on generative feature replay which does not require any exemplars. To do this, we split the network into two parts: a feature extractor and a classifier. To prevent forgetting, we combine generative feature replay in the classifier with feature distillation in the feature extractor. Through feature generation, our method reduces the complexity of generative replay and prevents the imbalance problem. Our approach is computationally efficient and scalable to large datasets. Experiments confirm that our approach achieves state-of-the-art results on CIFAR-100 and ImageNet, while requiring only a fraction of the storage needed for exemplar-based continual learning |
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| Address |
Virtual CVPR | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | LAMP; 601.309; 602.200; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LWM2020 | Serial | 3419 | ||
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| Author | Yaxing Wang; Abel Gonzalez-Garcia; David Berga; Luis Herranz; Fahad Shahbaz Khan; Joost Van de Weijer | ||||
| Title | MineGAN: effective knowledge transfer from GANs to target domains with few images | Type | Conference Article | ||
| Year | 2020 | Publication | 33rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
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| Abstract | One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective. We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility. We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. | ||||
| Address |
Virtual CVPR | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | LAMP; 600.109; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WGB2020 | Serial | 3421 | ||
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