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| Author | Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero | ||||
| Title | Recurrent Comparator with attention models to detect counterfeit documents | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | This paper is focused on the detection of counterfeit documents via the recurrent comparison of the security textured background regions of two images. The main contributions are twofold: first we apply and adapt a recurrent comparator architecture with attention mechanism to the counterfeit detection task, which constructs a representation of the background regions by recurrently condition the next observation, learning the difference between genuine and counterfeit images through iterative glimpses. Second we propose a new counterfeit document dataset to ensure the generalization of the learned model towards the detection of the lack of resolution during the counterfeit manufacturing. The presented network, outperforms state-of-the-art classification approaches for counterfeit detection as demonstrated in the evaluation. | ||||
| Address | Sidney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.140; 600.121; 601.269 | Approved | no | ||
| Call Number | Admin @ si @ BRL2019 | Serial | 3456 | ||
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| Author | Fernando Vilariño | ||||
| Title | Library Living Lab, Numérisation 3D des chapiteaux du cloître de Saint-Cugat : des citoyens co- créant le nouveau patrimoine culturel numérique | Type | Conference Article | ||
| Year | 2019 | Publication | Intersectorialité et approche Living Labs. Entretiens Jacques-Cartier | Abbreviated Journal | |
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| Address | Montreal; Canada; December 2019 | ||||
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| Notes | MV; DAG; 600.140; 600.121;SIAI | Approved | no | ||
| Call Number | Admin @ si @ Vil2019a | Serial | 3457 | ||
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| Author | Fernando Vilariño | ||||
| Title | Public Libraries Exploring how technology transforms the cultural experience of people | Type | Conference Article | ||
| Year | 2019 | Publication | Workshop on Social Impact of AI. Open Living Lab Days Conference. | Abbreviated Journal | |
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| Address | Thessaloniki; Grecia; September 2019 | ||||
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| Notes | MV; DAG; 600.140; 600.121;SIAI | Approved | no | ||
| Call Number | Admin @ si @ Vil2019b | Serial | 3458 | ||
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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 | Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell; Dimitris Samaras | ||||
| Title | Light Direction and Color Estimation from Single Image with Deep Regression | Type | Conference Article | ||
| Year | 2020 | Publication | London Imaging Conference | Abbreviated Journal | |
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| Abstract | We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source. Apart from showing good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves good performance when it is applied to real scenes. | ||||
| Address | Virtual; September 2020 | ||||
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| Area | Expedition | Conference | LIM | ||
| Notes | CIC; 600.118; 600.140; | Approved | no | ||
| Call Number | Admin @ si @ SBV2020 | Serial | 3460 | ||
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| Author | Sagnik Das; Hassan Ahmed Sial; Ke Ma; Ramon Baldrich; Maria Vanrell; Dimitris Samaras | ||||
| Title | Intrinsic Decomposition of Document Images In-the-Wild | Type | Conference Article | ||
| Year | 2020 | Publication | 31st British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | Automatic document content processing is affected by artifacts caused by the shape
of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 21% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW. |
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| Address | Virtual; September 2020 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | CIC; 600.087; 600.140; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DSM2020 | Serial | 3461 | ||
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| Author | Fernando Vilariño | ||||
| Title | 3D Scanning of Capitals at Library Living Lab | Type | Book Whole | ||
| Year | 2019 | Publication | “Living Lab Projects 2019”. ENoLL. | Abbreviated Journal | |
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| Notes | MV; DAG; 600.140; 600.121;SIAI | Approved | no | ||
| Call Number | Admin @ si @ Vil2019c | Serial | 3463 | ||
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| Author | Xinhang Song; Haitao Zeng; Sixian Zhang; Luis Herranz; Shuqiang Jiang | ||||
| Title | Generalized Zero-shot Learning with Multi-source Semantic Embeddings for Scene Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | 28th ACM International Conference on Multimedia | Abbreviated Journal | |
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| Abstract | Recognizing visual categories from semantic descriptions is a promising way to extend the capability of a visual classifier beyond the concepts represented in the training data (i.e. seen categories). This problem is addressed by (generalized) zero-shot learning methods (GZSL), which leverage semantic descriptions that connect them to seen categories (e.g. label embedding, attributes). Conventional GZSL are designed mostly for object recognition. In this paper we focus on zero-shot scene recognition, a more challenging setting with hundreds of categories where their differences can be subtle and often localized in certain objects or regions. Conventional GZSL representations are not rich enough to capture these local discriminative differences. Addressing these limitations, we propose a feature generation framework with two novel components: 1) multiple sources of semantic information (i.e. attributes, word embeddings and descriptions), 2) region descriptions that can enhance scene discrimination. To generate synthetic visual features we propose a two-step generative approach, where local descriptions are sampled and used as conditions to generate visual features. The generated features are then aggregated and used together with real features to train a joint classifier. In order to evaluate the proposed method, we introduce a new dataset for zero-shot scene recognition with multi-semantic annotations. Experimental results on the proposed dataset and SUN Attribute dataset illustrate the effectiveness of the proposed method. | ||||
| Address | Virtual; October 2020 | ||||
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| Area | Expedition | Conference | ACM | ||
| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ SZZ2020 | Serial | 3465 | ||
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| Author | Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer | ||||
| Title | Bookworm continual learning: beyond zero-shot learning and continual learning | Type | Conference Article | ||
| Year | 2020 | Publication | Workshop TASK-CV 2020 | Abbreviated Journal | |
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| Abstract | We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem. | ||||
| Address | Virtual; August 2020 | ||||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WHD2020 | Serial | 3466 | ||
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| Author | Carola Figueroa Flores | ||||
| Title | Visual Saliency for Object Recognition, and Object Recognition for Visual Saliency | Type | Book Whole | ||
| Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Keywords | computer vision; visual saliency; fine-grained object recognition; convolutional neural networks; images classification | ||||
| Abstract | For humans, the recognition of objects is an almost instantaneous, precise and
extremely adaptable process. Furthermore, we have the innate capability to learn new object classes from only few examples. The human brain lowers the complexity of the incoming data by filtering out part of the information and only processing those things that capture our attention. This, mixed with our biological predisposition to respond to certain shapes or colors, allows us to recognize in a simple glance the most important or salient regions from an image. This mechanism can be observed by analyzing on which parts of images subjects place attention; where they fix their eyes when an image is shown to them. The most accurate way to record this behavior is to track eye movements while displaying images. Computational saliency estimation aims to identify to what extent regions or objects stand out with respect to their surroundings to human observers. Saliency maps can be used in a wide range of applications including object detection, image and video compression, and visual tracking. The majority of research in the field has focused on automatically estimating saliency maps given an input image. Instead, in this thesis, we set out to incorporate saliency maps in an object recognition pipeline: we want to investigate whether saliency maps can improve object recognition results. In this thesis, we identify several problems related to visual saliency estimation. First, to what extent the estimation of saliency can be exploited to improve the training of an object recognition model when scarce training data is available. To solve this problem, we design an image classification network that incorporates saliency information as input. This network processes the saliency map through a dedicated network branch and uses the resulting characteristics to modulate the standard bottom-up visual characteristics of the original image input. We will refer to this technique as saliency-modulated image classification (SMIC). In extensive experiments on standard benchmark datasets for fine-grained object recognition, we show that our proposed architecture can significantly improve performance, especially on dataset with scarce training data. Next, we address the main drawback of the above pipeline: SMIC requires an explicit saliency algorithm that must be trained on a saliency dataset. To solve this, we implement a hallucination mechanism that allows us to incorporate the saliency estimation branch in an end-to-end trained neural network architecture that only needs the RGB image as an input. A side-effect of this architecture is the estimation of saliency maps. In experiments, we show that this architecture can obtain similar results on object recognition as SMIC but without the requirement of ground truth saliency maps to train the system. Finally, we evaluated the accuracy of the saliency maps that occur as a sideeffect of object recognition. For this purpose, we use a set of benchmark datasets for saliency evaluation based on eye-tracking experiments. Surprisingly, the estimated saliency maps are very similar to the maps that are computed from human eye-tracking experiments. Our results show that these saliency maps can obtain competitive results on benchmark saliency maps. On one synthetic saliency dataset this method even obtains the state-of-the-art without the need of ever having seen an actual saliency image for training. |
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| Address | March 2021 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Bogdan Raducanu | |
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| ISSN | ISBN | 978-84-122714-4-7 | Medium | ||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ Fig2021 | Serial | 3600 | ||
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| Author | Debora Gil; Guillermo Torres | ||||
| Title | A multi-shape loss function with adaptive class balancing for the segmentation of lung structures | Type | Conference Article | ||
| Year | 2020 | Publication | 34th International Congress and Exhibition on Computer Assisted Radiology & Surgery | Abbreviated Journal | |
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| Address | Virtual; June 2020 | ||||
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| Area | Expedition | Conference | CARS | ||
| Notes | IAM; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GiT2020 | Serial | 3472 | ||
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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 | Debora Gil; Katerine Diaz; Carles Sanchez; Aura Hernandez-Sabate | ||||
| Title | Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images | Type | Miscellaneous | ||
| Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Future SARS-CoV-2 virus outbreak COVID-XX might possibly occur during the next years. However the pathology in humans is so recent that many clinical aspects, like early detection of complications, side effects after recovery or early screening, are currently unknown. In spite of the number of cases of COVID-19, its rapid spread putting many sanitary systems in the edge of collapse has hindered proper collection and analysis of the data related to COVID-19 clinical aspects. We describe an interdisciplinary initiative that integrates clinical research, with image diagnostics and the use of new technologies such as artificial intelligence and radiomics with the aim of clarifying some of SARS-CoV-2 open questions. The whole initiative addresses 3 main points: 1) collection of standardize data including images, clinical data and analytics; 2) COVID-19 screening for its early diagnosis at primary care centers; 3) define radiomic signatures of COVID-19 evolution and associated pathologies for the early treatment of complications. In particular, in this paper we present a general overview of the project, the experimental design and first results of X-ray COVID-19 detection using a classic approach based on HoG and feature selection. Our experiments include a comparison to some recent methods for COVID-19 screening in X-Ray and an exploratory analysis of the feasibility of X-Ray COVID-19 screening. Results show that classic approaches can outperform deep-learning methods in this experimental setting, indicate the feasibility of early COVID-19 screening and that non-COVID infiltration is the group of patients most similar to COVID-19 in terms of radiological description of X-ray. Therefore, an efficient COVID-19 screening should be complemented with other clinical data to better discriminate these cases. | ||||
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| Notes | IAM; 600.139; 600.145; 601.337 | Approved | no | ||
| Call Number | Admin @ si @ GDS2020 | Serial | 3474 | ||
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| Author | Oriol Ramos Terrades; Albert Berenguel; Debora Gil | ||||
| Title | A flexible outlier detector based on a topology given by graph communities | Type | Miscellaneous | ||
| Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings. | ||||
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| Notes | IAM; DAG; 600.139; 600.145; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RBG2020 | Serial | 3475 | ||
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| Author | Diego Porres | ||||
| Title | Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks | Type | Conference Article | ||
| Year | 2021 | Publication | Machine Learning for Creativity and Design, Neurips Workshop | Abbreviated Journal | |
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| Abstract | Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at this https URL. | ||||
| Address | Virtual; December 2021 | ||||
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| Area | Expedition | Conference | NEURIPSW | ||
| Notes | ADAS; 601.365 | Approved | no | ||
| Call Number | Admin @ si @ Por2021 | Serial | 3597 | ||
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