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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.
Address Valletta; Malta; February 2020
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Series Editor Series Title Abbreviated Series Title
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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 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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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
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 Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez
Title Computing the Testing Error Without a Testing Set Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
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Abstract Oral. Paper award nominee.
Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trailand-error. That is, typically, researchers will derive many DNN architectures (i.e., topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-thetesting-data is almost impossible. Using a sequestered testing dataset may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset. Specifically, we derive a number of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. This allows us to compute the DNN’s testing error on unseen samples, even when we do not have access to them. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach.
Address Virtual CVPR
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Area Expedition Conference CVPR
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ CEM2020 Serial 3437
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Author Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz
Title Gate-Shift Networks for Video Action Recognition Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
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Abstract Deep 3D CNNs for video action recognition are designed to learn powerful representations in the joint spatio-temporal feature space. In practice however, because of the large number of parameters and computations involved, they may under-perform in the lack of sufficiently large datasets for training them at scale. In this paper we introduce spatial gating in spatial-temporal decomposition of 3D kernels. We implement this concept with Gate-Shift Module (GSM). GSM is lightweight and turns a 2D-CNN into a highly efficient spatio-temporal feature extractor. With GSM plugged in, a 2D-CNN learns to adaptively route features through time and combine them, at almost no additional parameters and computational overhead. We perform an extensive evaluation of the proposed module to study its effectiveness in video action recognition, achieving state-of-the-art results on Something Something-V1 and Diving48 datasets, and obtaining competitive results on EPIC-Kitchens with far less model complexity.
Address Virtual CVPR
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference CVPR
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ SEL2020 Serial 3438
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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 Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes
Title A conditional GAN based approach for distorted camera captured documents recovery Type Conference Article
Year 2020 Publication 4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence Abbreviated Journal
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Abstract
Address Virtual; December 2020
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference MedPRAI
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ SKF2020 Serial 3450
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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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Abstract
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.
Address Virtual; September 2020
Corporate Author Thesis
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
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 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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Series Editor Series Title Abbreviated Series Title
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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 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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Abstract
Address Virtual; June 2020
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Series Editor Series Title Abbreviated Series Title
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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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Abstract
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.
Address
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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.
Address
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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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