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Author Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz
Title Gate-Shift Networks for Video Action Recognition Type Conference Article
Year (up) 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages
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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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Area Expedition Conference CVPR
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ SEL2020 Serial 3438
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Author Meysam Madadi; Hugo Bertiche; Sergio Escalera
Title SMPLR: Deep learning based SMPL reverse for 3D human pose and shape recovery Type Journal Article
Year (up) 2020 Publication Pattern Recognition Abbreviated Journal PR
Volume 106 Issue Pages 107472
Keywords Deep learning; 3D Human pose; Body shape; SMPL; Denoising autoencoder; Volumetric stack hourglass
Abstract In this paper we propose to embed SMPL within a deep-based model to accurately estimate 3D pose and shape from a still RGB image. We use CNN-based 3D joint predictions as an intermediate representation to regress SMPL pose and shape parameters. Later, 3D joints are reconstructed again in the SMPL output. This module can be seen as an autoencoder where the encoder is a deep neural network and the decoder is SMPL model. We refer to this as SMPL reverse (SMPLR). By implementing SMPLR as an encoder-decoder we avoid the need of complex constraints on pose and shape. Furthermore, given that in-the-wild datasets usually lack accurate 3D annotations, it is desirable to lift 2D joints to 3D without pairing 3D annotations with RGB images. Therefore, we also propose a denoising autoencoder (DAE) module between CNN and SMPLR, able to lift 2D joints to 3D and partially recover from structured error. We evaluate our method on SURREAL and Human3.6M datasets, showing improvement over SMPL-based state-of-the-art alternatives by about 4 and 12 mm, respectively.
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Notes HuPBA; no proj Approved no
Call Number Admin @ si @ MBE2020 Serial 3439
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Author Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li
Title Multi-modal Face Presentation Attach Detection Type Book Whole
Year (up) 2020 Publication Synthesis Lectures on Computer Vision Abbreviated Journal
Volume 13 Issue Pages
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Notes HuPBA Approved no
Call Number Admin @ si @ WGE2020 Serial 3440
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Author Thomas B. Moeslund; Sergio Escalera; Gholamreza Anbarjafari; Kamal Nasrollahi; Jun Wan
Title Statistical Machine Learning for Human Behaviour Analysis Type Journal Article
Year (up) 2020 Publication Entropy Abbreviated Journal ENTROPY
Volume 25 Issue 5 Pages 530
Keywords action recognition; emotion recognition; privacy-aware
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Notes HuPBA; no proj Approved no
Call Number Admin @ si @ MEA2020 Serial 3441
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title Video-based Isolated Hand Sign Language Recognition Using a Deep Cascaded Model Type Journal Article
Year (up) 2020 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 79 Issue Pages 22965–22987
Keywords
Abstract In this paper, we propose an efficient cascaded model for sign language recognition taking benefit from spatio-temporal hand-based information using deep learning approaches, especially Single Shot Detector (SSD), Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM), from videos. Our simple yet efficient and accurate model includes two main parts: hand detection and sign recognition. Three types of spatial features, including hand features, Extra Spatial Hand Relation (ESHR) features, and Hand Pose (HP) features, have been fused in the model to feed to LSTM for temporal features extraction. We train SSD model for hand detection using some videos collected from five online sign dictionaries. Our model is evaluated on our proposed dataset (Rastgoo et al., Expert Syst Appl 150: 113336, 2020), including 10’000 sign videos for 100 Persian sign using 10 contributors in 10 different backgrounds, and isoGD dataset. Using the 5-fold cross-validation method, our model outperforms state-of-the-art alternatives in sign language recognition
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Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ RKE2020b Serial 3442
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Author Raquel Justo; Leila Ben Letaifa; Cristina Palmero; Eduardo Gonzalez-Fraile; Anna Torp Johansen; Alain Vazquez; Gennaro Cordasco; Stephan Schlogl; Begoña Fernandez-Ruanova; Micaela Silva; Sergio Escalera; Mikel de Velasco; Joffre Tenorio-Laranga; Anna Esposito; Maria Korsnes; M. Ines Torres
Title Analysis of the Interaction between Elderly People and a Simulated Virtual Coach, Journal of Ambient Intelligence and Humanized Computing Type Journal Article
Year (up) 2020 Publication Journal of Ambient Intelligence and Humanized Computing Abbreviated Journal AIHC
Volume 11 Issue 12 Pages 6125-6140
Keywords
Abstract The EMPATHIC project develops and validates new interaction paradigms for personalized virtual coaches (VC) to promote healthy and independent aging. To this end, the work presented in this paper is aimed to analyze the interaction between the EMPATHIC-VC and the users. One of the goals of the project is to ensure an end-user driven design, involving senior users from the beginning and during each phase of the project. Thus, the paper focuses on some sessions where the seniors carried out interactions with a Wizard of Oz driven, simulated system. A coaching strategy based on the GROW model was used throughout these sessions so as to guide interactions and engage the elderly with the goals of the project. In this interaction framework, both the human and the system behavior were analyzed. The way the wizard implements the GROW coaching strategy is a key aspect of the system behavior during the interaction. The language used by the virtual agent as well as his or her physical aspect are also important cues that were analyzed. Regarding the user behavior, the vocal communication provides information about the speaker’s emotional status, that is closely related to human behavior and which can be extracted from the speech and language analysis. In the same way, the analysis of the facial expression, gazes and gestures can provide information on the non verbal human communication even when the user is not talking. In addition, in order to engage senior users, their preferences and likes had to be considered. To this end, the effect of the VC on the users was gathered by means of direct questionnaires. These analyses have shown a positive and calm behavior of users when interacting with the simulated virtual coach as well as some difficulties of the system to develop the proposed coaching strategy.
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Notes HuPBA; no proj Approved no
Call Number Admin @ si @ JLP2020 Serial 3443
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Author David Berga; Xavier Otazu
Title Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1 Type Journal Article
Year (up) 2020 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume 417 Issue Pages 270-289
Keywords
Abstract Previous studies suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return, oculomotor and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks. Results show that our model outpeforms other biologically inspired models of saliency prediction while predicting visual saccade sequences with the same model. We also show how temporal and spatial characteristics of saccade amplitude and inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts.
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Notes NEUROBIT Approved no
Call Number Admin @ si @ BeO2020c Serial 3444
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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 (up) 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 (up) 2020 Publication 4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence Abbreviated Journal
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Address Virtual; December 2020
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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 Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados
Title A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages Type Journal Article
Year (up) 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 136 Issue Pages 219-227
Keywords
Abstract In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks.
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Notes DAG; 600.140; 601.311; 600.121 Approved no
Call Number Admin @ si @ CFV2020 Serial 3451
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Author Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez
Title SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities Type Journal Article
Year (up) 2020 Publication Sensors Abbreviated Journal SENS
Volume 20 Issue 16 Pages 4587
Keywords
Abstract This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included.
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Notes MSIAU; 600.130; 601.349; 600.122 Approved no
Call Number Admin @ si @ MSM2020 Serial 3452
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Author B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols
Title Knowledge graph based methods for record linkage Type Journal Article
Year (up) 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 136 Issue Pages 127-133
Keywords
Abstract Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.

In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results.
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Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ GRP2020 Serial 3453
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Author Fernando Vilariño
Title Unveiling the Social Impact of AI Type Conference Article
Year (up) 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 (up) 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 (up) 2020 Publication 31st British Machine Vision Conference Abbreviated Journal
Volume Issue Pages
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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
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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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