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| Author | Debora Gil; Antoni Rosell | ||||
| Title | Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress? | Type | Abstract | ||
| Year | 2019 | Publication | World Lung Cancer Conference | Abbreviated Journal | |
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| Abstract | Invited speaker | ||||
| Address | Barcelona; September 2019 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IASLC WCLC | ||
| Notes | IAM; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GiR2019 | Serial | 3361 | ||
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| Author | Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz | ||||
| Title | SDIT: Scalable and Diverse Cross-domain Image Translation | Type | Conference Article | ||
| Year | 2019 | Publication | 27th ACM International Conference on Multimedia | Abbreviated Journal | |
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Issue | Pages | 1267–1276 | ||
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| Abstract | Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces. | ||||
| Address | Nice; Francia; October 2019 | ||||
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| Area | Expedition | Conference | ACM-MM | ||
| Notes | LAMP; 600.106; 600.109; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WGW2019 | Serial | 3363 | ||
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| Author | Mohammed Al Rawi; Ernest Valveny | ||||
| Title | Compact and Efficient Multitask Learning in Vision, Language and Speech | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume |
Issue | Pages | 2933-2942 | ||
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| Abstract | Across-domain multitask learning is a challenging area of computer vision and machine learning due to the intra-similarities among class distributions. Addressing this problem to cope with the human cognition system by considering inter and intra-class categorization and recognition complicates the problem even further. We propose in this work an effective holistic and hierarchical learning by using a text embedding layer on top of a deep learning model. We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains. We then train the model concurrently on textual sentiment analysis, speech recognition, image classification, action recognition from video, and handwriting word spotting of two different scripts (Arabic and English). The model we propose successfully learned different tasks across multiple domains. | ||||
| Address | Seul; Korea; October 2019 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ RaV2019 | Serial | 3365 | ||
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| Author | Md. Mostafa Kamal Sarker; Syeda Furruka Banu; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Sylvie Chambon; Petia Radeva; Domenec Puig | ||||
| Title | Food Places Classification in Egocentric Images Using Siamese Neural Networks | Type | Conference Article | ||
| Year | 2019 | Publication | 22nd International Conference of the Catalan Association of Artificial Intelligence | Abbreviated Journal | |
| Volume |
Issue | Pages | 145-151 | ||
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| Abstract | Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with ‘MiniEgoFoodPlaces’ with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2. | ||||
| Address | Illes Balears; October 2019 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | CCIA | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ SBR2019 | Serial | 3368 | ||
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| Author | Emanuel Sanchez Aimar; Petia Radeva; Mariella Dimiccoli | ||||
| Title | Social Relation Recognition in Egocentric Photostreams | Type | Conference Article | ||
| Year | 2019 | Publication | 26th International Conference on Image Processing | Abbreviated Journal | |
| Volume |
Issue | Pages | 3227-3231 | ||
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| Abstract | This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental's social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal. | ||||
| Address | Taipei; Taiwan; September 2019 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ICIP | ||
| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SRD2019 | Serial | 3370 | ||
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| Author | C. Alejandro Parraga; Xavier Otazu; Arash Akbarinia | ||||
| Title | Modelling symmetry perception with banks of quadrature convolutional Gabor kernels | Type | Conference Article | ||
| Year | 2019 | Publication | 42nd edition of the European Conference on Visual Perception | Abbreviated Journal | |
| Volume |
Issue | Pages | 224-224 | ||
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| Abstract | Mirror symmetry is a property most likely to be encountered in animals than in medium scale vegetation or inanimate objects in the natural world. This might be the reason why the human visual system has evolved to detect it quickly and robustly. Indeed, the perception of symmetry assists higher-level visual processing that are crucial for survival such as target recognition and identification irrespective of position and location. Although the task of detecting symmetrical objects seems effortless to us, it is very challenging for computers (to the extent that it has been proposed as a robust “captcha” by Funk & Liu in 2016). Indeed, the exact mechanism of symmetry detection in primates is not well understood: fMRI studies have shown that symmetrical shapes activate specific higher-level areas of the visual cortex (Sasaki et al.; 2005) and similarly, a large body of psychophysical experiments suggest that the symmetry perception is critically influenced by low-level mechanisms (Treder; 2010). In this work we attempt to find plausible low-level mechanisms that might form the basis for symmetry perception. Our simple model is made from banks of (i) odd-symmetric Gabors (resembling edge-detecting V1 neurons); and (ii) banks of larger odd- and even-symmetric Gabors (resembling higher visual cortex neurons), that pool signals from the 'edge image'. As reported previously (Akbarinia et al, ECVP2017), the convolution of the symmetrical lines with the two Gabor kernels of alternative phase produces a minimum in one and a maximum in the other (Osorio; 1996), and the rectification and combination of these signals create lines which hint of mirror symmetry in natural images. We improved the algorithm by combining these signals across several spatial scales. Our preliminary results suggest that such multiscale combination of convolutional operations might form the basis for much of the operation of the HVS in terms of symmetry detection and representation. | ||||
| Address | Leuven; Belgium; August 2019 | ||||
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| Area | Expedition | Conference | ECVP | ||
| Notes | NEUROBIT; 600.128 | Approved | no | ||
| Call Number | Admin @ si @ POA2019 | Serial | 3371 | ||
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| Author | David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; Xose M. Pardo | ||||
| Title | SID4VAM: A Benchmark Dataset with Synthetic Images for Visual Attention Modeling | Type | Conference Article | ||
| Year | 2019 | Publication | 18th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume |
Issue | Pages | 8788-8797 | ||
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| Abstract | A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor popout type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-ofthe-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets. | ||||
| Address | Seul; Corea; October 2019 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ICCV | ||
| Notes | NEUROBIT; 600.128 | Approved | no | ||
| Call Number | Admin @ si @ BFO2019b | Serial | 3372 | ||
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| Author | David Berga; Xavier Otazu | ||||
| Title | Computations of inhibition of return mechanisms by modulating V1 dynamics | Type | Conference Article | ||
| Year | 2019 | Publication | 28th Annual Computational Neuroscience Meeting | Abbreviated Journal | |
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| Abstract | In this study we present a unifed model of the visual cortex for predicting visual attention using real image scenes. Feedforward mechanisms from RGC and LGN have been functionally modeled using wavelet filters at distinct orientations and scales for each chromatic pathway (Magno-, Parvo-, Konio-cellular) and polarity (ON-/OFF-center), by processing image components in the CIE Lab space. In V1, we process cortical interactions with an excitatory-inhibitory network of fring rate neurons, initially proposed by (Li, 1999), later extended by (Penacchio et al. 2013). Firing rates from model’s output have been used as predictors of neuronal activity to be projected in a map in superior colliculus (with WTA-like computations), determining locations of visual fxations. These locations will be considered as already visited areas for future saccades, therefore we integrated a spatiotemporal function of inhibition of return mechanisms (where LIP/FEF is responsible) to feed to the model with spatial memory for next saccades. Foveation mechanisms have been simulated with a cortical magnifcation function, which distort spatial viewing properties for each fxation. Results show lower prediction errors than with respect no IoR cases (Fig. 1), and it is functionally consistent with human psychophysical measurements. Our model follows a biologically-constrained architecture, previously shown to reproduce visual saliency (Berga & Otazu, 2018), visual discomfort (Penacchio et al. 2016), brightness (Penacchio et al. 2013) and chromatic induction (Cerda & Otazu, 2016). | ||||
| Address | Barcelona; July 2019 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | CNS | ||
| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number | Admin @ si @ BeO2019a | Serial | 3373 | ||
| Permanent link to this record | |||||
| Author | David Berga; Xavier Otazu | ||||
| Title | Computational modelingof visual attention: What do we know from physiology and psychophysics? | Type | Conference Article | ||
| Year | 2019 | Publication | 8th Iberian Conference on Perception | Abbreviated Journal | |
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| Abstract | Latest computer vision architectures use a chain of feedforward computations, mainly optimizing artificial neural networks for very specific tasks. Although their impressive performance (i.e. in saliency) using real image datasets, these models do not follow several biological principles of the human visual system (e.g. feedback and horizontal connections in cortex) and are unable to predict several visual tasks simultaneously. In this study we present biologically plausible computations from the early stages of the human visual system (i.e. retina and lateral geniculate nucleus) and lateral connections in V1. Despite the simplicity of these processes and without any type of training or optimization, simulations of firing-rate dynamics of V1 are able to predict bottom-up visual attention at distinct contexts (shown previously as well to predict visual discomfort, brightness and chromatic induction). We also show functional top-down selection mechanisms as feedback inhibition projections (i.e. prefrontal cortex for search/task-based attention and parietal area for inhibition of return). Distinct saliency model predictions are tested with eye tracking datasets in free-viewing and visual search tasks, using real images and synthetically-generated patterns. Results on predicting saliency and scanpaths show that artificial models do not outperform biologically-inspired ones (specifically for datasets that lack of common endogenous biases found in eye tracking experimentation), as well as, do not correctly predict contrast sensitivities in pop-out stimulus patterns. This work remarks the importance of considering biological principles of the visual system for building models that reproduce this (and any other) visual effects. | ||||
| Address | San Lorenzo El Escorial; July 2019 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | CIP | ||
| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number | Admin @ si @ BeO2019b | Serial | 3374 | ||
| Permanent link to this record | |||||
| Author | David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; Victor Leboran; Xose M. Pardo | ||||
| Title | Measuring bottom-up visual attention in eye tracking experimentation with synthetic images | Type | Conference Article | ||
| Year | 2019 | Publication | 8th Iberian Conference on Perception | Abbreviated Journal | |
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Issue | Pages | |||
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| Abstract | A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor pop-out type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-of-the-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets. | ||||
| Address | San Lorenzo El Escorial; July 2019 | ||||
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| Area | Expedition | Conference | CIP | ||
| Notes | NEUROBIT; 600.128 | Approved | no | ||
| Call Number | Admin @ si @ BFO2019c | Serial | 3375 | ||
| Permanent link to this record | |||||
| 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 | Estefania Talavera; Nicolai Petkov; Petia Radeva | ||||
| Title | Towards Unsupervised Familiar Scene Recognition in Egocentric Videos | Type | Miscellaneous | ||
| Year | 2019 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | CoRR abs/1905.04093
Nowadays, there is an upsurge of interest in using lifelogging devices. Such devices generate huge amounts of image data; consequently, the need for automatic methods for analyzing and summarizing these data is drastically increasing. We present a new method for familiar scene recognition in egocentric videos, based on background pattern detection through automatically configurable COSFIRE filters. We present some experiments over egocentric data acquired with the Narrative Clip. |
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ TPR2019b | Serial | 3379 | ||
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| Author | Estefania Talavera; Petia Radeva; Nicolai Petkov | ||||
| Title | Towards Emotion Retrieval in Egocentric PhotoStream | Type | Miscellaneous | ||
| Year | 2019 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | CoRR abs/1905.04107
The availability and use of egocentric data are rapidly increasing due to the growing use of wearable cameras. Our aim is to study the effect (positive, neutral or negative) of egocentric images or events on an observer. Given egocentric photostreams capturing the wearer's days, we propose a method that aims to assign sentiment to events extracted from egocentric photostreams. Such moments can be candidates to retrieve according to their possibility of representing a positive experience for the camera's wearer. The proposed approach obtained a classification accuracy of 75% on the test set, with a deviation of 8%. Our model makes a step forward opening the door to sentiment recognition in egocentric photostreams. |
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ TRP2019 | Serial | 3381 | ||
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| Author | Alejandro Cartas; Jordi Luque; Petia Radeva; Carlos Segura; Mariella Dimiccoli | ||||
| Title | How Much Does Audio Matter to Recognize Egocentric Object Interactions? | Type | Miscellaneous | ||
| Year | 2019 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | CoRR abs/1906.00634
Sounds are an important source of information on our daily interactions with objects. For instance, a significant amount of people can discern the temperature of water that it is being poured just by using the sense of hearing. However, only a few works have explored the use of audio for the classification of object interactions in conjunction with vision or as single modality. In this preliminary work, we propose an audio model for egocentric action recognition and explore its usefulness on the parts of the problem (noun, verb, and action classification). Our model achieves a competitive result in terms of verb classification (34.26% accuracy) on a standard benchmark with respect to vision-based state of the art systems, using a comparatively lighter architecture. |
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ CLR2019 | Serial | 3383 | ||
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| Author | Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Syeda Furruka Banu; Farhan Akram; Forhad U. H. Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig | ||||
| Title | MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network | Type | Miscellaneous | ||
| Year | 2019 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | CoRR abs/1907.00856
Skin lesion segmentation in dermoscopic images is a challenge due to their blurry and irregular boundaries. Most of the segmentation approaches based on deep learning are time and memory consuming due to the hundreds of millions of parameters. Consequently, it is difficult to apply them to real dermatoscope devices with limited GPU and memory resources. In this paper, we propose a lightweight and efficient Generative Adversarial Networks (GAN) model, called MobileGAN for skin lesion segmentation. More precisely, the MobileGAN combines 1D non-bottleneck factorization networks with position and channel attention modules in a GAN model. The proposed model is evaluated on the test dataset of the ISBI 2017 challenges and the validation dataset of ISIC 2018 challenges. Although the proposed network has only 2.35 millions of parameters, it is still comparable with the state-of-the-art. The experimental results show that our MobileGAN obtains comparable performance with an accuracy of 97.61%. |
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ MRA2019 | Serial | 3384 | ||
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