Maedeh Aghaei, Mariella Dimiccoli, C. Canton-Ferrer, & Petia Radeva. (2018). Towards social pattern characterization from egocentric photo-streams. CVIU - Computer Vision and Image Understanding, 171, 104–117.
Abstract: Following the increasingly popular trend of social interaction analysis in egocentric vision, this article presents a comprehensive pipeline for automatic social pattern characterization of a wearable photo-camera user. The proposed framework relies merely on the visual analysis of egocentric photo-streams and consists of three major steps. The first step is to detect social interactions of the user where the impact of several social signals on the task is explored. The detected social events are inspected in the second step for categorization into different social meetings. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task; finally, LSTM is employed to classify the time-series. The last step of the framework is to characterize social patterns of the user. Our goal is to quantify the duration, the diversity and the frequency of the user social relations in various social situations. This goal is achieved by the discovery of recurrences of the same people across the whole set of social events related to the user. Experimental evaluation over EgoSocialStyle – the proposed dataset in this work, and EGO-GROUP demonstrates promising results on the task of social pattern characterization from egocentric photo-streams.
Keywords: Social pattern characterization; Social signal extraction; Lifelogging; Convolutional and recurrent neural networks
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Alejandro Cartas, Mariella Dimiccoli, & Petia Radeva. (2017). Batch-based activity recognition from egocentric photo-streams. In 1st International workshop on Egocentric Perception, Interaction and Computing.
Abstract: Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries.
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Aniol Lidon, Marc Bolaños, Mariella Dimiccoli, Petia Radeva, Maite Garolera, & Xavier Giro. (2017). Semantic Summarization of Egocentric Photo-Stream Events. In 2nd Workshop on Lifelogging Tools and Applications.
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Maedeh Aghaei, Mariella Dimiccoli, & Petia Radeva. (2017). All the people around me: face clustering in egocentric photo streams. In 24th International Conference on Image Processing.
Abstract: arxiv1703.01790
Given an unconstrained stream of images captured by a wearable photo-camera (2fpm), we propose an unsupervised bottom-up approach for automatic clustering appearing faces into the individual identities present in these data. The problem is challenging since images are acquired under real world conditions; hence the visible appearance of the people in the images undergoes intensive variations. Our proposed pipeline consists of first arranging the photo-stream into events, later, localizing the appearance of multiple people in them, and
finally, grouping various appearances of the same person across different events. Experimental results performed on a dataset acquired by wearing a photo-camera during one month, demonstrate the effectiveness of the proposed approach for the considered purpose.
Keywords: face discovery; face clustering; deepmatching; bag-of-tracklets; egocentric photo-streams
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Mireia Forns-Nadal, Federico Sem, Anna Mane, Laura Igual, Dani Guinart, & Oscar Vilarroya. (2017). Increased Nucleus Accumbens Volume in First-Episode Psychosis. PRN - Psychiatry Research-Neuroimaging, 263, 57–60.
Abstract: Nucleus accumbens has been reported as a key structure in the neurobiology of schizophrenia. Studies analyzing structural abnormalities have shown conflicting results, possibly related to confounding factors. We investigated the nucleus accumbens volume using manual delimitation in first-episode psychosis (FEP) controlling for age, cannabis use and medication. Thirty-one FEP subjects who were naive or minimally exposed to antipsychotics and a control group were MRI scanned and clinically assessed from baseline to 6 months of follow-up. FEP showed increased relative and total accumbens volumes. Clinical correlations with negative symptoms, duration of untreated psychosis and cannabis use were not significant.
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Fernando Vilariño, & Dan Norton. (2017). Using mutimedia tools to spread poetry collections. In Internet librarian International Conference.
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Fernando Vilariño. (2017). Citizen experience as a powerful communication tool: Open Innovation and the role of Living Labs in EU. In European Conference of Science Journalists.
Abstract: The Open Innovation 2.0 model spearheaded by the European Commission introduces conceptual changes in how innovation processes should be developed. The notion of an innovation ecosystem, and the active participation of the citizens (and all the different actors of the quadruple helix) in innovation processes, opens up new channels for scientific communication, where the citizens (and all actors) can be naturally reached and facilitate the spread of the scientific message in their communities. Unleashing the power of such mechanisms, while maintaining control over the scientific communication done through such channels presents an opportunity and a challenge at the same time.
This workshop will look into key concepts that the Open Innovation 2.0 EU model introduces, and what new opportunities for communication they bring about. Specifically, we will focus on Living Labs, as a key instrument for implementing this innovation model at the regional level, and their potential in creating scientific dissemination spaces.
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Fernando Vilariño. (2017). Bringing and keeping all the stakeholders together: creating a catalog of models of governance for innovation.
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Marc Masana, Joost Van de Weijer, Luis Herranz, Andrew Bagdanov, & Jose Manuel Alvarez. (2017). Domain-adaptive deep network compression. In 17th IEEE International Conference on Computer Vision.
Abstract: Deep Neural Networks trained on large datasets can be easily transferred to new domains with far fewer labeled examples by a process called fine-tuning. This has the advantage that representations learned in the large source domain can be exploited on smaller target domains. However, networks designed to be optimal for the source task are often prohibitively large for the target task. In this work we address the compression of networks after domain transfer.
We focus on compression algorithms based on low-rank matrix decomposition. Existing methods base compression solely on learned network weights and ignore the statistics of network activations. We show that domain transfer leads to large shifts in network activations and that it is desirable to take this into account when compressing.
We demonstrate that considering activation statistics when compressing weights leads to a rank-constrained regression problem with a closed-form solution. Because our method takes into account the target domain, it can more optimally
remove the redundancy in the weights. Experiments show that our Domain Adaptive Low Rank (DALR) method significantly outperforms existing low-rank compression techniques. With our approach, the fc6 layer of VGG19 can be compressed more than 4x more than using truncated SVD alone – with only a minor or no loss in accuracy. When applied to domain-transferred networks it allows for compression down to only 5-20% of the original number of parameters with only a minor drop in performance.
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Xialei Liu, Joost Van de Weijer, & Andrew Bagdanov. (2017). RankIQA: Learning from Rankings for No-reference Image Quality Assessment. In 17th IEEE International Conference on Computer Vision.
Abstract: We propose a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA). To address the problem of limited IQA dataset size, we train a Siamese Network to rank images in terms of image quality by using synthetically generated distortions for which relative image quality is known. These ranked image sets can be automatically generated without laborious human labeling. We then use fine-tuning to transfer the knowledge represented in the trained Siamese Network to a traditional CNN that estimates absolute image quality from single images. We demonstrate how our approach can be made significantly more efficient than traditional Siamese Networks by forward propagating a batch of images through a single network and backpropagating gradients derived from all pairs of images in the batch. Experiments on the TID2013 benchmark show that we improve the state-of-the-art by over 5%. Furthermore, on the LIVE benchmark we show that our approach is superior to existing NR-IQA techniques and that we even outperform the state-of-the-art in full-reference IQA (FR-IQA) methods without having to resort to high-quality reference images to infer IQA.
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Rada Deeb, Damien Muselet, Mathieu Hebert, Alain Tremeau, & Joost Van de Weijer. (2017). 3D color charts for camera spectral sensitivity estimation. In 28th British Machine Vision Conference.
Abstract: Estimating spectral data such as camera sensor responses or illuminant spectral power distribution from raw RGB camera outputs is crucial in many computer vision applications.
Usually, 2D color charts with various patches of known spectral reflectance are
used as reference for such purpose. Deducing n-D spectral data (n»3) from 3D RGB inputs is an ill-posed problem that requires a high number of inputs. Unfortunately, most of the natural color surfaces have spectral reflectances that are well described by low-dimensional linear models, i.e. each spectral reflectance can be approximated by a weighted sum of the others. It has been shown that adding patches to color charts does not help in practice, because the information they add is redundant with the information provided by the first set of patches. In this paper, we propose to use spectral data of
higher dimensionality by using 3D color charts that create inter-reflections between the surfaces. These inter-reflections produce multiplications between natural spectral curves and so provide non-linear spectral curves. We show that such data provide enough information for accurate spectral data estimation.
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Muhammad Anwer Rao, Fahad Shahbaz Khan, Joost Van de Weijer, & Jorma Laaksonen. (2017). Tex-Nets: Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition. In 19th International Conference on Multimodal Interaction.
Abstract: Recognizing materials and textures in realistic imaging conditions is a challenging computer vision problem. For many years, local features based orderless representations were a dominant approach for texture recognition. Recently deep local features, extracted from the intermediate layers of a Convolutional Neural Network (CNN), are used as filter banks. These dense local descriptors from a deep model, when encoded with Fisher Vectors, have shown to provide excellent results for texture recognition. The CNN models, employed in such approaches, take RGB patches as input and train on a large amount of labeled images. We show that CNN models, which we call TEX-Nets, trained using mapped coded images with explicit texture information provide complementary information to the standard deep models trained on RGB patches. We further investigate two deep architectures, namely early and late fusion, to combine the texture and color information. Experiments on benchmark texture datasets clearly demonstrate that TEX-Nets provide complementary information to standard RGB deep network. Our approach provides a large gain of 4.8%, 3.5%, 2.6% and 4.1% respectively in accuracy on the DTD, KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets, compared to the standard RGB network of the same architecture. Further, our final combination leads to consistent improvements over the state-of-the-art on all four datasets.
Keywords: Convolutional Neural Networks; Texture Recognition; Local Binary Paterns
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Muhammad Anwer Rao, Fahad Shahbaz Khan, Joost Van de Weijer, & Jorma Laaksonen. (2017). Top-Down Deep Appearance Attention for Action Recognition. In 20th Scandinavian Conference on Image Analysis (Vol. 10269, pp. 297–309). LNCS.
Abstract: Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, we investigate the problem of fusing deep appearance and motion cues for action recognition. We propose a video representation which combines deep appearance and motion based local convolutional features within the bag-of-deep-features framework. Firstly, dense deep appearance and motion based local convolutional features are extracted from spatial (RGB) and temporal (flow) networks, respectively. Both visual cues are processed in parallel by constructing separate visual vocabularies for appearance and motion. A category-specific appearance map is then learned to modulate the weights of the deep motion features. The proposed representation is discriminative and binds the deep local convolutional features to their spatial locations. Experiments are performed on two challenging datasets: JHMDB dataset with 21 action classes and ACT dataset with 43 categories. The results clearly demonstrate that our approach outperforms both standard approaches of early and late feature fusion. Further, our approach is only employing action labels and without exploiting body part information, but achieves competitive performance compared to the state-of-the-art deep features based approaches.
Keywords: Action recognition; CNNs; Feature fusion
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N. Nayef, F. Yin, I. Bizid, H.Choi, Y. Feng, Dimosthenis Karatzas, et al. (2017). ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification – RRC-MLT. In 14th International Conference on Document Analysis and Recognition (pp. 1454–1459).
Abstract: Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge.
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Mireia Sole, Joan Blanco, Debora Gil, G. Fonseka, Richard Frodsham, Oliver Valero, et al. (2017). Análisis 3d de la territorialidad cromosómica en células espermatogénicas: explorando la infertilidad desde un nuevo prisma. ASEBIR - Revista Asociación para el Estudio de la Biología de la Reproducción, 105.
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