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| Author | Sergio Alloza; Flavio Escribano; Sergi Delgado; Ciprian Corneanu; Sergio Escalera | ||||
| Title | XBadges. Identifying and training soft skills with commercial video games Improving persistence, risk taking & spatial reasoning with commercial video games and facial and emotional recognition system | Type | Conference Article | ||
| Year | 2017 | Publication | 4th Congreso de la Sociedad Española para las Ciencias del Videojuego | Abbreviated Journal | |
| Volume | 1957 | Issue | Pages | 13-28 | |
| Keywords | Video Games; Soft Skills; Training; Skilling Development; Emotions; Cognitive Abilities; Flappy Bird; Pacman; Tetris | ||||
Abstract  |
XBadges is a research project based on the hypothesis that commercial video games (nonserious games) can train soft skills. We measure persistence, patial reasoning and risk taking before and after subjects paticipate in controlled game playing sessions.
In addition, we have developed an automatic facial expression recognition system capable of inferring their emotions while playing, allowing us to study the role of emotions in soft skills acquisition. We have used Flappy Bird, Pacman and Tetris for assessing changes in persistence, risk taking and spatial reasoning respectively. Results show how playing Tetris significantly improves spatial reasoning and how playing Pacman significantly improves prudence in certain areas of behavior. As for emotions, they reveal that being concentrated helps to improve performance and skills acquisition. Frustration is also shown as a key element. With the results obtained we are able to glimpse multiple applications in areas which need soft skills development. |
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| Address | Barcelona; June 2017 | ||||
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| Area | Expedition | Conference | COSECIVI; CEUR-WS | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ AED2017 | Serial | 3065 | ||
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| Author | Mariella Dimiccoli; Marc Bolaños; Estefania Talavera; Maedeh Aghaei; Stavri G. Nikolov; Petia Radeva | ||||
| Title | SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation | Type | Journal Article | ||
| Year | 2017 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 155 | Issue | Pages | 55-69 | |
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| Abstract |
While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods. | ||||
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| Notes | MILAB; 601.235 | Approved | no | ||
| Call Number | Admin @ si @ DBT2017 | Serial | 2714 | ||
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| Author | Hana Jarraya; Oriol Ramos Terrades; Josep Llados | ||||
| Title | Learning structural loss parameters on graph embedding applied on symbolic graphs | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
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| Abstract |
We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset. | ||||
| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ JRL2017b | Serial | 3073 | ||
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| Author | Victor Vaquero; German Ros; Francesc Moreno-Noguer; Antonio Lopez; Alberto Sanfeliu | ||||
| Title | Joint coarse-and-fine reasoning for deep optical flow | Type | Conference Article | ||
| Year | 2017 | Publication | 24th International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 2558-2562 | ||
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| Abstract |
We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets. | ||||
| Address | Beijing; China; September 2017 | ||||
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| Area | Expedition | Conference | ICIP | ||
| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ VRM2017 | Serial | 2898 | ||
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| Author | Xialei Liu; Joost Van de Weijer; Andrew Bagdanov | ||||
| Title | RankIQA: Learning from Rankings for No-reference Image Quality Assessment | Type | Conference Article | ||
| Year | 2017 | Publication | 17th IEEE International Conference on Computer Vision | Abbreviated Journal | |
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| 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. | ||||
| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCV | ||
| Notes | LAMP; 600.106; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LWB2017b | Serial | 3036 | ||
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| Author | Hana Jarraya; Oriol Ramos Terrades; Josep Llados | ||||
| Title | Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs | Type | Conference Article | ||
| Year | 2017 | Publication | 8th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines | ||||
| Abstract |
We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction. | ||||
| Address | Faro; Portugal; June 2017 | ||||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ JRL2017a | Serial | 2953 | ||
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| Author | Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun | ||||
| Title | CARLA: An Open Urban Driving Simulator | Type | Conference Article | ||
| Year | 2017 | Publication | 1st Annual Conference on Robot Learning. Proceedings of Machine Learning | Abbreviated Journal | |
| Volume | 78 | Issue | Pages | 1-16 | |
| Keywords | Autonomous driving; sensorimotor control; simulation | ||||
| Abstract |
We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via reinforcement learning. The approaches are evaluated in controlled scenarios of increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research. |
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| Address | Mountain View; CA; USA; November 2017 | ||||
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| Area | Expedition | Conference | CORL | ||
| Notes | ADAS; 600.085; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DRC2017 | Serial | 2988 | ||
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| Author | Chirster Loob; Pejman Rasti; Iiris Lusi; Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera; Tomasz Sapinski; Dorota Kaminska; Gholamreza Anbarjafari | ||||
| Title | Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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| Abstract |
We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions. | ||||
| Address | Washington; DC; USA; May 2017 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ LRL2017 | Serial | 2925 | ||
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| Author | Jun Wan; Sergio Escalera; Gholamreza Anbarjafari; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon; Meysam Madadi; Juri Allik; Jelena Gorbova; Chi Lin; Yiliang Xie | ||||
| Title | Results and Analysis of ChaLearn LAP Multi-modal Isolated and ContinuousGesture Recognition, and Real versus Fake Expressed Emotions Challenges | Type | Conference Article | ||
| Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
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| Abstract |
We analyze the results of the 2017 ChaLearn Looking at People Challenge at ICCV. The challenge comprised three tracks: (1) large-scale isolated (2) continuous gesture recognition, and (3) real versus fake expressed emotions tracks. It is the second round for both gesture recognition challenges, which were held first in the context of the ICPR 2016 workshop on “multimedia challenges beyond visual analysis”. In this second round, more participants joined the competitions, and the performances considerably improved compared to the first round. Particularly, the best recognition accuracy of isolated gesture recognition has improved from 56.90% to 67.71% in the IsoGD test set, and Mean Jaccard Index (MJI) of continuous gesture recognition has improved from 0.2869 to 0.6103 in the ConGD test set. The third track is the first challenge on real versus fake expressed emotion classification, including six emotion categories, for which a novel database was introduced. The first place was shared between two teams who achieved 67.70% averaged recognition rate on the test set. The data of the three tracks, the participants' code and method descriptions are publicly available to allow researchers to keep making progress in the field. | ||||
| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ WEA2017 | Serial | 3066 | ||
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| Author | Ivet Rafegas | ||||
| Title | Color in Visual Recognition: from flat to deep representations and some biological parallelisms | Type | Book Whole | ||
| Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract |
Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.
Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations. Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias. |
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| Address | November 2017 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Maria Vanrell | |
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| ISSN | ISBN | 978-84-945373-7-0 | Medium | ||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ Raf2017 | Serial | 3100 | ||
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| Author | Marc Bolaños; Mariella Dimiccoli; Petia Radeva | ||||
| Title | Towards Storytelling from Visual Lifelogging: An Overview | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Human-Machine Systems | Abbreviated Journal | THMS |
| Volume | 47 | Issue | 1 | Pages | 77 - 90 |
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| Abstract |
Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging. |
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| Notes | MILAB; 601.235 | Approved | no | ||
| Call Number | Admin @ si @ BDR2017 | Serial | 2712 | ||
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| Author | German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez | ||||
| Title | Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA | Type | Book Chapter | ||
| Year | 2017 | Publication | Domain Adaptation in Computer Vision Applications | Abbreviated Journal | |
| Volume | 12 | Issue | Pages | 227-241 | |
| Keywords | SYNTHIA; Virtual worlds; Autonomous Driving | ||||
| Abstract |
Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. | ||||
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| Publisher | Springer | Place of Publication | Editor | Gabriela Csurka | |
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| Notes | ADAS; 600.085; 600.082; 600.076; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ RSV2017 | Serial | 2882 | ||
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| Author | Sergio Escalera; Vassilis Athitsos; Isabelle Guyon | ||||
| Title | Challenges in Multi-modal Gesture Recognition | Type | Book Chapter | ||
| Year | 2017 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | 1-60 | ||
| Keywords | Gesture recognition; Time series analysis; Multimodal data analysis; Computer vision; Pattern recognition; Wearable sensors; Infrared cameras; Kinect TMTM | ||||
| Abstract |
This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011–2015. We began right at the start of the Kinect TMTM revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ EAG2017 | Serial | 3008 | ||
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| Author | Sergio Escalera; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon | ||||
| Title | ChaLearn Looking at People: A Review of Events and Resources | Type | Conference Article | ||
| Year | 2017 | Publication | 30th International Joint Conference on Neural Networks | Abbreviated Journal | |
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| Abstract |
This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities. | ||||
| Address | Anchorage; Alaska; USA; May 2017 | ||||
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| Area | Expedition | Conference | IJCNN | ||
| Notes | HuPBA; 602.143 | Approved | no | ||
| Call Number | Admin @ si @ EBE2017 | Serial | 3012 | ||
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| Author | Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Julio C. S. Jacques Junior; Xavier Baro; Evelyne Viegas; Yagmur Gucluturk; Umut Guclu; Marcel A. J. van Gerven; Rob van Lier; Meysam Madadi; Stephane Ayache | ||||
| Title | Design of an Explainable Machine Learning Challenge for Video Interviews | Type | Conference Article | ||
| Year | 2017 | Publication | International Joint Conference on Neural Networks | Abbreviated Journal | |
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| Abstract |
This paper reviews and discusses research advances on “explainable machine learning” in computer vision. We focus on a particular area of the “Looking at People” (LAP) thematic domain: first impressions and personality analysis. Our aim is to make the computational intelligence and computer vision communities aware of the importance of developing explanatory mechanisms for computer-assisted decision making applications, such as automating recruitment. Judgments based on personality traits are being made routinely by human resource departments to evaluate the candidates' capacity of social insertion and their potential of career growth. However, inferring personality traits and, in general, the process by which we humans form a first impression of people, is highly subjective and may be biased. Previous studies have demonstrated that learning machines can learn to mimic human decisions. In this paper, we go one step further and formulate the problem of explaining the decisions of the models as a means of identifying what visual aspects are important, understanding how they relate to decisions suggested, and possibly gaining insight into undesirable negative biases. We design a new challenge on explainability of learning machines for first impressions analysis. We describe the setting, scenario, evaluation metrics and preliminary outcomes of the competition. To the best of our knowledge this is the first effort in terms of challenges for explainability in computer vision. In addition our challenge design comprises several other quantitative and qualitative elements of novelty, including a “coopetition” setting, which combines competition and collaboration. | ||||
| Address | Anchorage; Alaska; USA; May 2017 | ||||
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| Area | Expedition | Conference | IJCNN | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ EGE2017 | Serial | 2922 | ||
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