| Records |
| Author |
Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca |
| Title |
Integrating Vision and Language in Social Networks for Identifying Visual Patterns of Personality Traits |
Type |
Journal |
| Year |
2019 |
Publication |
International Journal of Social Science and Humanity |
Abbreviated Journal |
IJSSH |
| Volume |
9 |
Issue |
1 |
Pages |
6-12 |
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| Abstract |
Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. In this sense, user text interactions are widely used to sense the whys of certain social user’s demands and cultural- driven interests. However, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited. Following this trend on visual-based social analysis, we present a novel methodology based on neural networks to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So, the key contribution in this work is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between posted images and the personality estimated from their accompanying texts. Thus, the experimental results are consistent with previous cyber-psychology results based on texts, suggesting that images could also be used for personality estimation: classification results on some personality traits show that specific and characteristic visual patterns emerge, in essence representing abstract concepts. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts, and to further substitute current textual personality questionnaires by image-based ones. |
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ISE; 600.119 |
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no |
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Admin @ si @ RGG2019 |
Serial |
3414 |
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| Author |
Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca |
| Title |
Towards Visual Personality Questionnaires based on Deep Learning and Social Media |
Type |
Conference Article |
| Year |
2019 |
Publication |
21st International Conference on Social Influence and Social Psychology |
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April 2019; Tokio; Japan |
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ICSISP |
| Notes |
ISE; 600.119 |
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no |
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Admin @ si @ RGG2020 |
Serial |
3554 |
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| Author |
Pau Rodriguez; Guillem Cucurull; Jordi Gonzalez; Josep M. Gonfaus; Kamal Nasrollahi; Thomas B. Moeslund; Xavier Roca |
| Title |
Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification |
Type |
Journal Article |
| Year |
2017 |
Publication |
IEEE Transactions on cybernetics |
Abbreviated Journal |
Cyber |
| Volume |
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Issue |
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Pages |
1-11 |
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| Abstract |
Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data. As a baseline, our approach first uses convolutional neural networks (CNNs) to learn facial features from VGG_Faces, which are then linked to a long short-term memory to exploit the temporal relation between video frames. We further compare the performances of using the so popular schema based on the canonically normalized appearance versus taking into account the whole image. As a result, we outperform current state-of-the-art area under the curve performance in the UNBC-McMaster Shoulder Pain Expression Archive Database. In addition, to evaluate the generalization properties of our proposed methodology on facial motion recognition, we also report competitive results in the Cohn Kanade+ facial expression database. |
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ISE; 600.119; 600.098 |
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no |
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Admin @ si @ RCG2017a |
Serial |
2926 |
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Parichehr Behjati Ardakani; Pau Rodriguez; Armin Mehri; Isabelle Hupont; Carles Fernandez; Jordi Gonzalez |
| Title |
OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
2693-2702 |
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Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the depth and width of the networks increase, CNN-based SR methods have been faced with the challenge of computational complexity in practice. More- over, most SR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. To address these limitations we introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We make the following contributions: first, we introduce a lightweight feature extractor that enforces efficient reuse of information through a novel recursive structure of skip and dense connections. Second, to maximize the performance of the feature extractor, we propose a model agnostic reconstruction module that generates accurate high-resolution images from overscaled feature maps obtained from any SR architecture. Third, we introduce a multi-scale loss function to achieve generalization across scales. Experiments show that our proposal outperforms previous state-of-the-art approaches in standard benchmarks, while maintaining relatively low computation and memory requirements. |
| Address |
Virtual; January 2021 |
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WACV |
| Notes |
ISE; 600.119; 600.098 |
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no |
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Admin @ si @ BRM2021 |
Serial |
3512 |
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| Author |
Pau Rodriguez; Diego Velazquez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez |
| Title |
Pay attention to the activations: a modular attention mechanism for fine-grained image recognition |
Type |
Journal Article |
| Year |
2020 |
Publication |
IEEE Transactions on Multimedia |
Abbreviated Journal |
TMM |
| Volume |
22 |
Issue |
2 |
Pages |
502-514 |
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| Abstract |
Fine-grained image recognition is central to many multimedia tasks such as search, retrieval, and captioning. Unfortunately, these tasks are still challenging since the appearance of samples of the same class can be more different than those from different classes. This issue is mainly due to changes in deformation, pose, and the presence of clutter. In the literature, attention has been one of the most successful strategies to handle the aforementioned problems. Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification. In contrast, in this paper, attention is not applied at the image level but to the convolutional feature activations. In essence, with our approach, the neural model learns to attend to lower-level feature activations without requiring part annotations and uses those activations to update and rectify the output likelihood distribution. The proposed mechanism is modular, architecture-independent, and efficient in terms of both parameters and computation required. Experiments demonstrate that well-known networks such as wide residual networks and ResNeXt, when augmented with our approach, systematically improve their classification accuracy and become more robust to changes in deformation and pose and to the presence of clutter. As a result, our proposal reaches state-of-the-art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford Dogs, and UEC-Food100 while obtaining competitive performance in ImageNet, CIFAR-100, CUB200 Birds, and Stanford Cars. In addition, we analyze the different components of our model, showing that the proposed attention modules succeed in finding the most discriminative regions of the image. Finally, as a proof of concept, we demonstrate that with only local predictions, an augmented neural network can successfully classify an image before reaching any fully connected layer, thus reducing the computational amount up to 10%. |
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| Notes |
ISE; 600.119; 600.098 |
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no |
| Call Number |
Admin @ si @ RVC2020a |
Serial |
3417 |
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| Author |
Wenjuan Gong; Zhang Yue; Wei Wang; Cheng Peng; Jordi Gonzalez |
| Title |
Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition |
Type |
Journal Article |
| Year |
2022 |
Publication |
ACM Transactions on Multimedia Computing, Communications, and Applications |
Abbreviated Journal |
ACMTMC |
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Issue |
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Pages |
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Feature Fusion; Model Fusion; Meta-Learning; Micro-Expression Recognition |
| Abstract |
Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
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May 2022 |
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| Notes |
ISE; 600.157 |
Approved |
no |
| Call Number |
Admin @ si @ GYW2022 |
Serial |
3692 |
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| Author |
Ana Garcia Rodriguez; Yael Tudela; Henry Cordova; S. Carballal; I. Ordas; L. Moreira; E. Vaquero; O. Ortiz; L. Rivero; F. Javier Sanchez; Miriam Cuatrecasas; Maria Pellise; Jorge Bernal; Gloria Fernandez Esparrach |
| Title |
In vivo computer-aided diagnosis of colorectal polyps using white light endoscopy |
Type |
Journal Article |
| Year |
2022 |
Publication |
Endoscopy International Open |
Abbreviated Journal |
ENDIO |
| Volume |
10 |
Issue |
9 |
Pages |
E1201-E1207 |
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| Abstract |
Background and study aims Artificial intelligence is currently able to accurately predict the histology of colorectal polyps. However, systems developed to date use complex optical technologies and have not been tested in vivo. The objective of this study was to evaluate the efficacy of a new deep learning-based optical diagnosis system, ATENEA, in a real clinical setting using only high-definition white light endoscopy (WLE) and to compare its performance with endoscopists. Methods ATENEA was prospectively tested in real life on consecutive polyps detected in colorectal cancer screening colonoscopies at Hospital Clínic. No images were discarded, and only WLE was used. The in vivo ATENEA's prediction (adenoma vs non-adenoma) was compared with the prediction of four staff endoscopists without specific training in optical diagnosis for the study purposes. Endoscopists were blind to the ATENEA output. Histology was the gold standard. Results Ninety polyps (median size: 5 mm, range: 2-25) from 31 patients were included of which 69 (76.7 %) were adenomas. ATENEA correctly predicted the histology in 63 of 69 (91.3 %, 95 % CI: 82 %-97 %) adenomas and 12 of 21 (57.1 %, 95 % CI: 34 %-78 %) non-adenomas while endoscopists made correct predictions in 52 of 69 (75.4 %, 95 % CI: 60 %-85 %) and 20 of 21 (95.2 %, 95 % CI: 76 %-100 %), respectively. The global accuracy was 83.3 % (95 % CI: 74%-90 %) and 80 % (95 % CI: 70 %-88 %) for ATENEA and endoscopists, respectively. Conclusion ATENEA can accurately be used for in vivo characterization of colorectal polyps, enabling the endoscopist to make direct decisions. ATENEA showed a global accuracy similar to that of endoscopists despite an unsatisfactory performance for non-adenomatous lesions. |
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2022 Sep 14 |
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PMID |
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ISE; 600.157 |
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no |
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Admin @ si @ GTC2022b |
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3752 |
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| Author |
Yecong Wan; Yuanshuo Cheng; Miingwen Shao; Jordi Gonzalez |
| Title |
Image rain removal and illumination enhancement done in one go |
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Journal Article |
| Year |
2022 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
| Volume |
252 |
Issue |
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Pages |
109244 |
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Rain removal plays an important role in the restoration of degraded images. Recently, CNN-based methods have achieved remarkable success. However, these approaches neglect that the appearance of real-world rain is often accompanied by low light conditions, which will further degrade the image quality, thereby hindering the restoration mission. Therefore, it is very indispensable to jointly remove the rain and enhance illumination for real-world rain image restoration. To this end, we proposed a novel spatially-adaptive network, dubbed SANet, which can remove the rain and enhance illumination in one go with the guidance of degradation mask. Meanwhile, to fully utilize negative samples, a contrastive loss is proposed to preserve more natural textures and consistent illumination. In addition, we present a new synthetic dataset, named DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates real-world rainfall scenarios. SANet is extensively evaluated on the proposed dataset and attains new state-of-the-art performance against other combining methods. Moreover, after a simple transformation, our SANet surpasses existing the state-of-the-art algorithms in both rain removal and low-light image enhancement. |
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Sept 2022 |
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Elsevier |
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| Notes |
ISE; 600.157; 600.168 |
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no |
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Admin @ si @ WCS2022 |
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3744 |
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Marco Pedersoli; Jordi Gonzalez; Xu Hu; Xavier Roca |
| Title |
Toward Real-Time Pedestrian Detection Based on a Deformable Template Model |
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Journal Article |
| Year |
2014 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
| Volume |
15 |
Issue |
1 |
Pages |
355-364 |
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Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed. |
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1524-9050 |
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ISE; 601.213; 600.078 |
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no |
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PGH2014 |
Serial |
2350 |
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Pau Rodriguez; Jordi Gonzalez; Jordi Cucurull; Josep M. Gonfaus; Xavier Roca |
| Title |
Regularizing CNNs with Locally Constrained Decorrelations |
Type |
Conference Article |
| Year |
2017 |
Publication |
5th International Conference on Learning Representations |
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Toulon; France; April 2017 |
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ICLR |
| Notes |
ISE; 602.143; 600.119; 600.098 |
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no |
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Admin @ si @ RGC2017 |
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2927 |
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| Author |
Francisco Javier Orozco; Ognjen Rudovic; Jordi Gonzalez; Maja Pantic |
| Title |
Hierarchical On-line Appearance-Based Tracking for 3D Head Pose, Eyebrows, Lips, Eyelids and Irises |
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Journal Article |
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2013 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
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31 |
Issue |
4 |
Pages |
322-340 |
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On-line appearance models; Levenberg–Marquardt algorithm; Line-search optimization; 3D face tracking; Facial action tracking; Eyelid tracking; Iris tracking |
| Abstract |
In this paper, we propose an On-line Appearance-Based Tracker (OABT) for simultaneous tracking of 3D head pose, lips, eyebrows, eyelids and irises in monocular video sequences. In contrast to previously proposed tracking approaches, which deal with face and gaze tracking separately, our OABT can also be used for eyelid and iris tracking, as well as 3D head pose, lips and eyebrows facial actions tracking. Furthermore, our approach applies an on-line learning of changes in the appearance of the tracked target. Hence, the prior training of appearance models, which usually requires a large amount of labeled facial images, is avoided. Moreover, the proposed method is built upon a hierarchical combination of three OABTs, which are optimized using a Levenberg–Marquardt Algorithm (LMA) enhanced with line-search procedures. This, in turn, makes the proposed method robust to changes in lighting conditions, occlusions and translucent textures, as evidenced by our experiments. Finally, the proposed method achieves head and facial actions tracking in real-time. |
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Elsevier |
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ISE; 605.203; 302.012; 302.018; 600.049 |
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ORG2013 |
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2221 |
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Carles Fernandez; Jordi Gonzalez; Joao Manuel R. S. Taveres; Xavier Roca |
| Title |
Towards Ontological Cognitive System |
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Book Chapter |
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2013 |
Publication |
Topics in Medical Image Processing and Computational Vision |
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8 |
Issue |
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87-99 |
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The increasing ubiquitousness of digital information in our daily lives has positioned video as a favored information vehicle, and given rise to an astonishing generation of social media and surveillance footage. This raises a series of technological demands for automatic video understanding and management, which together with the compromising attentional limitations of human operators, have motivated the research community to guide its steps towards a better attainment of such capabilities. As a result, current trends on cognitive vision promise to recognize complex events and self-adapt to different environments, while managing and integrating several types of knowledge. Future directions suggest to reinforce the multi-modal fusion of information sources and the communication with end-users. |
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Springer Netherlands |
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2212-9391 |
ISBN |
978-94-007-0725-2 |
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| Notes |
ISE; 605.203; 302.018; 600.049 |
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no |
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Admin @ si @ FGT2013 |
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2287 |
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Marc Castello; Jordi Gonzalez; Ariel Amato; Pau Baiget; Carles Fernandez; Josep M. Gonfaus; Ramon Mollineda; Marco Pedersoli; Nicolas Perez de la Blanca; Xavier Roca |
| Title |
Exploiting Multimodal Interaction Techniques for Video-Surveillance |
Type |
Book Chapter |
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2013 |
Publication |
Multimodal Interaction in Image and Video Applications Intelligent Systems Reference Library |
Abbreviated Journal |
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48 |
Issue |
8 |
Pages |
135-151 |
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In this paper we present an example of a video surveillance application that exploits Multimodal Interactive (MI) technologies. The main objective of the so-called VID-Hum prototype was to develop a cognitive artificial system for both the detection and description of a particular set of human behaviours arising from real-world events. The main procedure of the prototype described in this chapter entails: (i) adaptation, since the system adapts itself to the most common behaviours (qualitative data) inferred from tracking (quantitative data) thus being able to recognize abnormal behaviors; (ii) feedback, since an advanced interface based on Natural Language understanding allows end-users the communicationwith the prototype by means of conceptual sentences; and (iii) multimodality, since a virtual avatar has been designed to describe what is happening in the scene, based on those textual interpretations generated by the prototype. Thus, the MI methodology has provided an adequate framework for all these cooperating processes. |
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Springer Berlin Heidelberg |
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1868-4394 |
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978-3-642-35931-6 |
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ISE; 605.203; 600.049 |
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CGA2013 |
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2222 |
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Ariel Amato; Ivan Huerta; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez |
| Title |
Moving Cast Shadows Detection Methods for Video Surveillance Applications |
Type |
Book Chapter |
| Year |
2014 |
Publication |
Augmented Vision and Reality |
Abbreviated Journal |
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| Volume |
6 |
Issue |
|
Pages |
23-47 |
| Keywords |
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| Abstract |
Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (‘shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows). |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2190-5916 |
ISBN |
978-3-642-37840-9 |
Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ISE; 605.203; 600.049; 302.018; 302.012; 600.078 |
Approved |
no |
| Call Number |
Admin @ si @ AHM2014 |
Serial |
2223 |
| Permanent link to this record |
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| Author |
Ariel Amato; Felipe Lumbreras; Angel Sappa |
| Title |
A General-purpose Crowdsourcing Platform for Mobile Devices |
Type |
Conference Article |
| Year |
2014 |
Publication |
9th International Conference on Computer Vision Theory and Applications |
Abbreviated Journal |
|
| Volume |
3 |
Issue |
|
Pages |
211-215 |
| Keywords |
Crowdsourcing Platform; Mobile Crowdsourcing |
| Abstract |
This paper presents details of a general purpose micro-task on-demand platform based on the crowdsourcing philosophy. This platform was specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquity and iii) embedded sensors. The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks. Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and tasksolver). Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way. Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications. Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform. |
| Address |
Lisboa; Portugal; January 2014 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
VISAPP |
| Notes |
ISE; ADAS; 600.054; 600.055; 600.076; 600.078 |
Approved |
no |
| Call Number |
Admin @ si @ ALS2014 |
Serial |
2478 |
| Permanent link to this record |
