| Records |
| Author |
Ivet Rafegas; Maria Vanrell |
| Title |
Color encoding in biologically-inspired convolutional neural networks |
Type |
Journal Article |
| Year |
2018 |
Publication |
Vision Research |
Abbreviated Journal |
VR |
| Volume |
151 |
Issue |
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Pages |
7-17 |
| Keywords |
Color coding; Computer vision; Deep learning; Convolutional neural networks |
| Abstract |
Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. |
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CIC; 600.051; 600.087 |
Approved |
no |
| Call Number |
Admin @ si @RaV2018 |
Serial |
3114 |
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| Author |
Shanxin Yuan; Guillermo Garcia-Hernando; Bjorn Stenger; Gyeongsik Moon; Ju Yong Chang; Kyoung Mu Lee; Pavlo Molchanov; Jan Kautz; Sina Honari; Liuhao Ge; Junsong Yuan; Xinghao Chen; Guijin Wang; Fan Yang; Kai Akiyama; Yang Wu; Qingfu Wan; Meysam Madadi; Sergio Escalera; Shile Li; Dongheui Lee; Iason Oikonomidis; Antonis Argyros; Tae-Kyun Kim |
| Title |
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals |
Type |
Conference Article |
| Year |
2018 |
Publication |
31st IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2636 - 2645 |
| Keywords |
Three-dimensional displays; Task analysis; Pose estimation; Two dimensional displays; Joints; Training; Solid modeling |
| Abstract |
In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include: (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points; (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data; (3) Discriminative methods still generalize poorly to unseen hand shapes; (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints. |
| Address |
Salt Lake City; USA; June 2018 |
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CVPR |
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HUPBA; no proj |
Approved |
no |
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Admin @ si @ YGS2018 |
Serial |
3115 |
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Albert Clapes; Ozan Bilici; Dariia Temirova; Egils Avots; Gholamreza Anbarjafari; Sergio Escalera |
| Title |
From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation |
Type |
Conference Article |
| Year |
2018 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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Issue |
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Pages |
2373-2382 |
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Salt Lake City; USA; June 2018 |
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CVPRW |
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HUPBA |
Approved |
no |
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Admin @ si @ |
Serial |
3116 |
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Mohammad A. Haque; Ruben B. Bautista; Kamal Nasrollahi; Sergio Escalera; Christian B. Laursen; Ramin Irani; Ole K. Andersen; Erika G. Spaich; Kaustubh Kulkarni; Thomas B. Moeslund; Marco Bellantonio; Golamreza Anbarjafari; Fatemeh Noroozi |
| Title |
Deep Multimodal Pain Recognition: A Database and Comparision of Spatio-Temporal Visual Modalities, Faces and Gestures |
Type |
Conference Article |
| Year |
2018 |
Publication |
13th IEEE Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
250 - 257 |
| Keywords |
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| Abstract |
Pain is a symptom of many disorders associated with actual or potential tissue damage in human body. Managing pain is not only a duty but also highly cost prone. The most primitive state of pain management is the assessment of pain. Traditionally it was accomplished by self-report or visual inspection by experts. However, automatic pain assessment systems from facial videos are also rapidly evolving due to the need of managing pain in a robust and cost effective way. Among different challenges of automatic pain assessment from facial video data two issues are increasingly prevalent: first, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data on shallow learning scenarios. However, employing deep learning techniques for spatio-temporal analysis considering Depth (D) and Thermal (T) along with RGB has high potential in this area. In this paper, we present the first state-of-the-art publicly available database, 'Multimodal Intensity Pain (MIntPAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate. |
| Address |
Xian; China; May 2018 |
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FG |
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HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ HBN2018 |
Serial |
3117 |
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| Author |
Rain Eric Haamer; Kaustubh Kulkarni; Nasrin Imanpour; Mohammad Ahsanul Haque; Egils Avots; Michelle Breisch; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Xavier Baro; Ahmad R. Naghsh-Nilchi; Thomas B. Moeslund; Gholamreza Anbarjafari |
| Title |
Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification |
Type |
Conference Article |
| Year |
2018 |
Publication |
8th International Workshop on Human Behavior Understanding |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames. |
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Xian; China; May 2018 |
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FGW |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ HKI2018 |
Serial |
3118 |
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| Author |
Mohamed Ilyes Lakhal; Hakan Çevikalp; Sergio Escalera; Ferda Ofli |
| Title |
Recurrent Neural Networks for Remote Sensing Image Classification |
Type |
Journal Article |
| Year |
2018 |
Publication |
IET Computer Vision |
Abbreviated Journal |
IETCV |
| Volume |
12 |
Issue |
7 |
Pages |
1040 - 1045 |
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| Abstract |
Automatically classifying an image has been a central problem in computer vision for decades. A plethora of models has been proposed, from handcrafted feature solutions to more sophisticated approaches such as deep learning. The authors address the problem of remote sensing image classification, which is an important problem to many real world applications. They introduce a novel deep recurrent architecture that incorporates high-level feature descriptors to tackle this challenging problem. Their solution is based on the general encoder–decoder framework. To the best of the authors’ knowledge, this is the first study to use a recurrent network structure on this task. The experimental results show that the proposed framework outperforms the previous works in the three datasets widely used in the literature. They have achieved a state-of-the-art accuracy rate of 97.29% on the UC Merced dataset. |
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HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LÇE2018 |
Serial |
3119 |
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| Author |
Pau Rodriguez; Miguel Angel Bautista; Sergio Escalera; Jordi Gonzalez |
| Title |
Beyond Oneshot Encoding: lower dimensional target embedding |
Type |
Journal Article |
| Year |
2018 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
| Volume |
75 |
Issue |
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Pages |
21-31 |
| Keywords |
Error correcting output codes; Output embeddings; Deep learning; Computer vision |
| Abstract |
Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates. |
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| Notes |
ISE; HuPBA; 600.098; 602.133; 602.121; 600.119 |
Approved |
no |
| Call Number |
Admin @ si @ RBE2018 |
Serial |
3120 |
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| Author |
Mohammad N. S. Jahromi; Morten Bojesen Bonderup; Maryam Asadi-Aghbolaghi; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Shohreh Kasaei; Thomas B. Moeslund; Gholamreza Anbarjafari |
| Title |
Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context |
Type |
Conference Article |
| Year |
2018 |
Publication |
IEEE Winter Applications of Computer Vision Workshops |
Abbreviated Journal |
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Issue |
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Pages |
28-36 |
| Keywords |
IEEE Winter Applications of Computer Vision Workshops |
| Abstract |
Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users. |
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Lake Tahoe; USA; March 2018 |
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WACVW |
| Notes |
HUPBA; 602.133 |
Approved |
no |
| Call Number |
Admin @ si @ JBA2018 |
Serial |
3121 |
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| Author |
Jianzhy Guo; Zhen Lei; Jun Wan; Egils Avots; Noushin Hajarolasvadi; Boris Knyazev; Artem Kuharenko; Julio C. S. Jacques Junior; Xavier Baro; Hasan Demirel; Sergio Escalera; Juri Allik; Gholamreza Anbarjafari |
| Title |
Dominant and Complementary Emotion Recognition from Still Images of Faces |
Type |
Journal Article |
| Year |
2018 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
6 |
Issue |
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Pages |
26391 - 26403 |
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| Abstract |
Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition. |
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HUPBA; no proj |
Approved |
no |
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Admin @ si @ GLW2018 |
Serial |
3122 |
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| Author |
Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera |
| Title |
RGB-D-based Human Motion Recognition with Deep Learning: A Survey |
Type |
Journal Article |
| Year |
2018 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
171 |
Issue |
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Pages |
118-139 |
| Keywords |
Human motion recognition; RGB-D data; Deep learning; Survey |
| Abstract |
Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research. |
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HUPBA; no proj |
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no |
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Admin @ si @ WLO2018 |
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3123 |
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Jelena Gorbova; Egils Avots; Iiris Lusi; Mark Fishel; Sergio Escalera; Gholamreza Anbarjafari |
| Title |
Integrating Vision and Language for First Impression Personality Analysis |
Type |
Journal Article |
| Year |
2018 |
Publication |
IEEE Multimedia |
Abbreviated Journal |
MULTIMEDIA |
| Volume |
25 |
Issue |
2 |
Pages |
24 - 33 |
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The authors present a novel methodology for analyzing integrated audiovisual signals and language to assess a persons personality. An evaluation of their proposed multimodal method using a job candidate screening system that predicted five personality traits from a short video demonstrates the methods effectiveness. |
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HUPBA; 602.133 |
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no |
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Admin @ si @ GAL2018 |
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3124 |
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Albert Clapes; Alex Pardo; Oriol Pujol; Sergio Escalera |
| Title |
Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly |
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Journal Article |
| Year |
2018 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVAP |
| Volume |
29 |
Issue |
5 |
Pages |
765–788 |
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Multimodal activity detection; Computer vision; Inertial sensors; Dense trajectories; Dynamic time warping; Assistive technology |
| Abstract |
We present a vision-inertial system which combines two RGB-Depth devices together with a wearable inertial movement unit in order to detect activities of the daily living. From multi-view videos, we extract dense trajectories enriched with a histogram of normals description computed from the depth cue and bag them into multi-view codebooks. During the later classification step a multi-class support vector machine with a RBF- 2 kernel combines the descriptions at kernel level. In order to perform action detection from the videos, a sliding window approach is utilized. On the other hand, we extract accelerations, rotation angles, and jerk features from the inertial data collected by the wearable placed on the user’s dominant wrist. During gesture spotting, a dynamic time warping is applied and the aligning costs to a set of pre-selected gesture sub-classes are thresholded to determine possible detections. The outputs of the two modules are combined in a late-fusion fashion. The system is validated in a real-case scenario with elderly from an elder home. Learning-based fusion results improve the ones from the single modalities, demonstrating the success of such multimodal approach. |
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HUPBA; no proj |
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no |
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Admin @ si @ CPP2018 |
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3125 |
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Jun Wan; Sergio Escalera; Francisco Perales; Josef Kittler |
| Title |
Articulated Motion and Deformable Objects |
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Journal Article |
| Year |
2018 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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79 |
Issue |
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55-64 |
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This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field. |
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HUPBA; no proj |
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no |
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Admin @ si @ WEP2018 |
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3126 |
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| Author |
Cesar de Souza |
| Title |
Action Recognition in Videos: Data-efficient approaches for supervised learning of human action classification models for video |
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Book Whole |
| Year |
2018 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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| Abstract |
In this dissertation, we explore different ways to perform human action recognition in video clips. We focus on data efficiency, proposing new approaches that alleviate the need for laborious and time-consuming manual data annotation. In the first part of this dissertation, we start by analyzing previous state-of-the-art models, comparing their differences and similarities in order to pinpoint where their real strengths come from. Leveraging this information, we then proceed to boost the classification accuracy of shallow models to levels that rival deep neural networks. We introduce hybrid video classification architectures based on carefully designed unsupervised representations of handcrafted spatiotemporal features classified by supervised deep networks. We show in our experiments that our hybrid model combine the best of both worlds: it is data efficient (trained on 150 to 10,000 short clips) and yet improved significantly on the state of the art, including deep models trained on millions of manually labeled images and videos. In the second part of this research, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for “Procedural Human Action Videos”. It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We then introduce deep multi-task representation learning architectures to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF-101 and HMDB-51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, outperforming fine-tuning state-of-the-art unsupervised generative models of videos. |
| Address |
April 2018 |
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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
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Editor |
Antonio Lopez;Naila Murray |
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ADAS; 600.118 |
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no |
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Admin @ si @ Sou2018 |
Serial |
3127 |
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| Author |
Joan Serrat; Felipe Lumbreras; Idoia Ruiz |
| Title |
Learning to measure for preshipment garment sizing |
Type |
Journal Article |
| Year |
2018 |
Publication |
Measurement |
Abbreviated Journal |
MEASURE |
| Volume |
130 |
Issue |
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Pages |
327-339 |
| Keywords |
Apparel; Computer vision; Structured prediction; Regression |
| Abstract |
Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively. |
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ADAS; MSIAU; 600.122; 600.118 |
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no |
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Admin @ si @ SLR2018 |
Serial |
3128 |
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