| Records |
| Author |
Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy |
| Title |
End-to-end Driving via Conditional Imitation Learning |
Type |
Conference Article |
Year  |
2018 |
Publication |
IEEE International Conference on Robotics and Automation |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
4693 - 4700 |
| Keywords |
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| Abstract |
Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL |
| Address |
Brisbane; Australia; May 2018 |
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Medium |
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Expedition |
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Conference |
ICRA |
| Notes |
ADAS; 600.116; 600.124; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ CML2018 |
Serial |
3108 |
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| Author |
Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva |
| Title |
Egocentric video description based on temporally-linked sequences |
Type |
Journal Article |
| Year |
2018 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
| Volume |
50 |
Issue |
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Pages |
205-216 |
| Keywords |
egocentric vision; video description; deep learning; multi-modal learning |
| Abstract |
Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures.
In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description. |
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Expedition |
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| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ BPC2018 |
Serial |
3109 |
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| Author |
Stefan Schurischuster; Beatriz Remeseiro; Petia Radeva; Martin Kampel |
| Title |
A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees |
Type |
Conference Article |
| Year |
2018 |
Publication |
15th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
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| Volume |
10882 |
Issue |
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Pages |
465-473 |
| Keywords |
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| Abstract |
Varroa destructor is a parasite harming bee colonies. As the worldwide bee population is in danger, beekeepers as well as researchers are looking for methods to monitor the health of bee hives. In this context, we present a preliminary study to detect parasites on bee videos by means of image analysis and machine learning techniques. For this purpose, each video frame is analyzed individually to extract bee image patches, which are then processed to compute image descriptors and finally classified into mite and no mite bees. The experimental results demonstrated the adequacy of the proposed method, which will be a perfect stepping stone for a further bee monitoring system. |
| Address |
Povoa de Varzim; Portugal; June 2018 |
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LNCS |
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Conference |
ICIAR |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ SRR2018a |
Serial |
3110 |
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| Author |
Stefan Lonn; Petia Radeva; Mariella Dimiccoli |
| Title |
A picture is worth a thousand words but how to organize thousands of pictures? |
Type |
Miscellaneous |
| Year |
2018 |
Publication |
Arxiv |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization. |
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Conference |
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| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LRD2018 |
Serial |
3111 |
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| Author |
Md.Mostafa Kamal Sarker; , Hatem A. Rashwan; Farhan Akram; Syeda Furruka Banu; Adel Saleh; Vivek Kumar Singh; Forhad U. H. Chowdhury; Saddam Abdulwahab; Santiago Romani; Petia Radeva; Domenec Puig |
| Title |
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks. |
Type |
Conference Article |
| Year |
2018 |
Publication |
21st International Conference on Medical Image Computing & Computer Assisted Intervention |
Abbreviated Journal |
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| Volume |
2 |
Issue |
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Pages |
21-29 |
| Keywords |
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| Abstract |
Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than 100 images of size 384x384 per second on a recent GPU. |
| Address |
Granada; Espanya; September 2018 |
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Conference |
MICCAI |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ SRA2018 |
Serial |
3112 |
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| Author |
Md.Mostafa Kamal Sarker; Mohammed Jabreel; , Hatem A. Rashwan; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
| Title |
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network |
Type |
Conference Article |
| Year |
2018 |
Publication |
21st International Conference of the Catalan Association for Artificial Intelligence |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
365-372 |
| Keywords |
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| Abstract |
Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. |
| Address |
Roses; catalonia; October 2018 |
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Conference |
CCIA |
| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ SJR2018 |
Serial |
3113 |
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| 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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Conference |
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| Notes |
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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Conference |
CVPR |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ YGS2018 |
Serial |
3115 |
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| Author |
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 |
Abbreviated Journal |
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Issue |
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Pages |
2373-2382 |
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| Address |
Salt Lake City; USA; June 2018 |
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Conference |
CVPRW |
| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ |
Serial |
3116 |
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| Author |
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 |
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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 |
| Notes |
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 |
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Issue |
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Pages |
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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. |
| Address |
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 |
| Keywords |
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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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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 |
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Conference Article |
| Year |
2018 |
Publication |
IEEE Winter Applications of Computer Vision Workshops |
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28-36 |
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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 |
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HUPBA; 602.133 |
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Admin @ si @ JBA2018 |
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3121 |
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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 |
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Journal Article |
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2018 |
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IEEE Access |
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ACCESS |
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6 |
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26391 - 26403 |
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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 |
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Admin @ si @ GLW2018 |
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3122 |
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