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| Author | Marc Oliu; Javier Selva; Sergio Escalera | ||||
| Title | Folded Recurrent Neural Networks for Future Video Prediction | Type | Conference Article | ||
| Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
| Volume | 11218 | Issue | Pages | 745-761 | |
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| Abstract | Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution of possible futures. This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer. This allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems. We show how with this topology only the encoder or decoder needs to be applied for input encoding and prediction, respectively. This reduces the computational cost and avoids re-encoding the predictions when generating a sequence of frames, mitigating the propagation of errors. Furthermore, it is possible to remove layers from an already trained model, giving an insight to the role performed by each layer and making the model more explainable. We evaluate our approach on three video datasets, outperforming state of the art prediction results on MMNIST and UCF101, and obtaining competitive results on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach. | ||||
| Address | Munich; September 2018 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCV | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ OSE2018 | Serial | 3204 | ||
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| Author | Ciprian Corneanu; Meysam Madadi; Sergio Escalera | ||||
| Title | Deep Structure Inference Network for Facial Action Unit Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
| Volume | 11216 | Issue | Pages | 309-324 | |
| Keywords | Computer Vision; Machine Learning; Deep Learning; Facial Expression Analysis; Facial Action Units; Structure Inference | ||||
| Abstract | Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for general facial expression analysis. Recently, efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between AUs. We propose a deep neural architecture that tackles both problems by combining learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stages. We show that by training the model end-to-end with increased supervision we improve state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets, respectively. | ||||
| Address | Munich; September 2018 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCV | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ CME2018 | Serial | 3205 | ||
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| Author | Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro | ||||
| Title | Residual Stacked RNNs for Action Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 9th International Workshop on Human Behavior Understanding | Abbreviated Journal | |
| Volume | Issue | Pages | 534-548 | ||
| Keywords | Action recognition; Deep residual learning; Two-stream RNN | ||||
| Abstract | Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset. | ||||
| Address | Munich; September 2018 | ||||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LCE2018b | Serial | 3206 | ||
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| Author | Giuseppe Pezzano; Oliver Diaz; Vicent Ribas Ripoll; Petia Radeva | ||||
| Title | CoLe-CNN+: Context learning – Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation | Type | Journal Article | ||
| Year | 2021 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
| Volume | 136 | Issue | Pages | 104689 | |
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| Abstract | The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19. | ||||
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ PDR2021 | Serial | 3635 | ||
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| Author | Cristina Palmero; Javier Selva; Mohammad Ali Bagheri; Sergio Escalera | ||||
| Title | Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues | Type | Conference Article | ||
| Year | 2018 | Publication | 29th British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | Gaze behavior is an important non-verbal cue in social signal processing and humancomputer interaction. In this paper, we tackle the problem of person- and head poseindependent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame. Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on
EYEDIAP dataset, further improved by 4% when the temporal modality is included. |
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| Address | Newcastle; UK; September 2018 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ PSB2018 | Serial | 3208 | ||
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| Author | Yagmur Gucluturk; Umut Guclu; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Marcel A. J. van Gerven; Rob van Lier | ||||
| Title | Multimodal First Impression Analysis with Deep Residual Networks | Type | Journal Article | ||
| Year | 2018 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
| Volume | 8 | Issue | 3 | Pages | 316-329 |
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| Abstract | People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities. Finally, in order to promote explainability in machine learning and to provide an example for the upcoming ChaLearn challenges, we present a simple approach for explaining the predictions for job interview recommendations | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ GGB2018 | Serial | 3210 | ||
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| Author | Gabriela Ramirez; Esau Villatoro; Bogdan Ionescu; Hugo Jair Escalante; Sergio Escalera; Martha Larson; Henning Muller; Isabelle Guyon | ||||
| Title | Overview of the Multimedia Information Processing for Personality & Social Networks Analysis Contes | Type | Conference Article | ||
| Year | 2018 | Publication | Multimedia Information Processing for Personality and Social Networks Analysis (MIPPSNA 2018) | Abbreviated Journal | |
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| Address | Beijing; China; August 2018 | ||||
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| Area | Expedition | Conference | ICPRW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ RVI2018 | Serial | 3211 | ||
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| Author | Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | Review on Emotion Recognition Databases | Type | Book Chapter | ||
| Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | emotion; computer vision; databases | ||||
| Abstract | Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format. | ||||
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| ISSN | ISBN | 978-1-78923-316-2 | Medium | ||
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| Notes | HUPBA; 602.133 | Approved | no | ||
| Call Number | Admin @ si @ HRL2018 | Serial | 3212 | ||
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| Author | Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera | ||||
| Title | Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Circuits and Systems for Video Technology | Abbreviated Journal | TCSVT |
| Volume | 29 | Issue | 6 | Pages | 1729-1740 |
| Keywords | Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding | ||||
| Abstract | Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset. | ||||
| Address | June 2019, | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ AAK2018 | Serial | 3213 | ||
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| Author | Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya | ||||
| Title | Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” | Type | Journal | ||
| Year | 2018 | Publication | Informaciones Psiquiatricas | Abbreviated Journal | |
| Volume | 232 | Issue | Pages | 47-59 | |
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| ISSN | 0210-7279 | ISBN | Medium | ||
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| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FAA2018 | Serial | 3214 | ||
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| Author | Gholamreza Anbarjafari; Sergio Escalera | ||||
| Title | Human-Robot Interaction: Theory and Application | Type | Book Whole | ||
| Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
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| ISSN | ISBN | 978-1-78923-316-2 | Medium | ||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ AnE2018 | Serial | 3216 | ||
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| Author | Mikhail Mozerov; Joost Van de Weijer | ||||
| Title | One-view occlusion detection for stereo matching with a fully connected CRF model | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 28 | Issue | 6 | Pages | 2936-2947 |
| Keywords | Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter | ||||
| Abstract | In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method [15] to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only
one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-ofthe-art especially for median, average and mean squared error metrics. |
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| Notes | LAMP; 600.098; 600.109; 602.133; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ MoW2019 | Serial | 3221 | ||
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| Author | Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi | ||||
| Title | WiCV 2018: The Fourth Women In Computer Vision Workshop | Type | Conference Article | ||
| Year | 2018 | Publication | 4th Women in Computer Vision Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 1941-19412 | ||
| Keywords | Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning | ||||
| Abstract | We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
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| Address | Salt Lake City; USA; June 2018 | ||||
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| Area | Expedition | Conference | WiCV | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ DBR2018 | Serial | 3222 | ||
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| Author | Arnau Baro; Pau Riba; Alicia Fornes | ||||
| Title | A Starting Point for Handwritten Music Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 1st International Workshop on Reading Music Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 5-6 | ||
| Keywords | Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA | ||||
| Abstract | In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community. | ||||
| Address | Paris; France; September 2018 | ||||
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| Area | Expedition | Conference | WORMS | ||
| Notes | DAG; 600.097; 601.302; 601.330; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRF2018 | Serial | 3223 | ||
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| Author | Laura Lopez-Fuentes; Alessandro Farasin; Harald Skinnemoen; Paolo Garza | ||||
| Title | Deep Learning models for passability detection of flooded roads | Type | Conference Article | ||
| Year | 2018 | Publication | MediaEval 2018 Multimedia Benchmark Workshop | Abbreviated Journal | |
| Volume | 2283 | Issue | Pages | ||
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| Abstract | In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task. | ||||
| Address | Sophia Antipolis; France; October 2018 | ||||
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| Area | Expedition | Conference | MediaEval | ||
| Notes | LAMP; 600.084; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LFS2018 | Serial | 3224 | ||
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