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Author |
David Berga; Marc Masana; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Disentanglement of Color and Shape Representations for Continual Learning |
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Conference Article |
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Year |
2020 |
Publication |
ICML Workshop on Continual Learning |
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We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjusting the network architecture. We tested classification accuracy and forgetting in a task-incremental setting with Oxford-102 Flowers dataset. We combine our method with Elastic Weight Consolidation, Learning without Forgetting, Synaptic Intelligence and Memory Aware Synapses, and show that feature disentanglement positively impacts continual learning performance. |
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Virtual; July 2020 |
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ICMLW |
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LAMP; 600.120 |
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Admin @ si @ BMW2020 |
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3506 |
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Author |
Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornes; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents |
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Conference Article |
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Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
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The use of administrative documents to communicate and leave record of business information requires of methods
able to automatically extract and understand the content from
such documents in a robust and efficient way. In addition,
the semi-structured nature of these reports is specially suited
for the use of graph-based representations which are flexible
enough to adapt to the deformations from the different document
templates. Moreover, Graph Neural Networks provide the proper
methodology to learn relations among the data elements in
these documents. In this work we study the use of Graph
Neural Network architectures to tackle the problem of entity
recognition and relation extraction in semi-structured documents.
Our approach achieves state of the art results in the three
tasks involved in the process. Additionally, the experimentation
with two datasets of different nature demonstrates the good
generalization ability of our approach. |
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Virtual; January 2021 |
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ICPR |
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DAG; 600.121 |
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Admin @ si @ CRV2020 |
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3509 |
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Author |
M. Li; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Learning to Rank for Active Learning: A Listwise Approach |
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Conference Article |
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2020 |
Publication |
25th International Conference on Pattern Recognition |
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5587-5594 |
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Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.). The goal of active learning is to automatically select a number of unlabeled samples for annotation (according to a budget), based on an acquisition function, which indicates how valuable a sample is for training the model. The learning loss method is a task-agnostic approach which attaches a module to learn to predict the target loss of unlabeled data, and select data with the highest loss for labeling. In this work, we follow this strategy but we define the acquisition function as a learning to rank problem and rethink the structure of the loss prediction module, using a simple but effective listwise approach. Experimental results on four datasets demonstrate that our method outperforms recent state-of-the-art active learning approaches for both image classification and regression tasks. |
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Virtual; January 2021 |
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LAMP; 600.120 |
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Admin @ si @ LLW2020a |
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3511 |
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Author |
Gemma Rotger |
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Lifelike Humans: Detailed Reconstruction of Expressive Human Faces |
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Book Whole |
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Year |
2021 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Developing human-like digital characters is a challenging task since humans are used to recognizing our fellows, and find the computed generated characters inadequately humanized. To fulfill the standards of the videogame and digital film productions it is necessary to model and animate these characters the most closely to human beings. However, it is an arduous and expensive task, since many artists and specialists are required to work on a single character. Therefore, to fulfill these requirements we found an interesting option to study the automatic creation of detailed characters through inexpensive setups. In this work, we develop novel techniques to bring detailed characters by combining different aspects that stand out when developing realistic characters, skin detail, facial hairs, expressions, and microexpressions. We examine each of the mentioned areas with the aim of automatically recover each of the parts without user interaction nor training data. We study the problems for their robustness but also for the simplicity of the setup, preferring single-image with uncontrolled illumination and methods that can be easily computed with the commodity of a standard laptop. A detailed face with wrinkles and skin details is vital to develop a realistic character. In this work, we introduce our method to automatically describe facial wrinkles on the image and transfer to the recovered base face. Then we advance to facial hair recovery by resolving a fitting problem with a novel parametrization model. As of last, we develop a mapping function that allows transfer expressions and microexpressions between different meshes, which provides realistic animations to our detailed mesh. We cover all the mentioned points with the focus on key aspects as (i) how to describe skin wrinkles in a simple and straightforward manner, (ii) how to recover 3D from 2D detections, (iii) how to recover and model facial hair from 2D to 3D, (iv) how to transfer expressions between models holding both skin detail and facial hair, (v) how to perform all the described actions without training data nor user interaction. In this work, we present our proposals to solve these aspects with an efficient and simple setup. We validate our work with several datasets both synthetic and real data, prooving remarkable results even in challenging cases as occlusions as glasses, thick beards, and indeed working with different face topologies like single-eyed cyclops. |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Felipe Lumbreras;Antonio Agudo |
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978-84-122714-3-0 |
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ADAS |
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Call Number |
Admin @ si @ Rot2021 |
Serial |
3513 |
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Author |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Explainable Early Stopping for Action Unit Recognition |
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Conference Article |
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Year |
2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
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693-699 |
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A common technique to avoid overfitting when training deep neural networks (DNN) is to monitor the performance in a dedicated validation data partition and to stop
training as soon as it saturates. This only focuses on what the model does, while completely ignoring what happens inside it.
In this work, we open the “black-box” of DNN in order to perform early stopping. We propose to use a novel theoretical framework that analyses meso-scale patterns in the topology of the functional graph of a network while it trains. Based on it,
we decide when it transitions from learning towards overfitting in a more explainable way. We exemplify the benefits of this approach on a state-of-the art custom DNN that jointly learns local representations and label structure employing an ensemble of dedicated subnetworks. We show that it is practically equivalent in performance to early stopping with patience, the standard early stopping algorithm in the literature. This proves beneficial for AU recognition performance and provides new insights into how learning of AUs occurs in DNNs. |
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Virtual; November 2020 |
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FGW |
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HUPBA; |
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Admin @ si @ CME2020 |
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3514 |
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Author |
Anna Esposito; Terry Amorese; Nelson Maldonato; Alessandro Vinciarelli; Maria Ines Torres; Sergio Escalera; Gennaro Cordasco |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Seniors’ ability to decode differently aged facial emotional expressions |
Type |
Conference Article |
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Year |
2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
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716-722 |
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Virtual; November 2020 |
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HUPBA |
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Admin @ si @ EAM2020 |
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3515 |
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Author |
Anna Esposito; Italia Cirillo; Antonietta Esposito; Leopoldina Fortunati; Gian Luca Foresti; Sergio Escalera; Nikolaos Bourbakis |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Impairments in decoding facial and vocal emotional expressions in high functioning autistic adults and adolescents |
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Conference Article |
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2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
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667-674 |
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Virtual; November 2020 |
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HUPBA |
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Admin @ si @ ECE2020 |
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3516 |
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Author |
Carlos Martin-Isla; Maryam Asadi-Aghbolaghi; Polyxeni Gkontra; Victor M. Campello; Sergio Escalera; Karim Lekadir |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Stacked BCDU-net with semantic CMR synthesis: application to Myocardial Pathology Segmentation challenge |
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2020 |
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MYOPS challenge and workshop |
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Virtual; October 2020 |
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MICCAIW |
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HUPBA |
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Admin @ si @ MAG2020 |
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3518 |
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Author |
Hugo Bertiche; Meysam Madadi; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
CLOTH3D: Clothed 3D Humans |
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Conference Article |
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2020 |
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16th European Conference on Computer Vision |
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This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape. |
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Virtual; August 2020 |
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ECCV |
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HUPBA |
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Admin @ si @ BME2020 |
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3519 |
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Author |
Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation |
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Conference Article |
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2020 |
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Bioimage computation workshop |
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Virtual; August 2020 |
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ECCVW |
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HUPBA |
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Admin @ si @ AAF2020 |
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3520 |
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Author |
Petia Radeva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Uncertainty Modeling within an End-to-end Framework for Food Image Analysis |
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Conference Article |
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2020 |
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1st DELTA |
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MILAB |
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Admin @ si @ Rad2020 |
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3527 |
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Soumick Chatterjee; Fatima Saad; Chompunuch Sarasaen; Suhita Ghosh; Rupali Khatun; Petia Radeva; Georg Rose; Sebastian Stober; Oliver Speck; Andreas Nürnberger |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images |
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Miscellaneous |
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2020 |
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Arxiv |
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CoRR abs/2006.02570
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoniæ and healthy subjects using Chest X-Ray. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable model. |
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MILAB |
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Admin @ si @ CSS2020 |
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3534 |
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Author |
Estefania Talavera; Andreea Glavan; Alina Matei; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Eating Habits Discovery in Egocentric Photo-streams |
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Miscellaneous |
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2020 |
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Arxiv |
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CoRR abs/2009.07646
Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioural pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals. |
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MILAB |
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Admin @ si @ TGM2020 |
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3536 |
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Author |
Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Unsupervised Domain Adaptation without Source Data by Casting a BAIT |
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2020 |
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Arxiv |
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arXiv:2010.12427
Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain. Existing UDA methods require access to source data during adaptation, which may not be feasible in some real-world applications. In this paper, we address the source-free unsupervised domain adaptation (SFUDA) problem, where only the source model is available during the adaptation. We propose a method named BAIT to address SFUDA. Specifically, given only the source model, with the source classifier head fixed, we introduce a new learnable classifier. When adapting to the target domain, class prototypes of the new added classifier will act as a bait. They will first approach the target features which deviate from prototypes of the source classifier due to domain shift. Then those target features are pulled towards the corresponding prototypes of the source classifier, thus achieving feature alignment with the source classifier in the absence of source data. Experimental results show that the proposed method achieves state-of-the-art performance on several benchmark datasets compared with existing UDA and SFUDA methods. |
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LAMP; 600.120 |
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Admin @ si @ YWW2020 |
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3539 |
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Carola Figueroa Flores; Bogdan Raducanu; David Berga; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains |
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Conference Article |
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2021 |
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16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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4 |
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163-171 |
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arXiv:2007.12562
Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classification. In the current paper, we propose an approach which does not require explicit saliency maps to improve image classification, but they are learned implicitely, during the training of an end-to-end image classification task. We show that our approach obtains similar results as the case when the saliency maps are provided explicitely. Combining RGB data with saliency maps represents a significant advantage for object recognition, especially for the case when training data is limited. We validate our method on several datasets for fine-grained classification tasks (Flowers, Birds and Cars). In addition, we show that our saliency estimation method, which is trained without any saliency groundtruth data, obtains competitive results on real image saliency benchmark (Toronto), and outperforms deep saliency models with synthetic images (SID4VAM). |
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Virtual; February 2021 |
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VISAPP |
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LAMP |
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Admin @ si @ FRB2021c |
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3540 |
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