|
Records |
Links |
|
Author |
Jon Almazan; Lluis Gomez; Suman Ghosh; Ernest Valveny; Dimosthenis Karatzas |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
WATTS: A common representation of word images and strings using embedded attributes for text recognition and retrieval |
Type |
Book Chapter |
|
Year |
2020 |
Publication |
Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer |
Place of Publication |
|
Editor |
Analysis”, K. Alahari; C.V. Jawahar |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
Series on Advances in Computer Vision and Pattern Recognition |
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ AGG2020 |
Serial |
3496 |
|
Permanent link to this record |
|
|
|
|
Author |
Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Retrieval Guided Unsupervised Multi-domain Image to Image Translation |
Type |
Conference Article |
|
Year |
2020 |
Publication |
28th ACM International Conference on Multimedia |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ACM |
|
|
Notes |
DAG; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GLN2020 |
Serial |
3497 |
|
Permanent link to this record |
|
|
|
|
Author |
Albert Clapes; Julio C. S. Jacques Junior; Carla Morral; Sergio Escalera |
![goto web page url](img/www.gif)
|
|
Title |
ChaLearn LAP 2020 Challenge on Identity-preserved Human Detection: Dataset and Results |
Type |
Conference Article |
|
Year |
2020 |
Publication |
15th IEEE International Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
801-808 |
|
|
Keywords |
|
|
|
Abstract |
This paper summarizes the ChaLearn Looking at People 2020 Challenge on Identity-preserved Human Detection (IPHD). For the purpose, we released a large novel dataset containing more than 112K pairs of spatiotemporally aligned depth and thermal frames (and 175K instances of humans) sampled from 780 sequences. The sequences contain hundreds of non-identifiable people appearing in a mix of in-the-wild and scripted scenarios recorded in public and private places. The competition was divided into three tracks depending on the modalities exploited for the detection: (1) depth, (2) thermal, and (3) depth-thermal fusion. Color was also captured but only used to facilitate the groundtruth annotation. Still the temporal synchronization of three sensory devices is challenging, so bad temporal matches across modalities can occur. Hence, the labels provided should considered “weak”, although test frames were carefully selected to minimize this effect and ensure the fairest comparison of the participants’ results. Despite this added difficulty, the results got by the participants demonstrate current fully-supervised methods can deal with that and achieve outstanding detection performance when measured in terms of AP@0.50. |
|
|
Address |
Virtual; November 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
FG |
|
|
Notes |
HUPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ CJM2020 |
Serial |
3501 |
|
Permanent link to this record |
|
|
|
|
Author |
Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Sebastien Treguer |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
How far are we from true AutoML: reflection from winning solutions and results of AutoDL challenge |
Type |
Conference Article |
|
Year |
2020 |
Publication |
7th ICML Workshop on Automated Machine Learning |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Following the completion of the AutoDL challenge (the final challenge in the ChaLearn
AutoDL challenge series 2019), we investigate winning solutions and challenge results to
answer an important motivational question: how far are we from achieving true AutoML?
On one hand, the winning solutions achieve good (accurate and fast) classification performance on unseen datasets. On the other hand, all winning solutions still contain a
considerable amount of hard-coded knowledge on the domain (or modality) such as image,
video, text, speech and tabular. This form of ad-hoc meta-learning could be replaced by
more automated forms of meta-learning in the future. Organizing a meta-learning challenge could help forging AutoML solutions that generalize to new unseen domains (e.g.
new types of sensor data) as well as gaining insights on the AutoML problem from a more
fundamental point of view. The datasets of the AutoDL challenge are a resource that can
be used for further benchmarks and the code of the winners has been outsourced, which is
a big step towards “democratizing” Deep Learning. |
|
|
Address |
Virtual; July 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICML |
|
|
Notes |
HUPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ LPX2020 |
Serial |
3502 |
|
Permanent link to this record |
|
|
|
|
Author |
Marc Masana; Bartlomiej Twardowski; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
On Class Orderings for Incremental Learning |
Type |
Conference Article |
|
Year |
2020 |
Publication |
ICML Workshop on Continual Learning |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
The influence of class orderings in the evaluation of incremental learning has received very little attention. In this paper, we investigate the impact of class orderings for incrementally learned classifiers. We propose a method to compute various orderings for a dataset. The orderings are derived by simulated annealing optimization from the confusion matrix and reflect different incremental learning scenarios, including maximally and minimally confusing tasks. We evaluate a wide range of state-of-the-art incremental learning methods on the proposed orderings. Results show that orderings can have a significant impact on performance and the ranking of the methods. |
|
|
Address |
Virtual; July 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICMLW |
|
|
Notes |
LAMP; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MTW2020 |
Serial |
3505 |
|
Permanent link to this record |
|
|
|
|
Author |
David Berga; Marc Masana; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Disentanglement of Color and Shape Representations for Continual Learning |
Type |
Conference Article |
|
Year |
2020 |
Publication |
ICML Workshop on Continual Learning |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
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. |
|
|
Address |
Virtual; July 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICMLW |
|
|
Notes |
LAMP; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ BMW2020 |
Serial |
3506 |
|
Permanent link to this record |
|
|
|
|
Author |
Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornes; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents |
Type |
Conference Article |
|
Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
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. |
|
|
Address |
Virtual; January 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICPR |
|
|
Notes |
DAG; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ CRV2020 |
Serial |
3509 |
|
Permanent link to this record |
|
|
|
|
Author |
M. Li; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Learning to Rank for Active Learning: A Listwise Approach |
Type |
Conference Article |
|
Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
5587-5594 |
|
|
Keywords |
|
|
|
Abstract |
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. |
|
|
Address |
Virtual; January 2021 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICPR |
|
|
Notes |
LAMP; 600.120 |
Approved |
no |
|
|
Call Number |
Admin @ si @ LLW2020a |
Serial |
3511 |
|
Permanent link to this record |
|
|
|
|
Author |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Explainable Early Stopping for Action Unit Recognition |
Type |
Conference Article |
|
Year |
2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
693-699 |
|
|
Keywords |
|
|
|
Abstract |
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. |
|
|
Address |
Virtual; November 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
FGW |
|
|
Notes |
HUPBA; |
Approved |
no |
|
|
Call Number |
Admin @ si @ CME2020 |
Serial |
3514 |
|
Permanent link to this record |
|
|
|
|
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)
|
|
Title |
Seniors’ ability to decode differently aged facial emotional expressions |
Type |
Conference Article |
|
Year |
2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
716-722 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Virtual; November 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
FGW |
|
|
Notes |
HUPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ EAM2020 |
Serial |
3515 |
|
Permanent link to this record |
|
|
|
|
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)
|
|
Title |
Impairments in decoding facial and vocal emotional expressions in high functioning autistic adults and adolescents |
Type |
Conference Article |
|
Year |
2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
667-674 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Virtual; November 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
FGW |
|
|
Notes |
HUPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ ECE2020 |
Serial |
3516 |
|
Permanent link to this record |
|
|
|
|
Author |
Josep Famadas; Meysam Madadi; Cristina Palmero; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Generative Video Face Reenactment by AUs and Gaze Regularization |
Type |
Conference Article |
|
Year |
2020 |
Publication |
15th IEEE International Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
444-451 |
|
|
Keywords |
|
|
|
Abstract |
In this work, we propose an encoder-decoder-like architecture to perform face reenactment in image sequences. Our goal is to transfer the training subject identity to a given test subject. We regularize face reenactment by facial action unit intensity and 3D gaze vector regression. This way, we enforce the network to transfer subtle facial expressions and eye dynamics, providing a more lifelike result. The proposed encoder-decoder receives as input the previous sequence frame stacked to the current frame image of facial landmarks. Thus, the generated frames benefit from appearance and geometry, while keeping temporal coherence for the generated sequence. At test stage, a new target subject with the facial performance of the source subject and the appearance of the training subject is reenacted. Principal component analysis is applied to project the test subject geometry to the closest training subject geometry before reenactment. Evaluation of our proposal shows faster convergence, and more accurate and realistic results in comparison to other architectures without action units and gaze regularization. |
|
|
Address |
Virtual; November 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
FG |
|
|
Notes |
HUPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ FMP2020 |
Serial |
3517 |
|
Permanent link to this record |
|
|
|
|
Author |
Carlos Martin-Isla; Maryam Asadi-Aghbolaghi; Polyxeni Gkontra; Victor M. Campello; Sergio Escalera; Karim Lekadir |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Stacked BCDU-net with semantic CMR synthesis: application to Myocardial Pathology Segmentation challenge |
Type |
Conference Article |
|
Year |
2020 |
Publication |
MYOPS challenge and workshop |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Virtual; October 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
MICCAIW |
|
|
Notes |
HUPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ MAG2020 |
Serial |
3518 |
|
Permanent link to this record |
|
|
|
|
Author |
Hugo Bertiche; Meysam Madadi; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
CLOTH3D: Clothed 3D Humans |
Type |
Conference Article |
|
Year |
2020 |
Publication |
16th European Conference on Computer Vision |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
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. |
|
|
Address |
Virtual; August 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ECCV |
|
|
Notes |
HUPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ BME2020 |
Serial |
3519 |
|
Permanent link to this record |
|
|
|
|
Author |
Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation |
Type |
Conference Article |
|
Year |
2020 |
Publication |
Bioimage computation workshop |
Abbreviated Journal |
|
|
|
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Virtual; August 2020 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ECCVW |
|
|
Notes |
HUPBA |
Approved |
no |
|
|
Call Number |
Admin @ si @ AAF2020 |
Serial |
3520 |
|
Permanent link to this record |