| Records |
| Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin |
Title  |
Virtual Worlds and Active Learning for Human Detection |
Type |
Conference Article |
| Year |
2011 |
Publication |
13th International Conference on Multimodal Interaction |
Abbreviated Journal |
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| Volume |
|
Issue |
|
Pages |
393-400 |
| Keywords |
Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning |
| Abstract |
Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid. |
| Address |
Alicante, Spain |
| Corporate Author |
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Thesis |
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| Publisher |
ACM DL |
Place of Publication |
New York, NY, USA, USA |
Editor |
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| Language |
English |
Summary Language |
English |
Original Title |
Virtual Worlds and Active Learning for Human Detection |
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-1-4503-0641-6 |
Medium |
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| Area |
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Expedition |
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Conference |
ICMI |
| Notes |
ADAS |
Approved |
yes |
| Call Number |
ADAS @ adas @ VLP2011a |
Serial |
1683 |
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| Author |
Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
| Title |
Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project |
Type |
Journal Article |
| Year |
2023 |
Publication |
International Journal of Computer Assisted Radiology and Surgery |
Abbreviated Journal |
IJCARS |
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Issue |
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Pages |
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Medium |
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Expedition |
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Conference |
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| Notes |
IAM |
Approved |
no |
| Call Number |
Admin @ si @ TGM2023 |
Serial |
3830 |
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| Author |
Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
| Title |
Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules |
Type |
Conference Article |
| Year |
2023 |
Publication |
37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery |
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 |
Pòster |
| Address |
Munich; Germany; June 2023 |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
CARS |
| Notes |
IAM |
Approved |
no |
| Call Number |
Admin @ si @ TGR2023a |
Serial |
3950 |
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| Author |
Javier Marin |
| Title |
Virtual learning for real testing |
Type |
Report |
| Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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| Volume |
150 |
Issue |
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Pages |
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| Keywords |
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| Abstract |
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| Address |
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| Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
| Publisher |
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Place of Publication |
bell |
Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ Mar2009c |
Serial |
2403 |
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| Author |
David Vazquez; Javier Marin; Antonio Lopez; Daniel Ponsa; David Geronimo |
| Title |
Virtual and Real World Adaptation for Pedestrian Detection |
Type |
Journal Article |
| Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
36 |
Issue |
4 |
Pages |
797-809 |
| Keywords |
Domain Adaptation; Pedestrian Detection |
| Abstract |
Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector. |
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Place of Publication |
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Original Title |
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Series Title |
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Series Issue |
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Edition |
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| ISSN |
0162-8828 |
ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
ADAS; 600.057; 600.054; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ VML2014 |
Serial |
2275 |
| Permanent link to this record |
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| Author |
F. Javier Sanchez; Jordi Vitria |
| Title |
ViLi + : Extended Lisp for image Processing and Computer Vision. |
Type |
Conference Article |
| Year |
1994 |
Publication |
Progress in Image Analysis and Processing III |
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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| Address |
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Thesis |
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| Publisher |
World Scientific |
Place of Publication |
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Editor |
S.Impedovo |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
981-02-1552-5 |
Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
MV;OR |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ SaV1994; IAM @ iam @ SaV1994 |
Serial |
114 |
| Permanent link to this record |
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| Author |
Bhaskar Chakraborty; Ognjen Rudovic; Jordi Gonzalez |
| Title |
View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component-Wise HMM of Body Parts |
Type |
Conference Article |
| Year |
2008 |
Publication |
8th IEEE International Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
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| Address |
Amsterdam; The Netherlands |
| Corporate Author |
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Place of Publication |
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Original Title |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
FG |
| Notes |
ISE |
Approved |
no |
| Call Number |
ISE @ ise @ CRG2008 |
Serial |
1113 |
| Permanent link to this record |
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| |
| Author |
Bhaskar Chakraborty |
| Title |
View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component Wise HMM of Body Parts |
Type |
Miscellaneous |
| Year |
2008 |
Publication |
CVC Technical Report #123 |
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 |
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| Address |
Barcelona, Spain |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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Series Title |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ Cha2008 |
Serial |
1149 |
| Permanent link to this record |
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| Author |
Bhaskar Chakraborty; Marco Pedersoli; Jordi Gonzalez |
| Title |
View-Invariant Human Action Detection using Component-Wise HMM of Body Parts |
Type |
Book Chapter |
| Year |
2008 |
Publication |
Articulated Motion and Deformable Objects, 5th International Conference |
Abbreviated Journal |
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| Volume |
5098 |
Issue |
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Pages |
208–217 |
| Keywords |
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| Abstract |
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| Address |
Port d'Andratx (Mallorca) |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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LNCS |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
AMDO |
| Notes |
ISE |
Approved |
no |
| Call Number |
ISE @ ise @ CPG2008 |
Serial |
975 |
| Permanent link to this record |
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| Author |
Christian Keilstrup Ingwersen; Artur Xarles; Albert Clapes; Meysam Madadi; Janus Nortoft Jensen; Morten Rieger Hannemose; Anders Bjorholm Dahl; Sergio Escalera |
| Title |
Video-based Skill Assessment for Golf: Estimating Golf Handicap |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
31-39 |
| Keywords |
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| Abstract |
Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf. |
| Address |
Otawa; Canada; October 2023 |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Edition |
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Medium |
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Expedition |
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Conference |
MMSports |
| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ KXC2023 |
Serial |
3929 |
| Permanent link to this record |
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
| Title |
Video-based Isolated Hand Sign Language Recognition Using a Deep Cascaded Model |
Type |
Journal Article |
| Year |
2020 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
79 |
Issue |
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Pages |
22965–22987 |
| Keywords |
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| Abstract |
In this paper, we propose an efficient cascaded model for sign language recognition taking benefit from spatio-temporal hand-based information using deep learning approaches, especially Single Shot Detector (SSD), Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM), from videos. Our simple yet efficient and accurate model includes two main parts: hand detection and sign recognition. Three types of spatial features, including hand features, Extra Spatial Hand Relation (ESHR) features, and Hand Pose (HP) features, have been fused in the model to feed to LSTM for temporal features extraction. We train SSD model for hand detection using some videos collected from five online sign dictionaries. Our model is evaluated on our proposed dataset (Rastgoo et al., Expert Syst Appl 150: 113336, 2020), including 10’000 sign videos for 100 Persian sign using 10 contributors in 10 different backgrounds, and isoGD dataset. Using the 5-fold cross-validation method, our model outperforms state-of-the-art alternatives in sign language recognition |
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Thesis |
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Place of Publication |
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Conference |
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| Notes |
HuPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ RKE2020b |
Serial |
3442 |
| Permanent link to this record |
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| Author |
Javier Selva; Anders S. Johansen; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Albert Clapes |
| Title |
Video transformers: A survey |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
45 |
Issue |
11 |
Pages |
12922-12943 |
| Keywords |
Artificial Intelligence; Computer Vision; Self-Attention; Transformers; Video Representations |
| Abstract |
Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However, they lack inductive biases and scale quadratically with input length. These limitations are further exacerbated when dealing with the high dimensionality introduced by the temporal dimension. While there are surveys analyzing the advances of Transformers for vision, none focus on an in-depth analysis of video-specific designs. In this survey, we analyze the main contributions and trends of works leveraging Transformers to model video. Specifically, we delve into how videos are handled at the input level first. Then, we study the architectural changes made to deal with video more efficiently, reduce redundancy, re-introduce useful inductive biases, and capture long-term temporal dynamics. In addition, we provide an overview of different training regimes and explore effective self-supervised learning strategies for video. Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity. |
| Address |
1 Nov. 2023 |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
HUPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ SJE2023 |
Serial |
3823 |
| Permanent link to this record |
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| Author |
Marc Bolaños; Maite Garolera; Petia Radeva |
| Title |
Video Segmentation of Life-Logging Videos |
Type |
Conference Article |
| Year |
2014 |
Publication |
8th Conference on Articulated Motion and Deformable Objects |
Abbreviated Journal |
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| Volume |
8563 |
Issue |
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Pages |
1-9 |
| Keywords |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
AMDO |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ BGR2014 |
Serial |
2558 |
| Permanent link to this record |
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| Author |
Alvaro Peris; Marc Bolaños; Petia Radeva; Francisco Casacuberta |
| Title |
Video Description Using Bidirectional Recurrent Neural Networks |
Type |
Conference Article |
| Year |
2016 |
Publication |
25th International Conference on Artificial Neural Networks |
Abbreviated Journal |
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| Volume |
2 |
Issue |
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Pages |
3-11 |
| Keywords |
Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks |
| Abstract |
Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames. |
| Address |
Barcelona; September 2016 |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICANN |
| Notes |
MILAB; |
Approved |
no |
| Call Number |
Admin @ si @ PBR2016 |
Serial |
2833 |
| Permanent link to this record |
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| Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
| Title |
Video Co-segmentation |
Type |
Conference Article |
| Year |
2012 |
Publication |
11th Asian Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
7725 |
Issue |
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Pages |
13-24 |
| Keywords |
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| Abstract |
Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos. |
| Address |
Daejeon, Korea |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-37443-2 |
Medium |
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| Area |
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Expedition |
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Conference |
ACCV |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RSL2012d |
Serial |
2153 |
| Permanent link to this record |
