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Author | Bojana Gajic; Ramon Baldrich | ||||
Title | Cross-domain fashion image retrieval | Type | Conference Article | ||
Year | 2018 | Publication | CVPR 2018 Workshop on Women in Computer Vision (WiCV 2018, 4th Edition) | Abbreviated Journal | |
Volume | Issue | Pages | 19500-19502 | ||
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Abstract | Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products
in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task. |
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Address | Salt Lake City, USA; 22 June 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | CIC; 600.087 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3709 | ||
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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 | ||||
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 | WiCV | ||
Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ DBR2018 | Serial | 3222 | ||
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Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
Title | Deep Learning based Single Image Dehazing | Type | Conference Article | ||
Year | 2018 | Publication | 31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop | Abbreviated Journal | |
Volume | Issue | Pages | 1250 - 12507 | ||
Keywords | Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis | ||||
Abstract | This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. |
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Address | Salt Lake City; USA; June 2018 | ||||
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 | CVPRW | ||
Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ SSV2018d | Serial | 3197 | ||
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Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
Title | Infrared Image Colorization based on a Triplet DCGAN Architecture | Type | Conference Article | ||
Year | 2017 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the
color channels are obtained at the same time. |
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Address | Honolulu; Hawaii; USA; July 2017 | ||||
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 | CVPRW | ||
Notes | ADAS; 600.086; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SSV2017b | Serial | 2920 | ||
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Author | Sergio Escalera; Mercedes Torres-Torres; Brais Martinez; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Georgios Tzimiropoulos; Ciprian Corneanu; Marc Oliu Simón; Mohammad Ali Bagheri; Michel Valstar | ||||
Title | ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016 | Type | Conference Article | ||
Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory classification and smile and gender classification, respectively. We present two crowd-sourcing methodologies used to collect manual annotations. A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age). For the Faces of the World data, the citizen-science Zooniverse platform was used. This paper summarizes the three challenges and the data used, as well as the results achieved by the participants of the competitions. Details of the ChaLearn LAP FotW competitions can be found at http://gesture.chalearn.org. | ||||
Address | Las Vegas; USA; June 2016 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | HuPBA;MV; | Approved | no | ||
Call Number | ETM2016 | Serial | 2849 | ||
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Author | Cristhian A. Aguilera-Carrasco; F. Aguilera; Angel Sappa; C. Aguilera; Ricardo Toledo | ||||
Title | Learning cross-spectral similarity measures with deep convolutional neural networks | Type | Conference Article | ||
Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | The simultaneous use of images from different spectracan be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains. | ||||
Address | Las vegas; USA; June 2016 | ||||
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 | CVPRW | ||
Notes | ADAS; 600.086; 600.076 | Approved | no | ||
Call Number | Admin @ si @AAS2016 | Serial | 2809 | ||
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Author | Jun Wan; Yibing Zhao; Shuai Zhou; Isabelle Guyon; Sergio Escalera | ||||
Title | ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition | Type | Conference Article | ||
Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD)and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset
(CGD) that has a total of more than 50000 gestures for the “one-shot-learning” competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences.Using these datasets we will open two competitions on the CodaLab platform so that researchers can test and compare their methods for “user independent” gesture recognition. The first challenge is designed for gesture spotting and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. The baseline method based on the bag of visual words model is also presented. |
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Address | Las Vegas; USA; July 2016 | ||||
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 | CVPRW | ||
Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ WZZ2016 | Serial | 2771 | ||
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Author | Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika | ||||
Title | Multi-observation Face Recognition in Videos based on Label Propagation | Type | Conference Article | ||
Year | 2015 | Publication | 6th Workshop on Analysis and Modeling of Faces and Gestures AMFG2015 | Abbreviated Journal | |
Volume | Issue | Pages | 10-17 | ||
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Abstract | In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons’ identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we
propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video sequences extracted from the popular TV-series ’Friends’ show a distinct advantage of the proposed method over the existing standard graph construction methods. |
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Address | Boston; USA; June 2015 | ||||
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 | CVPRW | ||
Notes | LAMP; 600.068; 600.072; | Approved | no | ||
Call Number | Admin @ si @ RBD2015 | Serial | 2627 | ||
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Author | Ramin Irani; Kamal Nasrollahi; Chris Bahnsen; D.H. Lundtoft; Thomas B. Moeslund; Marc O. Simon; Ciprian Corneanu; Sergio Escalera; Tanja L. Pedersen; Maria-Louise Klitgaard; Laura Petrini | ||||
Title | Spatio-temporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition | Type | Conference Article | ||
Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
Volume | Issue | Pages | 88-95 | ||
Keywords | |||||
Abstract | Pain is a vital sign of human health and its automatic detection can be of crucial importance in many different contexts, including medical scenarios. While most available computer vision techniques are based on RGB, in this paper, we investigate the effect of combining RGB, depth, and thermal
facial images for pain detection and pain intensity level recognition. For this purpose, we extract energies released by facial pixels using a spatiotemporal filter. Experiments on a group of 12 elderly people applying the multimodal approach show that the proposed method successfully detects pain and recognizes between three intensity levels in 82% of the analyzed frames improving more than 6% over RGB only analysis in similar conditions. |
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Address | Boston; EEUU; June 2015 | ||||
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 | CVPRW | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ INB2015 | Serial | 2654 | ||
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Author | Andres Traumann; Sergio Escalera; Gholamreza Anbarjafari | ||||
Title | A New Retexturing Method for Virtual Fitting Room Using Kinect 2 Camera | Type | Conference Article | ||
Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
Volume | Issue | Pages | 75-79 | ||
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Abstract | |||||
Address | Boston; EEUU; June 2015 | ||||
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 | CVPRW | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ TEA2015 | Serial | 2653 | ||
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Author | Xavier Baro; Jordi Gonzalez; Junior Fabian; Miguel Angel Bautista; Marc Oliu; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera | ||||
Title | ChaLearn Looking at People 2015 challenges: action spotting and cultural event recognition | Type | Conference Article | ||
Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
Volume | Issue | Pages | 1-9 | ||
Keywords | |||||
Abstract | Following previous series on Looking at People (LAP) challenges [6, 5, 4], ChaLearn ran two competitions to be presented at CVPR 2015: action/interaction spotting and cultural event recognition in RGB data. We ran a second round on human activity recognition on RGB data sequences. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes the two performed challenges and obtained results. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/. | ||||
Address | Boston; EEUU; June 2015 | ||||
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 | CVPRW | ||
Notes | HuPBA;MV | Approved | no | ||
Call Number | Serial | 2652 | |||
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Author | Carlo Gatta; Adriana Romero; Joost Van de Weijer | ||||
Title | Unrolling loopy top-down semantic feedback in convolutional deep networks | Type | Conference Article | ||
Year | 2014 | Publication | Workshop on Deep Vision: Deep Learning for Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 498-505 | ||
Keywords | |||||
Abstract | In this paper, we propose a novel way to perform top-down semantic feedback in convolutional deep networks for efficient and accurate image parsing. We also show how to add global appearance/semantic features, which have shown to improve image parsing performance in state-of-the-art methods, and was not present in previous convolutional approaches. The proposed method is characterised by an efficient training and a sufficiently fast testing. We use the well known SIFTflow dataset to numerically show the advantages provided by our contributions, and to compare with state-of-the-art image parsing convolutional based approaches. | ||||
Address | Columbus; Ohio; June 2014 | ||||
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 | CVPRW | ||
Notes | LAMP; MILAB; 601.160; 600.079 | Approved | no | ||
Call Number | Admin @ si @ GRW2014 | Serial | 2490 | ||
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Author | Andreas Møgelmose; Chris Bahnsen; Thomas B. Moeslund; Albert Clapes; Sergio Escalera | ||||
Title | Tri-modal Person Re-identification with RGB, Depth and Thermal Features | Type | Conference Article | ||
Year | 2013 | Publication | 9th IEEE Workshop on Perception beyond the visible Spectrum, Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 301-307 | ||
Keywords | |||||
Abstract | Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work is mainly based on RGB data, but in this work we for the first time present a system where we combine RGB, depth, and thermal data for re-identification purposes. First, from each of the three modalities, we obtain some particular features: from RGB data, we model color information from different regions of the body, from depth data, we compute different soft body biometrics, and from thermal data, we extract local structural information. Then, the three information types are combined in a joined classifier. The tri-modal system is evaluated on a new RGB-D-T dataset, showing successful results in re-identification scenarios. | ||||
Address | Portland; oregon; June 2013 | ||||
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 | 978-0-7695-4990-3 | Medium | ||
Area | Expedition | Conference | CVPRW | ||
Notes | HUPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ MBM2013 | Serial | 2253 | ||
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Author | Fadi Dornaika; Bogdan Raducanu | ||||
Title | Out-of-Sample Embedding for Manifold Learning Applied to Face Recognition | Type | Conference Article | ||
Year | 2013 | Publication | IEEE International Workshop on Analysis and Modeling of Faces and Gestures | Abbreviated Journal | |
Volume | Issue | Pages | 862-868 | ||
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Abstract | Manifold learning techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data---the out-of-sample problem. For the first aspect, the proposed schemes were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only reached for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that sparse coding theory not only serves for automatic graph reconstruction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the k-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on four public face databases. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes. | ||||
Address | Portland; USA; June 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | OR; 600.046;MV | Approved | no | ||
Call Number | Admin @ si @ DoR2013 | Serial | 2236 | ||
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Author | David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa | ||||
Title | Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes | Type | Conference Article | ||
Year | 2013 | Publication | CVPR Workshop on Ground Truth – What is a good dataset? | Abbreviated Journal | |
Volume | Issue | Pages | 706 - 711 | ||
Keywords | Pedestrian Detection; Domain Adaptation | ||||
Abstract | Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs. | ||||
Address | Portland; Oregon; June 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | IEEE | Place of Publication | Editor | ||
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | ADAS; 600.054; 600.057; 601.217 | Approved | no | ||
Call Number | ADAS @ adas @ VXR2013a | Serial | 2219 | ||
Permanent link to this record |