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Author (down) Shiqi Yang edit  isbn
openurl 
  Title Towards Source-Free Domain Adaption of Neural Networks in an Open World Type Book Whole
  Year 2023 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Though they achieve great success, deep neural networks typically require a huge
amount of labeled data for training. However, collecting labeled data is often laborious and expensive. It would, therefore, be ideal if the knowledge obtained from label-rich datasets could be transferred to unlabeled data. However, deep networks are weak at generalizing to unseen domains, even when the differences are only subtle between the datasets. In real-world situations, a typical factor impairing the model generalization ability is the distribution shift between data from different domains, which is a long-standing problem usually termed as (unsupervised) domain adaptation.
A crucial requirement in the methodology of these domain adaptation methods is that they require access to source domain data during the adaptation process to the target domain. Accessibility to the source data of a trained source model is often impossible in real-world applications, for example, when deploying domain adaptation algorithms on mobile devices where the computational capacity is limited or in situations where data privacy rules limit access to the source domain data. Without access to the source domain data, existing methods suffer from inferior performance. Thus, in this thesis, we investigate domain adaptation without source data (termed as source-free domain adaptation) in multiple different scenarios that focus on image classification tasks.
We first study the source-free domain adaptation problem in a closed-set setting,
where the label space of different domains is identical. Only accessing the pretrained source model, we propose to address source-free domain adaptation from the perspective of unsupervised clustering. We achieve this based on nearest neighborhood clustering. In this way, we can transfer the challenging source-free domain adaptation task to a type of clustering problem. The final optimization objective is an upper bound containing only two simple terms, which can be explained as discriminability and diversity. We show that this allows us to relate several other methods in domain adaptation, unsupervised clustering and contrastive learning via the perspective of discriminability and diversity.
 
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher IMPRIMA Place of Publication Editor Joost  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-126409-3-9 Medium  
  Area Expedition Conference  
  Notes LAMP Approved no  
  Call Number Admin @ si @ Yan2023 Serial 3963  
Permanent link to this record
 

 
Author (down) Shigang Yue; F. Claire Rind; Matthias S. Keil; Jorge Cuadri; Richard Stafford edit  openurl
  Title A bio-inspired visual collision detection mechanism for cars: Optimisation of a model of a locust neuron to a novel environment Type Journal
  Year 2006 Publication Neurocomputing 69(13–15): 1591–1598 Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  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  
  Notes Approved no  
  Call Number Admin @ si @ YRK2006 Serial 652  
Permanent link to this record
 

 
Author (down) Shifeng Zhang; Xiaobo Wang; Ajian Liu; Chenxu Zhao; Jun Wan; Sergio Escalera; Hailin Shi; Zezheng Wang; Stan Z. Li edit   pdf
url  doi
openurl 
  Title A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing Type Conference Article
  Year 2019 Publication 32nd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 919-928  
  Keywords  
  Abstract Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide a measurement set, evaluation protocol and training/validation/testing subsets, developing a new benchmark for face anti-spoofing. Moreover, we present a new multi-modal fusion method as baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/chalearnfacespoofingattackdete/.  
  Address California; June 2019  
  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 CVPR  
  Notes HuPBA; no proj Approved no  
  Call Number Admin @ si @ ZWL2019 Serial 3331  
Permanent link to this record
 

 
Author (down) Shifeng Zhang; Ajian Liu; Jun Wan; Yanyan Liang; Guogong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li edit  url
doi  openurl
  Title CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing Type Journal
  Year 2020 Publication IEEE Transactions on Biometrics, Behavior, and Identity Science Abbreviated Journal TTBIS  
  Volume 2 Issue 2 Pages 182 - 193  
  Keywords  
  Abstract Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities ( i.e. , RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2019?authuser=0  
  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  
  Notes HuPBA; no proj Approved no  
  Call Number Admin @ si @ ZLW2020 Serial 3412  
Permanent link to this record
 

 
Author (down) Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras edit   pdf
doi  openurl
  Title Intrinsic Image Evaluation On Synthetic Complex Scenes Type Conference Article
  Year 2013 Publication 20th IEEE International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages 285 - 289  
  Keywords  
  Abstract Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth
procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes.
 
  Address Melbourne; Australia; September 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 Medium  
  Area Expedition Conference ICIP  
  Notes CIC; 600.048; 600.052; 600.051 Approved no  
  Call Number Admin @ si @ BSW2013 Serial 2264  
Permanent link to this record
 

 
Author (down) Shida Beigpour; Joost Van de Weijer edit   pdf
openurl 
  Title Photo-Realistic Color Alteration for Architecture and Design Type Conference Article
  Year 2010 Publication Proceedings of The CREATE 2010 Conference Abbreviated Journal  
  Volume Issue Pages 84–88  
  Keywords  
  Abstract As color is a strong stimuli we receive from the exterior world, choosing the right color can prove crucial in creating the desired architecture and desing. We propose a framework to apply a realistic color change on both objects and their illuminant lights for snapshots of architectural designs, in order to visualize and choose the right color before actully applying the change in the real world. The proposed framework is based on the laws of physics in order to accomplish realistic and physically plausible results.  
  Address Gjovik (Norway)  
  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 CREATE  
  Notes CIC Approved no  
  Call Number CAT @ cat @ BeW2010 Serial 1330  
Permanent link to this record
 

 
Author (down) Shida Beigpour; Joost Van de Weijer edit   pdf
url  doi
isbn  openurl
  Title Object Recoloring Based on Intrinsic Image Estimation Type Conference Article
  Year 2011 Publication 13th IEEE International Conference in Computer Vision Abbreviated Journal  
  Volume Issue Pages 327 - 334  
  Keywords  
  Abstract Object recoloring is one of the most popular photo-editing tasks. The problem of object recoloring is highly under-constrained, and existing recoloring methods limit their application to objects lit by a white illuminant. Application of these methods to real-world scenes lit by colored illuminants, multiple illuminants, or interreflections, results in unrealistic recoloring of objects. In this paper, we focus on the recoloring of single-colored objects presegmented from their background. The single-color constraint allows us to fit a more comprehensive physical model to the object. We demonstrate that this permits us to perform realistic recoloring of objects lit by non-white illuminants, and multiple illuminants. Moreover, the model allows for more realistic handling of illuminant alteration of the scene. Recoloring results captured by uncalibrated cameras demonstrate that the proposed framework obtains realistic recoloring for complex natural images. Furthermore we use the model to transfer color between objects and show that the results are more realistic than existing color transfer methods.  
  Address Barcelona  
  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 1550-5499 ISBN 978-1-4577-1101-5 Medium  
  Area Expedition Conference ICCV  
  Notes CIC Approved no  
  Call Number Admin @ si @ BeW2011 Serial 1781  
Permanent link to this record
 

 
Author (down) Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou edit   pdf
doi  openurl
  Title Multi-Illuminant Estimation with Conditional Random Fields Type Journal Article
  Year 2014 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 23 Issue 1 Pages 83-95  
  Keywords color constancy; CRF; multi-illuminant  
  Abstract Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.  
  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 1057-7149 ISBN Medium  
  Area Expedition Conference  
  Notes CIC; LAMP; 600.074; 600.079 Approved no  
  Call Number Admin @ si @ BRW2014 Serial 2451  
Permanent link to this record
 

 
Author (down) Shida Beigpour edit  openurl
  Title Illumination and object reflectance modeling Type Book Whole
  Year 2013 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract More realistic and accurate models of the scene illumination and object reflectance can greatly improve the quality of many computer vision and computer graphics tasks. Using such model, a more profound knowledge about the interaction of light with object surfaces can be established which proves crucial to a variety of computer vision applications. In the current work, we investigate the various existing approaches to illumination and reflectance modeling and form an analysis on their shortcomings in capturing the complexity of real-world scenes. Based on this analysis we propose improvements to different aspects of reflectance and illumination estimation in order to more realistically model the real-world scenes in the presence of complex lighting phenomena (i.e, multiple illuminants, interreflections and shadows). Moreover, we captured our own multi-illuminant dataset which consists of complex scenes and illumination conditions both outdoor and in laboratory conditions. In addition we investigate the use of synthetic data to facilitate the construction of datasets and improve the process of obtaining ground-truth information.  
  Address Barcelona  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Joost Van de Weijer;Ernest Valveny  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes CIC Approved no  
  Call Number Admin @ si @ Bei2013 Serial 2267  
Permanent link to this record
 

 
Author (down) Shida Beigpour edit  openurl
  Title Physics-based Reflectance Estimation Applied to Recoloring Type Report
  Year 2009 Publication CVC Technical Report Abbreviated Journal  
  Volume 137 Issue Pages  
  Keywords  
  Abstract  
  Address  
  Corporate Author Computer Vision Center Thesis Master's thesis  
  Publisher Place of Publication Bellaterra, Barcelona Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes CIC Approved no  
  Call Number Admin @ si @ Bei2009 Serial 2396  
Permanent link to this record
 

 
Author (down) Shanxin Yuan; Guillermo Garcia-Hernando; Bjorn Stenger; Gyeongsik Moon; Ju Yong Chang; Kyoung Mu Lee; Pavlo Molchanov; Jan Kautz; Sina Honari; Liuhao Ge; Junsong Yuan; Xinghao Chen; Guijin Wang; Fan Yang; Kai Akiyama; Yang Wu; Qingfu Wan; Meysam Madadi; Sergio Escalera; Shile Li; Dongheui Lee; Iason Oikonomidis; Antonis Argyros; Tae-Kyun Kim edit   pdf
doi  openurl
  Title Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals Type Conference Article
  Year 2018 Publication 31st IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 2636 - 2645  
  Keywords Three-dimensional displays; Task analysis; Pose estimation; Two dimensional displays; Joints; Training; Solid modeling  
  Abstract In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include: (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points; (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data; (3) Discriminative methods still generalize poorly to unseen hand shapes; (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints.  
  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 CVPR  
  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ YGS2018 Serial 3115  
Permanent link to this record
 

 
Author (down) Sezer Karaoglu; Jan van Gemert; Theo Gevers edit  doi
openurl 
  Title Con-text: text detection using background connectivity for fine-grained object classification Type Conference Article
  Year 2013 Publication 21ST ACM International Conference on Multimedia Abbreviated Journal  
  Volume Issue Pages 757-760  
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  Abstract  
  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-MM  
  Notes ALTRES;ISE Approved no  
  Call Number Admin @ si @ KGG2013 Serial 2369  
Permanent link to this record
 

 
Author (down) Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil edit   pdf
openurl 
  Title Volumetric Anatomical Parameterization and Meshing for Inter-patient Liver Coordinate System Deffinition Type Conference Article
  Year 2013 Publication 16th International Conference on Medical Image Computing and Computer Assisted Intervention Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Nagoya; Japan; September 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 Medium  
  Area Expedition Conference MICCAI  
  Notes IAM Approved no  
  Call Number Admin @ si @ VGG2013 Serial 2301  
Permanent link to this record
 

 
Author (down) Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil edit   pdf
url  doi
isbn  openurl
  Title Optimal Medial Surface Generation for Anatomical Volume Representations Type Book Chapter
  Year 2012 Publication Abdominal Imaging. Computational and Clinical Applications Abbreviated Journal LNCS  
  Volume 7601 Issue Pages 265-273  
  Keywords Medial surface representation; volume reconstruction  
  Abstract Medial representations are a widely used technique in abdominal organ shape representation and parametrization. Those methods require good medial manifolds as a starting point. Any medial
surface used to parametrize a volume should be simple enough to allow an easy manipulation and complete enough to allow an accurate reconstruction of the volume. Obtaining good quality medial
surfaces is still a problem with current iterative thinning methods. This forces the usage of generic, pre-calculated medial templates that are adapted to the final shape at the cost of a drop in volume reconstruction.
This paper describes an operator for generation of medial structures that generates clean and complete manifolds well suited for their further use in medial representations of abdominal organ volumes. While being simpler than thinning surfaces, experiments show its high performance in volume reconstruction and preservation of medial surface main branching topology.
 
  Address Nice, France  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor Yoshida, Hiroyuki and Hawkes, David and Vannier, MichaelW.  
  Language Summary Language Original Title  
  Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-33611-9 Medium  
  Area Expedition Conference STACOM  
  Notes IAM Approved no  
  Call Number IAM @ iam @ VGG2012b Serial 1988  
Permanent link to this record
 

 
Author (down) Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil edit   pdf
doi  isbn
openurl 
  Title A medial map capturing the essential geometry of organs Type Conference Article
  Year 2012 Publication ISBI Workshop on Open Source Medical Image Analysis software Abbreviated Journal  
  Volume Issue Pages 1691 - 1694  
  Keywords Medial Surface Representation, Volume Reconstruction,Geometry , Image reconstruction , Liver , Manifolds , Shape , Surface morphology , Surface reconstruction  
  Abstract Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Accurate computation of one pixel wide medial surfaces is mandatory. Those surfaces must represent faithfully the geometry of the volume. Although morphological methods produce excellent results in 2D, their complexity and quality drops across dimensions, due to a more complex description of pixel neighborhoods. This paper introduces a continuous operator for accurate and efficient computation of medial structures of arbitrary dimension. Our experiments show its higher performance for medical imaging applications in terms of simplicity of medial structures and capability for reconstructing the anatomical volume  
  Address Barcelona,Spain  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1945-7928 ISBN 978-1-4577-1857-1 Medium  
  Area Expedition Conference ISBI  
  Notes IAM Approved no  
  Call Number IAM @ iam @ VGG2012a Serial 1989  
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