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Author | Rahat Khan; Joost Van de Weijer; Dimosthenis Karatzas; Damien Muselet | ||||
Title | Towards multispectral data acquisition with hand-held devices | Type | Conference Article | ||
Year | 2013 | Publication | 20th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | 2053 - 2057 | ||
Keywords | Multispectral; mobile devices; color measurements | ||||
Abstract | We propose a method to acquire multispectral data with handheld devices with front-mounted RGB cameras. We propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and
blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results are promising and show that the accuracy of the spectral reconstruction improves in the range from 30-40% over the spectral reconstruction based on a single illuminant. Furthermore, we propose to compute sensor-illuminant aware linear basis by discarding the part of the reflectances that falls in the sensorilluminant null-space. We show experimentally that optimizing reflectance estimation on these new basis functions decreases the RMSE significantly over basis functions that are independent to sensor-illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops, opening up applications which are currently considered to be unrealistic. |
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Address | Melbourne; Australia; September 2013 | ||||
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Area | Expedition | Conference | ICIP | ||
Notes | CIC; DAG; 600.048 | Approved | no | ||
Call Number | Admin @ si @ KWK2013b | Serial | 2265 | ||
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Author | Jiaolong Xu; Sebastian Ramos; Xu Hu; David Vazquez; Antonio Lopez | ||||
Title | Multi-task Bilinear Classifiers for Visual Domain Adaptation | Type | Conference Article | ||
Year | 2013 | Publication | Advances in Neural Information Processing Systems Workshop | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Domain Adaptation; Pedestrian Detection; ADAS | ||||
Abstract | We propose a method that aims to lessen the significant accuracy degradation
that a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation. The bilinear classifier encodes the feature transformation and classification parameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines. |
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Address | Lake Tahoe; Nevada; USA; December 2013 | ||||
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Area | Expedition | Conference | NIPSW | ||
Notes | ADAS; 600.054; 600.057; 601.217;ISE | Approved | no | ||
Call Number | ADAS @ adas @ XRH2013 | Serial | 2340 | ||
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Author | Adriana Romero; Petia Radeva; Carlo Gatta | ||||
Title | Meta-parameter free unsupervised sparse feature learning | Type | Journal Article | ||
Year | 2015 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 37 | Issue | 8 | Pages | 1716-1722 |
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Abstract | We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well. | ||||
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Notes | MILAB; 600.068; 600.079; 601.160 | Approved | no | ||
Call Number | Admin @ si @ RRG2014b | Serial | 2594 | ||
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Author | Arka Ujjal Dey; Suman Ghosh; Ernest Valveny | ||||
Title | Don't only Feel Read: Using Scene text to understand advertisements | Type | Conference Article | ||
Year | 2018 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
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Abstract | We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks. | ||||
Address | Salt Lake City; Utah; USA; June 2018 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ DGV2018 | Serial | 3551 | ||
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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 | ||||
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Area | Expedition | Conference | WiCV | ||
Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ DBR2018 | Serial | 3222 | ||
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Author | Zhengying Liu; Zhen Xu; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu | ||||
Title | Towards automated computer vision: analysis of the AutoCV challenges 2019 | Type | Journal Article | ||
Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 135 | Issue | Pages | 196-203 | |
Keywords | Computer vision; AutoML; Deep learning | ||||
Abstract | We present the results of recent challenges in Automated Computer Vision (AutoCV, renamed here for clarity AutoCV1 and AutoCV2, 2019), which are part of a series of challenge on Automated Deep Learning (AutoDL). These two competitions aim at searching for fully automated solutions for classification tasks in computer vision, with an emphasis on any-time performance. The first competition was limited to image classification while the second one included both images and videos. Our design imposed to the participants to submit their code on a challenge platform for blind testing on five datasets, both for training and testing, without any human intervention whatsoever. Winning solutions adopted deep learning techniques based on already published architectures, such as AutoAugment, MobileNet and ResNet, to reach state-of-the-art performance in the time budget of the challenge (only 20 minutes of GPU time). The novel contributions include strategies to deliver good preliminary results at any time during the learning process, such that a method can be stopped early and still deliver good performance. This feature is key for the adoption of such techniques by data analysts desiring to obtain rapidly preliminary results on large datasets and to speed up the development process. The soundness of our design was verified in several aspects: (1) Little overfitting of the on-line leaderboard providing feedback on 5 development datasets was observed, compared to the final blind testing on the 5 (separate) final test datasets, suggesting that winning solutions might generalize to other computer vision classification tasks; (2) Error bars on the winners’ performance allow us to say with confident that they performed significantly better than the baseline solutions we provided; (3) The ranking of participants according to the any-time metric we designed, namely the Area under the Learning Curve, was different from that of the fixed-time metric, i.e. AUC at the end of the fixed time budget. We released all winning solutions under open-source licenses. At the end of the AutoDL challenge series, all data of the challenge will be made publicly available, thus providing a collection of uniformly formatted datasets, which can serve to conduct further research, particularly on meta-learning. | ||||
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Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ LXE2020 | Serial | 3427 | ||
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Author | Zhengying Liu; Zhen Xu; Shangeth Rajaa; Meysam Madadi; Julio C. S. Jacques Junior; Sergio Escalera; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu; Isabelle Guyon | ||||
Title | Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019 | Type | Conference Article | ||
Year | 2020 | Publication | Proceedings of Machine Learning Research | Abbreviated Journal | |
Volume | 123 | Issue | Pages | 242-252 | |
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Abstract | We present the design and results of recent competitions in Automated Deep Learning (AutoDL). In the AutoDL challenge series 2019, we organized 5 machine learning challenges: AutoCV, AutoCV2, AutoNLP, AutoSpeech and AutoDL. The first 4 challenges concern each a specific application domain, such as computer vision, natural language processing and speech recognition. At the time of March 2020, the last challenge AutoDL is still on-going and we only present its design. Some highlights of this work include: (1) a benchmark suite of baseline AutoML solutions, with emphasis on domains for which Deep Learning methods have had prior success (image, video, text, speech, etc); (2) a novel any-time learning framework, which opens doors for further theoretical consideration; (3) a repository of around 100 datasets (from all above domains) over half of which are released as public datasets to enable research on meta-learning; (4) analyses revealing that winning solutions generalize to new unseen datasets, validating progress towards universal AutoML solution; (5) open-sourcing of the challenge platform, the starting kit, the dataset formatting toolkit, and all winning solutions (All information available at {autodl.chalearn.org}). | ||||
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Area | Expedition | Conference | NEURIPS | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ LXR2020 | Serial | 3500 | ||
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Author | Zhengying Liu; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Sergio Escalera; Adrien Pavao; Hugo Jair Escalante; Wei-Wei Tu; Zhen Xu; Sebastien Treguer | ||||
Title | AutoCV Challenge Design and Baseline Results | Type | Conference Article | ||
Year | 2019 | Publication | La Conference sur l’Apprentissage Automatique | Abbreviated Journal | |
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Abstract | We present the design and beta tests of a new machine learning challenge called AutoCV (for Automated Computer Vision), which is the first event in a series of challenges we are planning on the theme of Automated Deep Learning. We target applications for which Deep Learning methods have had great success in the past few years, with the aim of pushing the state of the art in fully automated methods to design the architecture of neural networks and train them without any human intervention. The tasks are restricted to multi-label image classification problems, from domains including medical, areal, people, object, and handwriting imaging. Thus the type of images will vary a lot in scales, textures, and structure. Raw data are provided (no features extracted), but all datasets are formatted in a uniform tensor manner (although images may have fixed or variable sizes within a dataset). The participants's code will be blind tested on a challenge platform in a controlled manner, with restrictions on training and test time and memory limitations. The challenge is part of the official selection of IJCNN 2019. | ||||
Address | Toulouse; Francia; July 2019 | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ LGJ2019 | Serial | 3323 | ||
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Author | Dustin Carrion Ojeda; Hong Chen; Adrian El Baz; Sergio Escalera; Chaoyu Guan; Isabelle Guyon; Ihsan Ullah; Xin Wang; Wenwu Zhu | ||||
Title | NeurIPS’22 Cross-Domain MetaDL competition: Design and baseline results | Type | Conference Article | ||
Year | 2022 | Publication | Understanding Social Behavior in Dyadic and Small Group Interactions | Abbreviated Journal | |
Volume | 191 | Issue | Pages | 24-37 | |
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Abstract | We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on “cross-domain” meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challenges the participants to solve “any-way” and “any-shot” problems drawn from various domains (healthcare, ecology, biology, manufacturing, and others), chosen for their humanitarian and societal impact. To that end, we created Meta-Album, a meta-dataset of 40 image classification datasets from 10 domains, from which we carve out tasks with any number of “ways” (within the range 2-20) and any number of “shots” (within the range 1-20). The competition is with code submission, fully blind-tested on the CodaLab challenge platform. The code of the winners will be open-sourced, enabling the deployment of automated machine learning solutions for few-shot image classification across several domains. | ||||
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Area | Expedition | Conference | PMLR | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ CCB2022 | Serial | 3802 | ||
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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 | |
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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 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | HuPBA;MV; | Approved | no | ||
Call Number | ETM2016 | Serial | 2849 | ||
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Author | Aitor Alvarez-Gila; Joost Van de Weijer; Yaxing Wang; Estibaliz Garrote | ||||
Title | MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation | Type | Conference Article | ||
Year | 2022 | Publication | 29th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | multi-view; cross-view; semantic segmentation; synthetic dataset | ||||
Abstract | We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view datasets, MVMO features wide baselines between cameras and high density of objects, which lead to large disparities, heavy occlusions and view-dependent object appearance. Single view semantic segmentation is hindered by self and inter-object occlusions that could benefit from additional viewpoints. Therefore, we expect that MVMO will propel research in multi-view semantic segmentation and cross-view semantic transfer. We also provide baselines that show that new research is needed in such fields to exploit the complementary information of multi-view setups 1 . | ||||
Address | Bordeaux; France; October2022 | ||||
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Area | Expedition | Conference | ICIP | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ AWW2022 | Serial | 3781 | ||
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Author | Jorge Bernal; Fernando Vilariño; F. Javier Sanchez; M. Arnold; Anarta Ghosh; Gerard Lacey | ||||
Title | Experts vs Novices: Applying Eye-tracking Methodologies in Colonoscopy Video Screening for Polyp Search | Type | Conference Article | ||
Year | 2014 | Publication | 2014 Symposium on Eye Tracking Research and Applications | Abbreviated Journal | |
Volume | Issue | Pages | 223-226 | ||
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Abstract | We present in this paper a novel study aiming at identifying the differences in visual search patterns between physicians of diverse levels of expertise during the screening of colonoscopy videos. Physicians were clustered into two groups -experts and novices- according to the number of procedures performed, and fixations were captured by an eye-tracker device during the task of polyp search in different video sequences. These fixations were integrated into heat maps, one for each cluster. The obtained maps were validated over a ground truth consisting of a mask of the polyp, and the comparison between experts and novices was performed by using metrics such as reaction time, dwelling time and energy concentration ratio. Experimental results show a statistically significant difference between experts and novices, and the obtained maps show to be a useful tool for the characterisation of the behaviour of each group. | ||||
Address | USA; March 2014 | ||||
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ISSN | ISBN | 978-1-4503-2751-0 | Medium | ||
Area | Expedition | Conference | ETRA | ||
Notes | MV; 600.047; 600.060;SIAI | Approved | no | ||
Call Number | Admin @ si @ BVS2014 | Serial | 2448 | ||
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Author | Carles Sanchez; Jorge Bernal; Debora Gil; F. Javier Sanchez | ||||
Title | On-line lumen centre detection in gastrointestinal and respiratory endoscopy | Type | Conference Article | ||
Year | 2013 | Publication | Second International Workshop Clinical Image-Based Procedures | Abbreviated Journal | |
Volume | 8361 | Issue | Pages | 31-38 | |
Keywords | Lumen centre detection; Bronchoscopy; Colonoscopy | ||||
Abstract | We present in this paper a novel lumen centre detection for gastrointestinal and respiratory endoscopic images. The proposed method is based on the appearance and geometry of the lumen, which we defined as the darkest image region which centre is a hub of image gradients. Experimental results validated on the first public annotated gastro-respiratory database prove the reliability of the method for a wide range of images (with precision over 95 %). | ||||
Address | Nagoya; Japan; September 2013 | ||||
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Publisher | Springer International Publishing | Place of Publication | Editor | Erdt, Marius and Linguraru, Marius George and Oyarzun Laura, Cristina and Shekhar, Raj and Wesarg, Stefan and González Ballester, Miguel Angel and Drechsler, Klaus | |
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | 978-3-319-05665-4 | Medium | ||
Area | 800 | Expedition | Conference | CLIP | |
Notes | MV; IAM; 600.047; 600.044; 600.060 | Approved | no | ||
Call Number | Admin @ si @ SBG2013 | Serial | 2302 | ||
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Author | Swathikiran Sudhakaran; Sergio Escalera;Oswald Lanz | ||||
Title | Learning to Recognize Actions on Objects in Egocentric Video with Attention Dictionaries | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
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Abstract | We present EgoACO, a deep neural architecture for video action recognition that learns to pool action-context-object descriptors from frame level features by leveraging the verb-noun structure of action labels in egocentric video datasets. The core component of EgoACO is class activation pooling (CAP), a differentiable pooling operation that combines ideas from bilinear pooling for fine-grained recognition and from feature learning for discriminative localization. CAP uses self-attention with a dictionary of learnable weights to pool from the most relevant feature regions. Through CAP, EgoACO learns to decode object and scene context descriptors from video frame features. For temporal modeling in EgoACO, we design a recurrent version of class activation pooling termed Long Short-Term Attention (LSTA). LSTA extends convolutional gated LSTM with built-in spatial attention and a re-designed output gate. Action, object and context descriptors are fused by a multi-head prediction that accounts for the inter-dependencies between noun-verb-action structured labels in egocentric video datasets. EgoACO features built-in visual explanations, helping learning and interpretation. Results on the two largest egocentric action recognition datasets currently available, EPIC-KITCHENS and EGTEA, show that by explicitly decoding action-context-object descriptors, EgoACO achieves state-of-the-art recognition performance. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ SEL2021 | Serial | 3656 | ||
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Author | Francesco Ciompi; Oriol Pujol; Petia Radeva | ||||
Title | ECOC-DRF: Discriminative random fields based on error correcting output codes | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 6 | Pages | 2193-2204 |
Keywords | Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models | ||||
Abstract | We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments. | ||||
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Notes | LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 | Approved | no | ||
Call Number | Admin @ si @ CPR2014b | Serial | 2470 | ||
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