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Author |
Fernando Vilariño; Dimosthenis Karatzas |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
The Library Living Lab |
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2015 |
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Open Living Lab Days |
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Istanbul; Turkey; August 2015 |
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OLLD |
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MV; DAG;SIAI |
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Admin @ si @ViK2015 |
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2797 |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Computer Vision and Performing Arts |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2015 |
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Korean Scholars of Marketing Science |
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Seoul; Korea; October 2015 |
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KAMS |
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MV;SIAI |
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Admin @ si @Vil2015 |
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2799 |
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Author |
Fernando Vilariño; Dan Norton; Onur Ferhat |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Memory Fields: DJs in the Library |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2015 |
Publication |
21 st Symposium of Electronic Arts |
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Vancouver; Canada; August 2015 |
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ISEA |
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;SIAI |
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Admin @ si @VNF2015 |
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2800 |
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Author |
Fernando Vilariño; Dan Norton; Onur Ferhat |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
The Eye Doesn't Click – Eyetracking and Digital Content Interaction |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2016 |
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4S/EASST Conference |
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Barcelona; Spain; September 2016 |
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EASST |
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MV; 600.097;SIAI |
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no |
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Admin @ si @VNF2016 |
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2801 |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Giving Value to digital collections in the Public Library |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2016 |
Publication |
Librarian 2020 |
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Brussels; Belgium; October 2016 |
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LIB |
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MV; 600.097;SIAI |
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no |
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Call Number |
Admin @ si @Vil2016a |
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2802 |
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Author |
Fernando Vilariño; Dimosthenis Karatzas |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
A Living Lab approach for Citizen Science in Libraries |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2016 |
Publication |
1st International ECSA Conference |
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Berlin; Germany; May 2016 |
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ECSA |
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MV; DAG; 600.084; 600.097;SIAI |
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no |
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Admin @ si @ViK2016 |
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2804 |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Dissemination, creation and education from archives: Case study of the collection of Digitized Visual Poems from Joan Brossa Foundation |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2016 |
Publication |
International Workshop on Poetry: Archives, Poetries and Receptions |
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Barcelona; Spain; October 2016 |
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POETRY |
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MV; 600.097;SIAI |
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no |
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Admin @ si @Vil2016b |
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2805 |
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Author |
Cristhian A. Aguilera-Carrasco; F. Aguilera; Angel Sappa; C. Aguilera; Ricardo Toledo |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Learning cross-spectral similarity measures with deep convolutional neural networks |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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Year |
2016 |
Publication |
29th IEEE Conference on Computer Vision and Pattern Recognition Worshops |
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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. |
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Las vegas; USA; June 2016 |
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CVPRW |
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ADAS; 600.086; 600.076 |
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no |
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Admin @ si @AAS2016 |
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2809 |
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Author |
Daniel Hernandez; Lukas Schneider; Antonio Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan C. Moure |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Slanted Stixels: Representing San Francisco's Steepest Streets} |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2017 |
Publication |
28th British Machine Vision Conference |
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In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. |
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London; uk; September 2017 |
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BMVC |
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ADAS; 600.118 |
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ADAS @ adas @ HSE2017a |
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2945 |
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Author |
Ozan Caglayan; Walid Aransa; Adrien Bardet; Mercedes Garcia-Martinez; Fethi Bougares; Loic Barrault; Marc Masana; Luis Herranz; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Title |
LIUM-CVC Submissions for WMT17 Multimodal Translation Task |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2017 |
Publication |
2nd Conference on Machine Translation |
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This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU. |
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WMT |
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LAMP; 600.106; 600.120 |
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Admin @ si @ CAB2017 |
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3035 |
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Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville |
![download PDF file pdf](img/file_PDF.gif)
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Title |
PixelVAE: A Latent Variable Model for Natural Images |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2017 |
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5th International Conference on Learning Representations |
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Deep Learning; Unsupervised Learning |
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Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and generate samples that preserve global structure but tend to suffer from image blurriness. PixelCNNs model sharp contours and details very well, but lack an explicit latent representation and have difficulty modeling large-scale structure in a computationally efficient way. In this paper, we present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. The resulting architecture achieves state-of-the-art log-likelihood on binarized MNIST. We extend PixelVAE to a hierarchy of multiple latent variables at different scales; this hierarchical model achieves competitive likelihood on 64x64 ImageNet and generates high-quality samples on LSUN bedrooms. |
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Toulon; France; April 2017 |
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ICLR |
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ADAS; 600.085; 600.076; 601.281; 600.118 |
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ADAS @ adas @ GKA2017 |
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2815 |
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Victor Ponce; Baiyu Chen; Marc Oliu; Ciprian Corneanu; Albert Clapes; Isabelle Guyon; Xavier Baro; Hugo Jair Escalante; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
ChaLearn LAP 2016: First Round Challenge on First Impressions – Dataset and Results |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2016 |
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14th European Conference on Computer Vision Workshops |
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Behavior Analysis; Personality Traits; First Impressions |
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This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the rst round of the competition. The goal of the competition was to automatically evaluate ve \apparent“ personality traits (the so-called \Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by tting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source
platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the nal phase. Despite the diculty of the task, the teams made great advances in this round of the challenge. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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HuPBA;MV; 600.063 |
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Admin @ si @ PCP2016 |
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2828 |
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Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi |
![download PDF file pdf](img/file_PDF.gif)
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Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2016 |
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19th International Conference of the Catalan Association for Artificial Intelligence |
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computer vision; video analysis; object recognition; tracking; behaviour; social; decapod; Nephrops norvegicus |
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Automated video and image analysis can be a very efficient tool to analyze
animal behavior based on sociality, especially in hard access environments
for researchers. The understanding of this social behavior can play a key role in the sustainable design of capture policies of many species. This paper proposes the use of computer vision algorithms to identify and track a specific specie, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior.
This emergent behavior is modulated by the day-night cycle, but their social
interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory pools, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-ofthe-art computer vision algorithms to distinguish and track the different animals’ movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community. |
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Barcelona; Spain; October 2016 |
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CCIA |
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OR;MV; |
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Admin @ si @ GMS2016 |
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2816 |
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Author |
Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi |
![download PDF file pdf](img/file_PDF.gif)
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Using ORB, BoW and SVM to identificate and track tagged Norway lobster Nephrops Norvegicus (L.) |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2016 |
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3rd International Conference on Maritime Technology and Engineering |
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Sustainable capture policies of many species strongly depend on the understanding of their social behaviour. Nevertheless, the analysis of emergent behaviour in marine species poses several challenges. Usually animals are captured and observed in tanks, and their behaviour is inferred from their dynamics and interactions. Therefore, researchers must deal with thousands of hours of video data. Without loss of generality, this paper proposes a computer
vision approach to identify and track specific species, the Norway lobster, Nephrops norvegicus. We propose an identification scheme were animals are marked using black and white tags with a geometric shape in the center (holed
triangle, filled triangle, holed circle and filled circle). Using a massive labelled dataset; we extract local features based on the ORB descriptor. These features are a posteriori clustered, and we construct a Bag of Visual Words feature vector per animal. This approximation yields us invariance to rotation
and translation. A SVM classifier achieves generalization results above 99%. In a second contribution, we will make the code and training data publically available. |
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Lisboa; Portugal; July 2016 |
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MARTECH |
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OR;MV; |
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Admin @ si @ GMS2016b |
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2817 |
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Vassileios Balntas; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk |
![download PDF file pdf](img/file_PDF.gif)
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Learning local feature descriptors with triplets and shallow convolutional neural networks |
Type ![sorted by Type field, ascending order (up)](img/sort_asc.gif) |
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2016 |
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27th British Machine Vision Conference |
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It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives.
We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets. |
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York; UK; September 2016 |
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BMVC |
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ADAS; 600.086 |
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Admin @ si @ BRP2016 |
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2818 |
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