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Author | Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas | ||||
Title | Learning to Learn from Web Data through Deep Semantic Embeddings | Type | Conference Article | ||
Year | 2018 | Publication | 15th European Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | 11134 | Issue | Pages | 514-529 | |
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Abstract | In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. | ||||
Address | Munich; Alemanya; September 2018 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCVW | ||
Notes | DAG; 600.129; 601.338; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GGG2018a | Serial | 3175 | ||
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Author | Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri | ||||
Title | Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction | Type | Journal Article | ||
Year | 2018 | Publication | Journal of Mathematical Imaging and Vision | Abbreviated Journal | JMIV |
Volume | 60 | Issue | 4 | Pages | 512-524 |
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Abstract | This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model. |
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Notes | DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129 | Approved | no | ||
Call Number | Admin @ si @ DMH2018a | Serial | 3062 | ||
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Author | Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund | ||||
Title | Back-dropout Transfer Learning for Action Recognition | Type | Journal Article | ||
Year | 2018 | Publication | IET Computer Vision | Abbreviated Journal | IETCV |
Volume | 12 | Issue | 4 | Pages | 484-491 |
Keywords | Learning (artificial intelligence); Pattern Recognition | ||||
Abstract | Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ RKM2018 | Serial | 3071 | ||
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Author | Stefan Schurischuster; Beatriz Remeseiro; Petia Radeva; Martin Kampel | ||||
Title | A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees | Type | Conference Article | ||
Year | 2018 | Publication | 15th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
Volume | 10882 | Issue | Pages | 465-473 | |
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Abstract | Varroa destructor is a parasite harming bee colonies. As the worldwide bee population is in danger, beekeepers as well as researchers are looking for methods to monitor the health of bee hives. In this context, we present a preliminary study to detect parasites on bee videos by means of image analysis and machine learning techniques. For this purpose, each video frame is analyzed individually to extract bee image patches, which are then processed to compute image descriptors and finally classified into mite and no mite bees. The experimental results demonstrated the adequacy of the proposed method, which will be a perfect stepping stone for a further bee monitoring system. | ||||
Address | Povoa de Varzim; Portugal; June 2018 | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICIAR | ||
Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ SRR2018a | Serial | 3110 | ||
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Author | Lei Kang; Juan Ignacio Toledo; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol | ||||
Title | Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition | Type | Conference Article | ||
Year | 2018 | Publication | 40th German Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 459-472 | ||
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Abstract | This paper proposes Convolve, Attend and Spell, an attention based sequence-to-sequence model for handwritten word recognition. The proposed architecture has three main parts: an encoder, consisting of a CNN and a bi-directional GRU, an attention mechanism devoted to focus on the pertinent features and a decoder formed by a one-directional GRU, able to spell the corresponding word, character by character. Compared with the recent state-of-the-art, our model achieves competitive results on the IAM dataset without needing any pre-processing step, predefined lexicon nor language model. Code and additional results are available in https://github.com/omni-us/research-seq2seq-HTR. | ||||
Address | Stuttgart; Germany; October 2018 | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | GCPR | ||
Notes | DAG; 600.097; 603.057; 302.065; 601.302; 600.084; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ KTR2018 | Serial | 3167 | ||
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Author | Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Hatem A. Rashwan; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig | ||||
Title | MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams | Type | Conference Article | ||
Year | 2018 | Publication | European Conference on Computer Vision workshops | Abbreviated Journal | |
Volume | Issue | Pages | 423-433 | ||
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Abstract | First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called “EgoFoodPlaces”. Experimental results shows promising results of food places classification recognition in egocentric photo-streams. | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LCNS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ECCVW | ||
Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ SRR2018b | Serial | 3185 | ||
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Author | Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados | ||||
Title | Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model | Type | Conference Article | ||
Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
Volume | Issue | Pages | 399-404 | ||
Keywords | Named entity recognition; Handwritten Text Recognition; neural networks | ||||
Abstract | When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing. |
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Address | Vienna; Austria; April 2018 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 600.097; 603.057; 601.311; 600.121 | Approved | no | ||
Call Number | Admin @ si @ CVF2018 | Serial | 3170 | ||
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Author | Antonio Lopez; David Vazquez; Gabriel Villalonga | ||||
Title | Data for Training Models, Domain Adaptation | Type | Book Chapter | ||
Year | 2018 | Publication | Intelligent Vehicles. Enabling Technologies and Future Developments | Abbreviated Journal | |
Volume | Issue | Pages | 395–436 | ||
Keywords | Driving simulator; hardware; software; interface; traffic simulation; macroscopic simulation; microscopic simulation; virtual data; training data | ||||
Abstract | Simulation can enable several developments in the field of intelligent vehicles. This chapter is divided into three main subsections. The first one deals with driving simulators. The continuous improvement of hardware performance is a well-known fact that is allowing the development of more complex driving simulators. The immersion in the simulation scene is increased by high fidelity feedback to the driver. In the second subsection, traffic simulation is explained as well as how it can be used for intelligent transport systems. Finally, it is rather clear that sensor-based perception and action must be based on data-driven algorithms. Simulation could provide data to train and test algorithms that are afterwards implemented in vehicles. These tools are explained in the third subsection. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ LVV2018 | Serial | 3047 | ||
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Author | Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska | ||||
Title | Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation | Type | Conference Article | ||
Year | 2018 | Publication | International MICCAI Brainlesion Workshop | Abbreviated Journal | |
Volume | 11384 | Issue | Pages | 393-405 | |
Keywords | Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution | ||||
Abstract | In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge. | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | MICCAIW | ||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ PSH2018 | Serial | 3251 | ||
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Author | Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Petia Radeva; Domenec Puig | ||||
Title | CuisineNet: Food Attributes Classification using Multi-scale Convolution Network | Type | Conference Article | ||
Year | 2018 | Publication | 21st International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
Volume | Issue | Pages | 365-372 | ||
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Abstract | Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. | ||||
Address | Roses; catalonia; October 2018 | ||||
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Area | Expedition | Conference | CCIA | ||
Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ SJR2018 | Serial | 3113 | ||
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Author | I. Sorodoc; S. Pezzelle; A. Herbelot; Mariella Dimiccoli; R. Bernardi | ||||
Title | Learning quantification from images: A structured neural architecture | Type | Journal Article | ||
Year | 2018 | Publication | Natural Language Engineering | Abbreviated Journal | NLE |
Volume | 24 | Issue | 3 | Pages | 363-392 |
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Abstract | Major advances have recently been made in merging language and vision representations. Most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even pre-school children) can abstract over raw multimodal data to perform certain types of higher level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like few, some and all. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in most fish are red, most encodes the proportion of fish which are red fish. In this paper, we study how well current neural network strategies model such relations. We propose a task where, given an image and a query expressed by an object–property pair, the system must return a quantifier expressing which proportions of the queried object have the queried property. Our contributions are twofold. First, we show that the best performance on this task involves coupling state-of-the-art attention mechanisms with a network architecture mirroring the logical structure assigned to quantifiers by classic linguistic formalisation. Second, we introduce a new balanced dataset of image scenarios associated with quantification queries, which we hope will foster further research in this area. | ||||
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Notes | MILAB; no menciona | Approved | no | ||
Call Number | Admin @ si @ SPH2018 | Serial | 3021 | ||
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Author | Lu Yu; Lichao Zhang; Joost Van de Weijer; Fahad Shahbaz Khan; Yongmei Cheng; C. Alejandro Parraga | ||||
Title | Beyond Eleven Color Names for Image Understanding | Type | Journal Article | ||
Year | 2018 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP |
Volume | 29 | Issue | 2 | Pages | 361-373 |
Keywords | Color name; Discriminative descriptors; Image classification; Re-identification; Tracking | ||||
Abstract | Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the task of visual tracking, person re-identification and image classification. | ||||
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Notes | LAMP; NEUROBIT; 600.068; 600.109; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YYW2018 | Serial | 3087 | ||
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Author | Pau Rodriguez; Josep M. Gonfaus; Guillem Cucurull; Xavier Roca; Jordi Gonzalez | ||||
Title | Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery | Type | Conference Article | ||
Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
Volume | 11212 | Issue | Pages | 357-372 | |
Keywords | Deep Learning; Convolutional Neural Networks; Attention | ||||
Abstract | We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, the proposed mechanism is modular, architecture-independent and efficient both in terms of parameters and computation required. Experiments show that networks augmented with our approach systematically improve their classification accuracy and become more robust to clutter. As a result, Wide Residual Networks augmented with our proposal surpasses the state of the art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford dogs, and UEC Food-100. | ||||
Address | Munich; September 2018 | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | ECCV | ||
Notes | ISE; 600.098; 602.121; 600.119 | Approved | no | ||
Call Number | Admin @ si @ RGC2018 | Serial | 3139 | ||
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Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
Title | Vegetation Index Estimation from Monospectral Images | Type | Conference Article | ||
Year | 2018 | Publication | 15th International Conference on Images Analysis and Recognition | Abbreviated Journal | |
Volume | 10882 | Issue | Pages | 353-362 | |
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Abstract | This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
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Address | Povoa de Varzim; Portugal; June 2018 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | ICIAR | ||
Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ SSV2018c | Serial | 3196 | ||
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Author | Mariella Dimiccoli; Cathal Gurrin; David J. Crandall; Xavier Giro; Petia Radeva | ||||
Title | Introduction to the special issue: Egocentric Vision and Lifelogging | Type | Journal Article | ||
Year | 2018 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal | JVCIR |
Volume | 55 | Issue | Pages | 352-353 | |
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ DGC2018 | Serial | 3187 | ||
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