| Records |
Author  |
Suman Ghosh |
| Title |
Word Spotting and Recognition in Images from Heterogeneous Sources A |
Type |
Book Whole |
| Year |
2018 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
Text is the most common way of information sharing from ages. With recent development of personal images databases and handwritten historic manuscripts the demand for algorithms to make these databases accessible for browsing and indexing are in rise. Enabling search or understanding large collection of manuscripts or image databases needs fast and robust methods. Researchers have found different ways to represent cropped words for understanding and matching, which works well when words are already segmented. However there is no trivial way to extend these for non-segmented documents. In this thesis we explore different methods for text retrieval and recognition from unsegmented document and scene images. Two different ways of representation exist in literature, one uses a fixed length representation learned from cropped words and another a sequence of features of variable length. Throughout this thesis, we have studied both these representation for their suitability in segmentation free understanding of text. In the first part we are focused on segmentation free word spotting using a fixed length representation. We extended the use of the successful PHOC (Pyramidal Histogram of Character) representation to segmentation free retrieval. In the second part of the thesis, we explore sequence based features and finally, we propose a unified solution where the same framework can generate both kind of representations. |
| Address |
November 2018 |
| Corporate Author |
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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Ernest Valveny |
| Language |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-84-948531-0-4 |
Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ Gho2018 |
Serial |
3217 |
| Permanent link to this record |
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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 |
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| Volume |
10882 |
Issue |
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Pages |
465-473 |
| Keywords |
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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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Place of Publication |
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Abbreviated Series Title |
LNCS |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
ICIAR |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ SRR2018a |
Serial |
3110 |
| Permanent link to this record |
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| Author |
Stefan Lonn; Petia Radeva; Mariella Dimiccoli |
| Title |
A picture is worth a thousand words but how to organize thousands of pictures? |
Type |
Miscellaneous |
| Year |
2018 |
Publication |
Arxiv |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization. |
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Medium |
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Expedition |
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Conference |
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| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LRD2018 |
Serial |
3111 |
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| Author |
Spyridon Bakas; Mauricio Reyes; Andras Jakab; Stefan Bauer; Markus Rempfler; Alessandro Crimi; Russell Takeshi Shinohara; Christoph Berger; Sung Min Ha; Martin Rozycki; Marcel Prastawa; Esther Alberts; Jana Lipkova; John Freymann; Justin Kirby; Michel Bilello; Hassan Fathallah-Shaykh; Roland Wiest; Jan Kirschke; Benedikt Wiestler; Rivka Colen; Aikaterini Kotrotsou; Pamela Lamontagne; Daniel Marcus; Mikhail Milchenko; Arash Nazeri; Marc-Andre Weber; Abhishek Mahajan; Ujjwal Baid; Dongjin Kwon; Manu Agarwal; Mahbubul Alam; Alberto Albiol; Antonio Albiol; Varghese Alex; Tuan Anh Tran; Tal Arbel; Aaron Avery; Subhashis Banerjee; Thomas Batchelder; Kayhan Batmanghelich; Enzo Battistella; Martin Bendszus; Eze Benson; Jose Bernal; George Biros; Mariano Cabezas; Siddhartha Chandra; Yi-Ju Chang; Joseph Chazalon; Shengcong Chen; Wei Chen; Jefferson Chen; Kun Cheng; Meinel Christoph; Roger Chylla; Albert Clérigues; Anthony Costa; Xiaomeng Cui; Zhenzhen Dai; Lutao Dai; Eric Deutsch; Changxing Ding; Chao Dong; Wojciech Dudzik; Theo Estienne; Hyung Eun Shin; Richard Everson; Jonathan Fabrizio; Longwei Fang; Xue Feng; Lucas Fidon; Naomi Fridman; Huan Fu; David Fuentes; David G Gering; Yaozong Gao; Evan Gates; Amir Gholami; Mingming Gong; Sandra Gonzalez-Villa; J Gregory Pauloski; Yuanfang Guan; Sheng Guo; Sudeep Gupta; Meenakshi H Thakur; Klaus H Maier-Hein; Woo-Sup Han; Huiguang He; Aura Hernandez-Sabate; Evelyn Herrmann; Naveen Himthani; Winston Hsu; Cheyu Hsu; Xiaojun Hu; Xiaobin Hu; Yan Hu; Yifan Hu; Rui Hua |
| Title |
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge |
Type |
Miscellaneous |
| Year |
2018 |
Publication |
Arxiv |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
BraTS; challenge; brain; tumor; segmentation; machine learning; glioma; glioblastoma; radiomics; survival; progression; RECIST |
| Abstract |
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multiparametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e. 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in preoperative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. |
| Address |
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Summary Language |
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Edition |
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ISBN |
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Expedition |
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Conference |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ BRJ2018 |
Serial |
3252 |
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| Author |
Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados; Umapada Pal |
| Title |
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch |
Type |
Conference Article |
| Year |
2018 |
Publication |
24th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
916 - 921 |
| Keywords |
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| Abstract |
In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets. |
| Address |
Beijing; China; August 2018 |
| Corporate Author |
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Thesis |
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Place of Publication |
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Summary Language |
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Original Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG; 602.167; 602.168; 600.097; 600.084; 600.121; 600.129 |
Approved |
no |
| Call Number |
Admin @ si @ DDG2018b |
Serial |
3152 |
| Permanent link to this record |
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| Author |
Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados |
| Title |
Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework |
Type |
Conference Article |
| Year |
2018 |
Publication |
14th Asian Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset. |
| Address |
Perth; Australia; December 2018 |
| Corporate Author |
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Thesis |
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Place of Publication |
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Summary Language |
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Original Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
ACCV |
| Notes |
DAG; 600.097; 600.121; 600.129 |
Approved |
no |
| Call Number |
Admin @ si @ DDG2018a |
Serial |
3151 |
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| Author |
Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal |
| Title |
SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification |
Type |
Miscellaneous |
| Year |
2018 |
Publication |
Arxiv |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction. |
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Expedition |
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Conference |
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| Notes |
DAG; 600.097; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ DDT2018 |
Serial |
3085 |
| Permanent link to this record |
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| Author |
Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas |
| Title |
Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets |
Type |
Conference Article |
| Year |
2018 |
Publication |
International Workshop on Artificial Intelligence and Pattern Recognition |
Abbreviated Journal |
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| Volume |
11047 |
Issue |
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Pages |
34-42 |
| Keywords |
Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis |
| Abstract |
The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall. |
| Address |
Cuba; September 2018 |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Edition |
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ISBN |
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Conference |
IWAIPR |
| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ BGÑ2018 |
Serial |
3237 |
| Permanent link to this record |
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| Author |
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 |
| 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 |
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| Volume |
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Issue |
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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 |
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Conference |
CVPR |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ YGS2018 |
Serial |
3115 |
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| Author |
Sergio Escalera; Markus Weimer; Mikhail Burtsev; Valentin Malykh; Varvara Logacheva; Ryan Lowe; Iulian Vlad Serban; Yoshua Bengio; Alexander Rudnicky; Alan W. Black; Shrimai Prabhumoye; Łukasz Kidzinski; Mohanty Sharada; Carmichael Ong; Jennifer Hicks; Sergey Levine; Marcel Salathe; Scott Delp; Iker Huerga; Alexander Grigorenko; Leifur Thorbergsson; Anasuya Das; Kyla Nemitz; Jenna Sandker; Stephen King; Alexander S. Ecker; Leon A. Gatys; Matthias Bethge; Jordan Boyd Graber; Shi Feng; Pedro Rodriguez; Mohit Iyyer; He He; Hal Daume III; Sean McGregor; Amir Banifatemi; Alexey Kurakin; Ian Goodfellow; Samy Bengio |
| Title |
Introduction to NIPS 2017 Competition Track |
Type |
Book Chapter |
| Year |
2018 |
Publication |
The NIPS ’17 Competition: Building Intelligent Systems |
Abbreviated Journal |
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Issue |
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Pages |
1-23 |
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| Abstract |
Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?
In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter. |
| Address |
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Springer |
Place of Publication |
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Editor |
Sergio Escalera; Markus Weimer |
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ISBN |
978-3-319-94042-7 |
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| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ EWB2018 |
Serial |
3200 |
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| Author |
Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon |
| Title |
Looking at People Special Issue |
Type |
Journal Article |
| Year |
2018 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
126 |
Issue |
2-4 |
Pages |
141-143 |
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| Notes |
HUPBA; ISE; 600.119 |
Approved |
no |
| Call Number |
Admin @ si @ EGJ2018 |
Serial |
3093 |
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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 |
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| Volume |
11384 |
Issue |
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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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LNCS |
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MICCAIW |
| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ PSH2018 |
Serial |
3251 |
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| Author |
Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
| Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
Type |
Journal Article |
| Year |
2018 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
80 |
Issue |
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Pages |
64-82 |
| Keywords |
Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
| Abstract |
Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
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DAG; 600.097; 600.121 |
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Admin @ si @ RSJ2018 |
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3096 |
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Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine |
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Journal Article |
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2018 |
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Entropy |
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ENTROPY |
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20 |
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11 |
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809 |
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hand sign language; deep learning; restricted Boltzmann machine (RBM); multi-modal; profoundly deaf; noisy image |
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In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey’s Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets. |
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HUPBA; no proj |
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no |
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Admin @ si @ RKE2018 |
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3198 |
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Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
| Title |
Learning to Learn from Web Data through Deep Semantic Embeddings |
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2018 |
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15th European Conference on Computer Vision Workshops |
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11134 |
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514-529 |
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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. |
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Munich; Alemanya; September 2018 |
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ECCVW |
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DAG; 600.129; 601.338; 600.121 |
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Admin @ si @ GGG2018a |
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3175 |
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