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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 | |
Volume | Issue | Pages | |||
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. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ BRJ2018 | Serial | 3252 | ||
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Author | Francisco Cruz; Oriol Ramos Terrades | ||||
Title | A probabilistic framework for handwritten text line segmentation | Type | Miscellaneous | ||
Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning | ||||
Abstract | We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer vision tasks. In this paper, we apply it to handwritten text line segmentation. We conduct several experiments that demonstrate that our method deal with common issues of this task, such as complex document layout or non-latin scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine tuning step. |
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Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ CrR2018 | Serial | 3253 | ||
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Author | Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone | ||||
Title | DSD: document sparse-based denoising algorithm | Type | Journal Article | ||
Year | 2019 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
Volume | 22 | Issue | 1 | Pages | 177–186 |
Keywords | Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models | ||||
Abstract | In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising. | ||||
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Notes | DAG; 600.097; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ DRT2019 | Serial | 3254 | ||
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Author | Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez | ||||
Title | System and method for video classification using a hybrid unsupervised and supervised multi-layer architecture | Type | Patent | ||
Year | 2018 | Publication | US9946933B2 | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | US9946933B2 | ||||
Abstract | A computer-implemented video classification method and system are disclosed. The method includes receiving an input video including a sequence of frames. At least one transformation of the input video is generated, each transformation including a sequence of frames. For the input video and each transformation, local descriptors are extracted from the respective sequence of frames. The local descriptors of the input video and each transformation are aggregated to form an aggregated feature vector with a first set of processing layers learned using unsupervised learning. An output classification value is generated for the input video, based on the aggregated feature vector with a second set of processing layers learned using supervised learning. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SGV2018 | Serial | 3255 | ||
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Author | Chris Bahnsen; David Vazquez; Antonio Lopez; Thomas B. Moeslund | ||||
Title | Learning to Remove Rain in Traffic Surveillance by Using Synthetic Data | Type | Conference Article | ||
Year | 2019 | Publication | 14th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
Volume | Issue | Pages | 123-130 | ||
Keywords | Rain Removal; Traffic Surveillance; Image Denoising | ||||
Abstract | Rainfall is a problem in automated traffic surveillance. Rain streaks occlude the road users and degrade the overall visibility which in turn decrease object detection performance. One way of alleviating this is by artificially removing the rain from the images. This requires knowledge of corresponding rainy and rain-free images. Such images are often produced by overlaying synthetic rain on top of rain-free images. However, this method fails to incorporate the fact that rain fall in the entire three-dimensional volume of the scene. To overcome this, we introduce training data from the SYNTHIA virtual world that models rain streaks in the entirety of a scene. We train a conditional Generative Adversarial Network for rain removal and apply it on traffic surveillance images from SYNTHIA and the AAU RainSnow datasets. To measure the applicability of the rain-removed images in a traffic surveillance context, we run the YOLOv2 object detection algorithm on the original and rain-removed frames. The results on SYNTHIA show an 8% increase in detection accuracy compared to the original rain image. Interestingly, we find that high PSNR or SSIM scores do not imply good object detection performance. | ||||
Address | Praga; Czech Republic; February 2019 | ||||
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Area | Expedition | Conference | VISIGRAPP | ||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ BVL2019 | Serial | 3256 | ||
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Author | W.Win; B.Bao; Q.Xu; Luis Herranz; Shuqiang Jiang | ||||
Title | Editorial Note: Efficient Multimedia Processing Methods and Applications | Type | Miscellaneous | ||
Year | 2019 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
Volume | 78 | Issue | 1 | Pages | |
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Notes | LAMP; 600.141; 600.120 | Approved | no | ||
Call Number | Admin @ si @ WBX2019 | Serial | 3257 | ||
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Author | Pau Rodriguez | ||||
Title | Towards Robust Neural Models for Fine-Grained Image Recognition | Type | Book Whole | ||
Year | 2019 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Fine-grained recognition, i.e. identifying similar subcategories of the same superclass, is central to human activity. Recognizing a friend, finding bacteria in microscopic imagery, or discovering a new kind of galaxy, are just but few examples. However, fine-grained image recognition is still a challenging computer vision task since the differences between two images of the same category can overwhelm the differences between two images of different fine-grained categories. In this regime, where the difference between two categories resides on subtle input changes, excessively invariant CNNs discard those details that help to discriminate between categories and focus on more obvious changes, yielding poor classification performance.
On the other hand, CNNs with too much capacity tend to memorize instance-specific details, thus causing overfitting. In this thesis,motivated by the potential impact of automatic fine-grained image recognition, we tackle the previous challenges and demonstrate that proper alignment of the inputs, multiple levels of attention, regularization, and explicitmodeling of the output space, results inmore accurate fine-grained recognitionmodels, that generalize better, and are more robust to intra-class variation. Concretely, we study the different stages of the neural network pipeline: input pre-processing, attention to regions, feature activations, and the label space. In each stage, we address different issues that hinder the recognition performance on various fine-grained tasks, and devise solutions in each chapter: i)We deal with the sensitivity to input alignment on fine-grained human facial motion such as pain. ii) We introduce an attention mechanism to allow CNNs to choose and process in detail the most discriminate regions of the image. iii)We further extend attention mechanisms to act on the network activations, thus allowing them to correct their predictions by looking back at certain regions, at different levels of abstraction. iv) We propose a regularization loss to prevent high-capacity neural networks to memorize instance details by means of almost-identical feature detectors. v)We finally study the advantages of explicitly modeling the output space within the error-correcting framework. As a result, in this thesis we demonstrate that attention and regularization seem promising directions to overcome the problems of fine-grained image recognition, as well as proper treatment of the input and the output space. |
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Address | March 2019 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Josep M. Gonfaus;Xavier Roca | |
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ISSN | ISBN | 978-84-948531-3-5 | Medium | ||
Area | Expedition | Conference | |||
Notes | ISE; 600.119 | Approved | no | ||
Call Number | Admin @ si @ Rod2019 | Serial | 3258 | ||
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Author | Xim Cerda-Company | ||||
Title | Understanding color vision: from psychophysics to computational modeling | Type | Book Whole | ||
Year | 2019 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | In this PhD we have approached the human color vision from two different points of view: psychophysics and computational modeling. First, we have evaluated 15 different tone-mapping operators (TMOs). We have conducted two experiments that
consider two different criteria: the first one evaluates the local relationships among intensity levels and the second one evaluates the global appearance of the tonemapped imagesw.r.t. the physical one (presented side by side). We conclude that the rankings depend on the criterion and they are not correlated. Considering both criteria, the best TMOs are KimKautz (Kim and Kautz, 2008) and Krawczyk (Krawczyk, Myszkowski, and Seidel, 2005). Another conclusion is that a more standardized evaluation criteria is needed to do a fair comparison among TMOs. Secondly, we have conducted several psychophysical experiments to study the color induction. We have studied two different properties of the visual stimuli: temporal frequency and luminance spatial distribution. To study the temporal frequency we defined equiluminant stimuli composed by both uniform and striped surrounds and we flashed them varying the flash duration. For uniform surrounds, the results show that color induction depends on both the flash duration and inducer’s chromaticity. As expected, in all chromatic conditions color contrast was induced. In contrast, for striped surrounds, we expected to induce color assimilation, but we observed color contrast or no induction. Since similar but not equiluminant striped stimuli induce color assimilation, we concluded that luminance differences could be a key factor to induce color assimilation. Thus, in a subsequent study, we have studied the luminance differences’ effect on color assimilation. We varied the luminance difference between the target region and its inducers and we observed that color assimilation depends on both this difference and the inducer’s chromaticity. For red-green condition (where the first inducer is red and the second one is green), color assimilation occurs in almost all luminance conditions. Instead, for green-red condition, color assimilation never occurs. Purple-lime and lime-purple chromatic conditions show that luminance difference is a key factor to induce color assimilation. When the target is darker than its surround, color assimilation is stronger in purple-lime, while when the target is brighter, color assimilation is stronger in lime-purple (’mirroring’ effect). Moreover, we evaluated whether color assimilation is due to luminance or brightness differences. Similarly to equiluminance condition, when the stimuli are equibrightness no color assimilation is induced. Our results support the hypothesis that mutual-inhibition plays a major role in color perception, or at least in color induction. Finally, we have defined a new firing rate model of color processing in the V1 parvocellular pathway. We have modeled two different layers of this cortical area: layers 4Cb and 2/3. Our model is a recurrent dynamic computational model that considers both excitatory and inhibitory cells and their lateral connections. Moreover, it considers the existent laminar differences and the cells’ variety. Thus, we have modeled both single- and double-opponent simple cells and complex cells, which are a pool of double-opponent simple cells. A set of sinusoidal drifting gratings have been used to test the architecture. In these gratings we have varied several spatial properties such as temporal and spatial frequencies, grating’s area and orientation. To reproduce the electrophysiological observations, the architecture has to consider the existence of non-oriented double-opponent cells in layer 4Cb and the lack of lateral connections between single-opponent cells. Moreover, we have tested our lateral connections simulating the center-surround modulation and we have reproduced physiological measurements where for high contrast stimulus, the result of the lateral connections is inhibitory, while it is facilitatory for low contrast stimulus. |
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Address | March 2019 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Xavier Otazu | |
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ISSN | ISBN | 978-84-948531-4-2 | Medium | ||
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Notes | NEUROBIT | Approved | no | ||
Call Number | Admin @ si @ Cer2019 | Serial | 3259 | ||
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Author | Sounak Dey; Palaiahnakote Shivakumara; K.S. Raghunanda; Umapada Pal; Tong Lu; G. Hemantha Kumar; Chee Seng Chan | ||||
Title | Script independent approach for multi-oriented text detection in scene image | Type | Journal Article | ||
Year | 2017 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
Volume | 242 | Issue | Pages | 96-112 | |
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Abstract | Developing a text detection method which is invariant to scripts in natural scene images is a challeng- ing task due to different geometrical structures of various scripts. Besides, multi-oriented of text lines in natural scene images make the problem more challenging. This paper proposes to explore ring radius transform (RRT) for text detection in multi-oriented and multi-script environments. The method finds component regions based on convex hull to generate radius matrices using RRT. It is a fact that RRT pro- vides low radius values for the pixels that are near to edges, constant radius values for the pixels that represent stroke width, and high radius values that represent holes created in background and convex hull because of the regular structures of text components. We apply k -means clustering on the radius matrices to group such spatially coherent regions into individual clusters. Then the proposed method studies the radius values of such cluster components that are close to the centroid and far from the cen- troid to detect text components. Furthermore, we have developed a Bangla dataset (named as ISI-UM dataset) and propose a semi-automatic system for generating its ground truth for text detection of arbi- trary orientations, which can be used by the researchers for text detection and recognition in the future. The ground truth will be released to public. Experimental results on our ISI-UM data and other standard datasets, namely, ICDAR 2013 scene, SVT and MSRA data, show that the proposed method outperforms the existing methods in terms of multi-lingual and multi-oriented text detection ability. | ||||
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Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ DSR2017 | Serial | 3260 | ||
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Author | Mikhail Mozerov; Fei Yang; Joost Van de Weijer | ||||
Title | Sparse Data Interpolation Using the Geodesic Distance Affinity Space | Type | Journal Article | ||
Year | 2019 | Publication | IEEE Signal Processing Letters | Abbreviated Journal | SPL |
Volume | 26 | Issue | 6 | Pages | 943 - 947 |
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Abstract | In this letter, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate its superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the paper on EpicFlow optical flow, which is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique. | ||||
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ MYW2019 | Serial | 3261 | ||
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Author | Julio C. S. Jacques Junior; Cagri Ozcinar; Marina Marjanovic; Xavier Baro; Gholamreza Anbarjafari; Sergio Escalera | ||||
Title | On the effect of age perception biases for real age regression | Type | Conference Article | ||
Year | 2019 | Publication | 14th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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Abstract | Automatic age estimation from facial images represents an important task in computer vision. This paper analyses the effect of gender, age, ethnic, makeup and expression attributes of faces as sources of bias to improve deep apparent age prediction. Following recent works where it is shown that apparent age labels benefit real age estimation, rather than direct real to real age regression, our main contribution is the integration, in an end-to-end architecture, of face attributes for apparent age prediction with an additional loss for real age regression. Experimental results on the APPA-REAL dataset indicate the proposed network successfully take advantage of the adopted attributes to improve both apparent and real age estimation. Our model outperformed a state-of-the-art architecture proposed to separately address apparent and real age regression. Finally, we present preliminary results and discussion of a proof of concept application using the proposed model to regress the apparent age of an individual based on the gender of an external observer. | ||||
Address | Lille; France; May 2019 | ||||
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Area | Expedition | Conference | FG | ||
Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ JOM2019 | Serial | 3262 | ||
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Author | Bojana Gajic; Ariel Amato; Ramon Baldrich; Carlo Gatta | ||||
Title | Bag of Negatives for Siamese Architectures | Type | Conference Article | ||
Year | 2019 | Publication | 30th British Machine Vision Conference | Abbreviated Journal | |
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Abstract | Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy. | ||||
Address | Cardiff; United Kingdom; September 2019 | ||||
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Area | Expedition | Conference | BMVC | ||
Notes | CIC; 600.140; 600.118 | Approved | no | ||
Call Number | Admin @ si @ GAB2019b | Serial | 3263 | ||
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Author | Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu | ||||
Title | Saliency for fine-grained object recognition in domains with scarce training data | Type | Journal Article | ||
Year | 2019 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 94 | Issue | Pages | 62-73 | |
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Abstract | This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate a large dataset. The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network’s performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline. | ||||
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Notes | LAMP; 600.109; 600.141; 600.120 | Approved | no | ||
Call Number | Admin @ si @ FGW2019 | Serial | 3264 | ||
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Author | Raul Gomez; Ali Furkan Biten; Lluis Gomez; Jaume Gibert; Marçal Rusiñol; Dimosthenis Karatzas | ||||
Title | Selective Style Transfer for Text | Type | Conference Article | ||
Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 805-812 | ||
Keywords | transfer; text style transfer; data augmentation; scene text detection | ||||
Abstract | This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means
transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available. |
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Address | Sydney; Australia; September 2019 | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.129; 600.135; 601.338; 601.310; 600.121 | Approved | no | ||
Call Number | GBG2019 | Serial | 3265 | ||
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Author | Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas | ||||
Title | Self-Supervised Learning from Web Data for Multimodal Retrieval | Type | Book Chapter | ||
Year | 2019 | Publication | Multi-Modal Scene Understanding Book | Abbreviated Journal | |
Volume | Issue | Pages | 279-306 | ||
Keywords | self-supervised learning; webly supervised learning; text embeddings; multimodal retrieval; multimodal embedding | ||||
Abstract | Self-Supervised learning from multimodal image and text data allows deep neural networks to learn powerful features with no need of human annotated data. Web and Social Media platforms provide a virtually unlimited amount of this multimodal data. In this work we propose to exploit this free available data to learn a multimodal image and text embedding, 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 proposed pipeline can learn from images with associated text without supervision and analyze the semantic structure of the learnt joint image and text embeddingspace. Weperformathoroughanalysisandperformancecomparisonoffivedifferentstateof the art 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 basedimageretrievaltask,andweclearlyoutperformstateoftheartintheMIRFlickrdatasetwhen 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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Notes | DAG; 600.129; 601.338; 601.310 | Approved | no | ||
Call Number | Admin @ si @ GGG2019 | Serial | 3266 | ||
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