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| Author | Xim Cerda-Company; Xavier Otazu; Nilai Sallent; C. Alejandro Parraga | ||||
| Title | The effect of luminance differences on color assimilation | Type | Journal Article | ||
| Year | 2018 | Publication | Journal of Vision | Abbreviated Journal | JV |
| Volume | 18 | Issue | 11 | Pages | 10-10 |
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| Abstract | The color appearance of a surface depends on the color of its surroundings (inducers). When the perceived color shifts towards that of the surroundings, the effect is called “color assimilation” and when it shifts away from the surroundings it is called “color contrast.” There is also evidence that the phenomenon depends on the spatial configuration of the inducer, e.g., uniform surrounds tend to induce color contrast and striped surrounds tend to induce color assimilation. However, previous work found that striped surrounds under certain conditions do not induce color assimilation but induce color contrast (or do not induce anything at all), suggesting that luminance differences and high spatial frequencies could be key factors in color assimilation. Here we present a new psychophysical study of color assimilation where we assessed the contribution of luminance differences (between the target and its surround) present in striped stimuli. Our results show that luminance differences are key factors in color assimilation for stimuli varying along the s axis of MacLeod-Boynton color space, but not for stimuli varying along the l axis. This asymmetry suggests that koniocellular neural mechanisms responsible for color assimilation only contribute when there is a luminance difference, supporting the idea that mutual-inhibition has a major role in color induction. | ||||
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| Notes | NEUROBIT; 600.120; 600.128 | Approved | no | ||
| Call Number | Admin @ si @ COS2018 | Serial | 3148 | ||
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| Author | Domicele Jonauskaite; Nele Dael; C. Alejandro Parraga; Laetitia Chevre; Alejandro Garcia Sanchez; Christine Mohr | ||||
| Title | Stripping #The Dress: The importance of contextual information on inter-individual differences in colour perception | Type | Journal Article | ||
| Year | 2018 | Publication | Psychological Research | Abbreviated Journal | PSYCHO R |
| Volume | Issue | Pages | 1-15 | ||
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| Abstract | In 2015, a picture of a Dress (henceforth the Dress) triggered popular and scientific interest; some reported seeing the Dress in white and gold (W&G) and others in blue and black (B&B). We aimed to describe the phenomenon and investigate the role of contextualization. Few days after the Dress had appeared on the Internet, we projected it to 240 students on two large screens in the classroom. Participants reported seeing the Dress in B&B (48%), W&G (38%), or blue and brown (B&Br; 7%). Amongst numerous socio-demographic variables, we only observed that W&G viewers were most likely to have always seen the Dress as W&G. In the laboratory, we tested how much contextual information is necessary for the phenomenon to occur. Fifty-seven participants selected colours most precisely matching predominant colours of parts or the full Dress. We presented, in this order, small squares (a), vertical strips (b), and the full Dress (c). We found that (1) B&B, B&Br, and W&G viewers had selected colours differing in lightness and chroma levels for contextualized images only (b, c conditions) and hue for fully contextualized condition only (c) and (2) B&B viewers selected colours most closely matching displayed colours of the Dress. Thus, the Dress phenomenon emerges due to inter-individual differences in subjectively perceived lightness, chroma, and hue, at least when all aspects of the picture need to be integrated. Our results support the previous conclusions that contextual information is key to colour perception; it should be important to understand how this actually happens. | ||||
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| Notes | NEUROBIT; no proj | Approved | no | ||
| Call Number | Admin @ si @ JDP2018 | Serial | 3149 | ||
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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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| 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 | ||||
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| Area | Expedition | 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; 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 | |
| Volume | Issue | Pages | 916 - 921 | ||
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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 | ||||
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| Area | Expedition | 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 | ||
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| Author | Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce | ||||
| Title | The Library Living Lab Barcelona: A participative approach to technology as an enabling factor for innovation in cultural spaces | Type | Journal | ||
| Year | 2018 | Publication | Technology Innovation Management Review | Abbreviated Journal | |
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| Notes | DAG; MV; 600.097; 600.121; 600.129;SIAI | Approved | no | ||
| Call Number | Admin @ si @ VKV2018a | Serial | 3153 | ||
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| Author | Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce | ||||
| Title | Libraries as New Innovation Hubs: The Library Living Lab | Type | Conference Article | ||
| Year | 2018 | Publication | 30th ISPIM Innovation Conference | Abbreviated Journal | |
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| Abstract | Libraries are in deep transformation both in EU and around the world, and they are thriving within a great window of opportunity for innovation. In this paper, we show how the Library Living Lab in Barcelona participated of this changing scenario and contributed to create the Bibliolab program, where more than 200 public libraries give voice to their users in a global user-centric innovation initiative, using technology as enabling factor. The Library Living Lab is a real 4-helix implementation where Universities, Research Centers, Public Administration, Companies and the Neighbors are joint together to explore how technology transforms the cultural experience of people. This case is an example of scalability and provides reference tools for policy making, sustainability, user engage methodologies and governance. We provide specific examples of new prototypes and services that help to understand how to redefine the role of the Library as a real hub for social innovation. | ||||
| Address | Stockholm; May 2018 | ||||
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| Area | Expedition | Conference | ISPIM | ||
| Notes | DAG; MV; 600.097; 600.121; 600.129;SIAI | Approved | no | ||
| Call Number | Admin @ si @ VKV2018b | Serial | 3154 | ||
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| Author | Abel Gonzalez-Garcia; Joost Van de Weijer; Yoshua Bengio | ||||
| Title | Image-to-image translation for cross-domain disentanglement | Type | Conference Article | ||
| Year | 2018 | Publication | 32nd Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Address | Montreal; Canada; December 2018 | ||||
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| Area | Expedition | Conference | NIPS | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ GWB2018 | Serial | 3155 | ||
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| Author | Marc Masana; Idoia Ruiz; Joan Serrat; Joost Van de Weijer; Antonio Lopez | ||||
| Title | Metric Learning for Novelty and Anomaly Detection | Type | Conference Article | ||
| Year | 2018 | Publication | 29th British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We divide out-of-distribution detection between novelty detection ---images of classes which are not in the training set but are related to those---, and anomaly detection ---images with classes which are unrelated to the training set. By related we mean they contain the same type of objects, like digits in MNIST and SVHN. Most existing work has focused on anomaly detection, and has addressed this problem considering networks trained with the cross-entropy loss. Differently from them, we propose to use metric learning which does not have the drawback of the softmax layer (inherent to cross-entropy methods), which forces the network to divide its prediction power over the learned classes. We perform extensive experiments and evaluate both novelty and anomaly detection, even in a relevant application such as traffic sign recognition, obtaining comparable or better results than previous works. | ||||
| Address | Newcastle; uk; September 2018 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | LAMP; ADAS; 601.305; 600.124; 600.106; 602.200; 600.120; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ MRS2018 | Serial | 3156 | ||
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| Author | Xialei Liu; Marc Masana; Luis Herranz; Joost Van de Weijer; Antonio Lopez; Andrew Bagdanov | ||||
| Title | Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting | Type | Conference Article | ||
| Year | 2018 | Publication | 24th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2262-2268 | ||
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| Abstract | In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of
a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting. |
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| Area | Expedition | Conference | ICPR | ||
| Notes | LAMP; ADAS; 601.305; 601.109; 600.124; 600.106; 602.200; 600.120; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ LMH2018 | Serial | 3160 | ||
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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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| 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 | Alicia Fornes; Bart Lamiroy | ||||
| Title | Graphics Recognition, Current Trends and Evolutions | Type | Book Whole | ||
| Year | 2018 | Publication | Graphics Recognition, Current Trends and Evolutions | Abbreviated Journal | |
| Volume | 11009 | Issue | Pages | ||
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| Abstract | This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017.
The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps. |
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| Publisher | Springer International Publishing | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | 978-3-030-02283-9 | Medium | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ FoL2018 | Serial | 3171 | ||
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| Author | Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas | ||||
| Title | Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods | Type | Conference Article | ||
| Year | 2018 | Publication | 15th European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | 11134 | Issue | Pages | 530-544 | |
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| Abstract | Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barcelona consisting on images-captions pairs and, using the text as a supervisory signal, we learn relations between images, words and neighborhoods. Our goal is to learn which visual elements appear in photos when people is posting about each neighborhood. We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level. The presented pipeline allows analyzing the differences between the images that tourists and locals associate to the different neighborhoods. The proposed method, which can be extended to other cities or subjects, proves that Instagram data can be used to train multi-modal (image and text) machine learning models that are useful to analyze publications about a city at a neighborhood level. We publish the collected dataset, InstaBarcelona and the code used in the analysis. | ||||
| Address | Munich; Alemanya; September 2018 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | DAG; 600.129; 601.338; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GGG2018b | Serial | 3176 | ||
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| Author | Y. Patel; Lluis Gomez; Raul Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar | ||||
| Title | TextTopicNet-Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community.
In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN. |
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| Notes | DAG; 600.084; 601.338; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ PGG2018 | Serial | 3177 | ||
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| Author | Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas | ||||
| Title | Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings | Type | Conference Article | ||
| Year | 2018 | Publication | International Workshop on Reproducible Research in Pattern Recognition | Abbreviated Journal | |
| Volume | 11455 | Issue | Pages | 71-82 | |
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| Abstract | Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ NDC2018 | Serial | 3178 | ||
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| Author | Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov | ||||
| Title | Word Spotting in Scene Images based on Character Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 1872-1874 | ||
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| Abstract | In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images. | ||||
| Address | Salt Lake City; USA; June 2018 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | DAG; 600.129; 600.121 | Approved | no | ||
| Call Number | BKB2018a | Serial | 3179 | ||
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