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Author  |
Vacit Oguz Yazici; Joost Van de Weijer; Arnau Ramisa |
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Title |
Color Naming for Multi-Color Fashion Items |
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Conference Article |
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2018 |
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6th World Conference on Information Systems and Technologies |
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747 |
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64-73 |
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Deep learning; Color; Multi-label |
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There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results. |
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Naples; March 2018 |
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WORLDCIST |
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LAMP; 600.109; 601.309; 600.120 |
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Admin @ si @ YWR2018 |
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3161 |
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Author |
Vacit Oguz Yazici; Abel Gonzalez-Garcia; Arnau Ramisa; Bartlomiej Twardowski; Joost Van de Weijer |
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Title |
Orderless Recurrent Models for Multi-label Classification |
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Conference Article |
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2020 |
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33rd IEEE Conference on Computer Vision and Pattern Recognition |
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Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task. Current approaches sort labels according to their frequency, typically ordering them in either rare-first or frequent-first. These imposed orderings do not take into account that the natural order to generate the labels can change for each image, e.g.\ first the dominant object before summing up the smaller objects in the image. Therefore, in this paper, we propose ways to dynamically order the ground truth labels with the predicted label sequence. This allows for the faster training of more optimal LSTM models for multi-label classification. Analysis evidences that our method does not suffer from duplicate generation, something which is common for other models. Furthermore, it outperforms other CNN-RNN models, and we show that a standard architecture of an image encoder and language decoder trained with our proposed loss obtains the state-of-the-art results on the challenging MS-COCO, WIDER Attribute and PA-100K and competitive results on NUS-WIDE. |
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CVPR |
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LAMP; 600.109; 601.309; 600.141; 600.120 |
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Admin @ si @ YGR2020 |
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3408 |
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Author |
Vacit Oguz Yazici |
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Title |
Towards Smart Fashion: Visual Recognition of Products and Attributes |
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2022 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Artificial intelligence is innovating the fashion industry by proposing new applications and solutions to the problems encountered by researchers and engineers working in the industry. In this thesis, we address three of these problems. In the first part of the thesis, we tackle the problem of multi-label image classification which is very related to fashion attribute recognition. In the second part of the thesis, we address two problems that are specific to fashion. Firstly, we address the problem of main product detection which is the task of associating correct image parts (e.g. bounding boxes) with the fashion product being sold. Secondly, we address the problem of color naming for multicolored fashion items. The task of multi-label image classification consists in assigning various concepts such as objects or attributes to images. Usually, there are dependencies that can be learned between the concepts to capture label correlations (chair and table classes are more likely to co-exist than chair and giraffe).
If we treat the multi-label image classification problem as an orderless set prediction problem, we can exploit recurrent neural networks (RNN) to capture label correlations. However, RNNs are trained to predict ordered sequences of tokens, so if the order of the predicted sequence is different than the order of the ground truth sequence, there will be penalization although the predictions are correct. Therefore, in the first part of the thesis, we propose an orderless loss function which will order the labels in the ground truth sequence dynamically in a way that the minimum loss is achieved. This results in a significant improvement of RNN models on multi-label image classification over the previous methods.
However, RNNs suffer from long term dependencies when the cardinality of set grows bigger. The decoding process might stop early if the current hidden state cannot find any object and outputs the termination token. This would cause the remaining classes not to be predicted and lower recall metric. Transformers can be used to avoid the long term dependency problem exploiting their selfattention modules that process sequential data simultaneously. Consequently, we propose a novel transformer model for multi-label image classification which surpasses the state-of-the-art results by a large margin.
In the second part of thesis, we focus on two fashion-specific problems. Main product detection is the task of associating image parts with the fashion product that is being sold, generally using associated textual metadata (product title or description). Normally, in fashion e-commerces, products are represented by multiple images where a person wears the product along with other fashion items. If all the fashion items in the images are marked with bounding boxes, we can use the textual metadata to decide which item is the main product. The initial work treated each of these images independently, discarding the fact that they all belong to the same product. In this thesis, we represent the bounding boxes from all the images as nodes in a fully connected graph. This allows the algorithm to learn relations between the nodes during training and take the entire context into account for the final decision. Our algorithm results in a significant improvement of the state-ofthe-art.
Moreover, we address the problem of color naming for multicolored fashion items, which is a challenging task due to the external factors such as illumination changes or objects that act as clutter. In the context of multi-label classification, the vaguely defined lines between the classes in the color space cause ambiguity. For example, a shade of blue which is very close to green might cause the model to incorrectly predict the color blue and green at the same time. Based on this, models trained for color naming are expected to recognize the colors and their quantities in both single colored and multicolored fashion items. Therefore, in this thesis, we propose a novel architecture with an additional head that explicitly estimates the number of colors in fashion items. This removes the ambiguity problem and results in better color naming performance. |
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January 2022 |
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Ph.D. thesis |
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IMPRIMA |
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Joost Van de Weijer;Arnau Ramisa |
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978-84-122714-6-1 |
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LAMP |
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Admin @ si @ Ogu2022 |
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3631 |
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Author |
V.C.Kieu; Alicia Fornes; M. Visani; N.Journet ; Anjan Dutta |
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Title |
The ICDAR/GREC 2013 Music Scores Competition on Staff Removal |
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Conference Article |
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2013 |
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10th IAPR International Workshop on Graphics Recognition |
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Competition; Music scores; Staff Removal |
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The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we propose a staff removal competition where we simulate old music scores. Thus, we have created a new set of images, which contain noise and 3D distortions. This paper describes the distortion methods, metrics, the participant’s methods and the obtained results. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.045; 600.061 |
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Admin @ si @ KFV2013 |
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2337 |
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Author |
V. Valev; Petia Radeva |
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Title |
Structural Pattern Recognition by Non-Reducible Descriptors |
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1994 |
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Proc. International Workshop on Syntactic and Structural Pattern Recognition. |
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Nahariya, Israel |
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MILAB |
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BCNPCL @ bcnpcl @ VaR1994 |
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107 |
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Author |
V. Valev; Petia Radeva |
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Title |
ECG Recognition by Non-Reducible Descriptors. |
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1995 |
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Portuguese Conference on Pattern Recognition. |
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Lisbon, Portugal |
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BCNPCL @ bcnpcl @ VaR1995a |
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139 |
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V. Valev; Petia Radeva |
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Title |
Constructing Quantitative Non-Reducible Descriptors. |
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Miscellaneous |
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1995 |
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9th Scandinavian Conference on Artificial Intelligence |
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Sweden |
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BCNPCL @ bcnpcl @ VaR1995b |
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140 |
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V. Valev; Petia Radeva |
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On the Determining of Non-Reducible Descriptors for Multidimensional Pattern Recognition Problems. |
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1993 |
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Pattern Recognition and image Analysis, Vol. 3, No. 3. |
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BCNPCL @ bcnpcl @ VaR1993 |
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175 |
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V. Valev; Petia Radeva |
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Determining structural descriptions by boolean formulas advances in structural and syntactic Pattern Recognition. |
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1992 |
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Proceeding of the International Workshop on Structural and Syntactic Pattern Recognition. Ed. Bunke, World Scientific Pub. |
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BCNPCL @ bcnpcl @ VaR1992a |
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251 |
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V. Valev; Petia Radeva |
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A Method of Solving Pattern or image Recognition Problems by Learning Boolean Formulas. |
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1992 |
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Proc. of 11th IAPR International Conference on Pattern Recognition, Hague, Netherlands, IEEE Computer Society Press, vol. II, pp. 359–362. |
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MILAB |
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BCNPCL @ bcnpcl @ VaR1992b |
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253 |
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V. Valev; Petia Radeva |
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Title |
Determining Structural Description by Boolean Formulas. |
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1992 |
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Advances in Structural and Syntactic Pattern Recognition |
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5 |
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131–140 |
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Pattern recognition is an active area of research with many applications, some of which have reached commercial maturity. Structural and syntactic methods are very powerful. They are based on symbolic data structures together with matching, parsing, and reasoning procedures that are able to infer interpretations of complex input patterns.
This book gives an overview of the latest developments and achievements in the field. |
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World Scientific |
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H. Bunke |
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Machine Perception and Artificial Intelligence: |
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978-981-279-791-9 |
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MILAB |
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BCNPCL @ bcnpcl @ VaR1992c |
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254 |
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Author |
V. Valev; B. Sankur; Petia Radeva |
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Generalized Non-Reducible Descriptors. |
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Miscellaneous |
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1997 |
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Technical Report. |
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BCNPCL @ bcnpcl @ VSR1997 |
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65 |
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V. Valev; B. Sankur; Petia Radeva |
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Generalized Non Reducible Descriptors. |
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2000 |
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15 th International Conference on Pattern Recognition, 2: 394–397. |
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Barcelona. |
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BCNPCL @ bcnpcl @ VSR2000 |
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230 |
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Author |
V. Poulain d'Andecy; Emmanuel Hartmann; Marçal Rusiñol |
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Title |
Field Extraction by hybrid incremental and a-priori structural templates |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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251 - 256 |
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Layout Analysis; information extraction; incremental learning |
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In this paper, we present an incremental framework for extracting information fields from administrative documents. First, we demonstrate some limits of the existing state-of-the-art methods such as the delay of the system efficiency. This is a concern in industrial context when we have only few samples of each document class. Based on this analysis, we propose a hybrid system combining incremental learning by means of itf-df statistics and a-priori generic
models. We report in the experimental section our results obtained with a dataset of real invoices. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.129; 600.121 |
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Admin @ si @ PHR2018 |
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3106 |
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Author |
V. Kober; Mikhail Mozerov; Josue Albarez; I.A. Ovseyevich |
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Title |
Algorithms for Impulse Noise Renoval from Corrupted Color Images |
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Journal |
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2007 |
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ISE |
Approved |
no |
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Call Number |
ISE @ ise @ KMA2007 |
Serial |
811 |
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