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Author |
Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
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Title |
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network |
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Conference Article |
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Year |
2018 |
Publication |
21st International Conference of the Catalan Association for Artificial Intelligence |
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365-372 |
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Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. |
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Roses; catalonia; October 2018 |
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CCIA |
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MILAB; no menciona |
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no |
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Call Number |
Admin @ si @ SJR2018 |
Serial |
3113 |
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Author |
Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Hatem A. Rashwan; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
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Title |
MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams |
Type |
Conference Article |
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Year |
2018 |
Publication |
European Conference on Computer Vision workshops |
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423-433 |
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First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called “EgoFoodPlaces”. Experimental results shows promising results of food places classification recognition in egocentric photo-streams. |
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ECCVW |
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MILAB; no menciona |
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no |
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Admin @ si @ SRR2018b |
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3185 |
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Author |
Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas |
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Title |
Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets |
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Conference Article |
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Year |
2018 |
Publication |
International Workshop on Artificial Intelligence and Pattern Recognition |
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Volume |
11047 |
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34-42 |
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Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis |
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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. |
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Cuba; September 2018 |
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IWAIPR |
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MILAB; no menciona |
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no |
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Admin @ si @ BGÑ2018 |
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3237 |
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Author |
Lu Yu; Lichao Zhang; Joost Van de Weijer; Fahad Shahbaz Khan; Yongmei Cheng; C. Alejandro Parraga |
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Title |
Beyond Eleven Color Names for Image Understanding |
Type |
Journal Article |
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Year |
2018 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVAP |
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Volume |
29 |
Issue |
2 |
Pages |
361-373 |
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Keywords |
Color name; Discriminative descriptors; Image classification; Re-identification; Tracking |
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Abstract |
Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the task of visual tracking, person re-identification and image classification. |
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LAMP; NEUROBIT; 600.068; 600.109; 600.120 |
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no |
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Call Number |
Admin @ si @ YYW2018 |
Serial |
3087 |
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Author |
Xialei Liu; Marc Masana; Luis Herranz; Joost Van de Weijer; Antonio Lopez; Andrew Bagdanov |
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Title |
Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting |
Type |
Conference Article |
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Year |
2018 |
Publication |
24th International Conference on Pattern Recognition |
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Pages |
2262-2268 |
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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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ICPR |
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Notes |
LAMP; ADAS; 601.305; 601.109; 600.124; 600.106; 602.200; 600.120; 600.118 |
Approved |
no |
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Call Number |
Admin @ si @ LMH2018 |
Serial |
3160 |
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Author |
Marc Masana; Idoia Ruiz; Joan Serrat; Joost Van de Weijer; Antonio Lopez |
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Title |
Metric Learning for Novelty and Anomaly Detection |
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Conference Article |
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Year |
2018 |
Publication |
29th British Machine Vision Conference |
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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. |
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Newcastle; uk; September 2018 |
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BMVC |
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Notes |
LAMP; ADAS; 601.305; 600.124; 600.106; 602.200; 600.120; 600.118 |
Approved |
no |
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Call Number |
Admin @ si @ MRS2018 |
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3156 |
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Author |
Aymen Azaza |
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Title |
Context, Motion and Semantic Information for Computational Saliency |
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2018 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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The main objective of this thesis is to highlight the salient object in an image or in a video sequence. We address three important—but in our opinion
insufficiently investigated—aspects of saliency detection. Firstly, we start
by extending previous research on saliency which explicitly models the information provided from the context. Then, we show the importance of
explicit context modelling for saliency estimation. Several important works
in saliency are based on the usage of object proposals. However, these methods
focus on the saliency of the object proposal itself and ignore the context.
To introduce context in such saliency approaches, we couple every object
proposal with its direct context. This allows us to evaluate the importance
of the immediate surround (context) for its saliency. We propose several
saliency features which are computed from the context proposals including
features based on omni-directional and horizontal context continuity. Secondly,
we investigate the usage of top-downmethods (high-level semantic
information) for the task of saliency prediction since most computational
methods are bottom-up or only include few semantic classes. We propose
to consider a wider group of object classes. These objects represent important
semantic information which we will exploit in our saliency prediction
approach. Thirdly, we develop a method to detect video saliency by computing
saliency from supervoxels and optical flow. In addition, we apply the
context features developed in this thesis for video saliency detection. The
method combines shape and motion features with our proposed context
features. To summarize, we prove that extending object proposals with their
direct context improves the task of saliency detection in both image and
video data. Also the importance of the semantic information in saliency
estimation is evaluated. Finally, we propose a newmotion feature to detect
saliency in video data. The three proposed novelties are evaluated on standard
saliency benchmark datasets and are shown to improve with respect to
state-of-the-art. |
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October 2018 |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Joost Van de Weijer;Ali Douik |
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978-84-945373-9-4 |
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LAMP; 600.120 |
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no |
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Admin @ si @ Aza2018 |
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3218 |
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Author |
Adrian Galdran; Aitor Alvarez-Gila; Alessandro Bria; Javier Vazquez; Marcelo Bertalmio |
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Title |
On the Duality Between Retinex and Image Dehazing |
Type |
Conference Article |
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Year |
2018 |
Publication |
31st IEEE Conference on Computer Vision and Pattern Recognition |
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8212–8221 |
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Image color analysis; Task analysis; Atmospheric modeling; Computer vision; Computational modeling; Lighting |
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Abstract |
Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem. |
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Salt Lake City; USA; June 2018 |
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CVPR |
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LAMP; 600.120 |
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no |
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Call Number |
Admin @ si @ GAB2018 |
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3146 |
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Author |
Abel Gonzalez-Garcia; Joost Van de Weijer; Yoshua Bengio |
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Title |
Image-to-image translation for cross-domain disentanglement |
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Conference Article |
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Year |
2018 |
Publication |
32nd Annual Conference on Neural Information Processing Systems |
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Montreal; Canada; December 2018 |
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NIPS |
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LAMP; 600.120 |
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no |
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Admin @ si @ GWB2018 |
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3155 |
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Author |
Hugo Prol; Vincent Dumoulin; Luis Herranz |
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Title |
Cross-Modulation Networks for Few-Shot Learning |
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Miscellaneous |
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2018 |
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Arxiv |
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A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this observation, we hypothesize that there may be benefits to combining the information at various levels of abstraction along the pipeline. We present an architecture called Cross-Modulation Networks which allows support and query examples to interact throughout the feature extraction process via a feature-wise modulation mechanism. We adapt the Matching Networks architecture to take advantage of these interactions and show encouraging initial results on miniImageNet in the 5-way, 1-shot setting, where we close the gap with state-of-the-art. |
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LAMP; 600.120 |
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no |
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Admin @ si @ PDH2018 |
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3248 |
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Author |
Luis Herranz; Weiqing Min; Shuqiang Jiang |
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Title |
Food recognition and recipe analysis: integrating visual content, context and external knowledge |
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Miscellaneous |
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2018 |
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Arxiv |
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The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information. We review how visual content, context and external knowledge can be integrated effectively into food-oriented applications, with special focus on recipe analysis and retrieval, food recommendation and restaurant context as emerging directions. |
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LAMP; 600.120 |
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no |
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Admin @ si @ HMJ2018 |
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3250 |
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Author |
Lu Yu; Yongmei Cheng; Joost Van de Weijer |
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Title |
Weakly Supervised Domain-Specific Color Naming Based on Attention |
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Conference Article |
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2018 |
Publication |
24th International Conference on Pattern Recognition |
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3019 - 3024 |
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The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains. |
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Beijing; August 2018 |
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ICPR |
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LAMP; 600.109; 602.200; 600.120 |
Approved |
no |
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Call Number |
Admin @ si @ YCW2018 |
Serial |
3243 |
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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 |
Type |
Conference Article |
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Year |
2018 |
Publication |
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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Abstract |
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 |
Marco Buzzelli; Joost Van de Weijer; Raimondo Schettini |
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Learning Illuminant Estimation from Object Recognition |
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2018 |
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25th International Conference on Image Processing |
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3234 - 3238 |
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Illuminant estimation; computational color constancy; semi-supervised learning; deep learning; convolutional neural networks |
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In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep
learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset evaluation
setup, and to present competitive results in a comparison with parametric solutions. |
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Athens; Greece; October 2018 |
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ICIP |
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LAMP; 600.109; 600.120 |
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Admin @ si @ BWS2018 |
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3157 |
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Author |
Abel Gonzalez-Garcia; Davide Modolo; Vittorio Ferrari |
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Objects as context for detecting their semantic parts |
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2018 |
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31st IEEE Conference on Computer Vision and Pattern Recognition |
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6907 - 6916 |
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Proposals; Semantics; Wheels; Automobiles; Context modeling; Task analysis; Object detection |
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We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to
detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare
to other part detection methods on both PASCAL-Part and CUB200-2011 datasets. |
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Salt Lake City; USA; June 2018 |
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LAMP; 600.109; 600.120 |
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Admin @ si @ GMF2018 |
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3229 |
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