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Author  |
Lu Yu |
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Title |
Semantic Representation: From Color to Deep Embeddings |
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2019 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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One of the fundamental problems of computer vision is to represent images with compact semantically relevant embeddings. These embeddings could then be used in a wide variety of applications, such as image retrieval, object detection, and video search. The main objective of this thesis is to study image embeddings from two aspects: color embeddings and deep embeddings.
In the first part of the thesis we start from hand-crafted color embeddings. We propose a method to order the additional color names according to their complementary nature with the basic eleven color names. This allows us to compute color name representations with high discriminative power of arbitrary length. Psychophysical experiments confirm that our proposed method outperforms baseline approaches. Secondly, we learn deep color embeddings from weakly labeled data by adding an attention strategy. The attention branch is able to correctly identify the relevant regions for each class. The advantage of our approach is that it can learn color names for specific domains for which no pixel-wise labels exists.
In the second part of the thesis, we focus on deep embeddings. Firstly, we address the problem of compressing large embedding networks into small networks, while maintaining similar performance. We propose to distillate the metrics from a teacher network to a student network. Two new losses are introduced to model the communication of a deep teacher network to a small student network: one based on an absolute teacher, where the student aims to produce the same embeddings as the teacher, and one based on a relative teacher, where the distances between pairs of data points is communicated from the teacher to the student. In addition, various aspects of distillation have been investigated for embeddings, including hint and attention layers, semi-supervised learning and cross quality distillation. Finally, another aspect of deep metric learning, namely lifelong learning, is studied. We observed some drift occurs during training of new tasks for metric learning. A method to estimate the semantic drift based on the drift which is experienced by data of the current task during its training is introduced. Having this estimation, previous tasks can be compensated for this drift, thereby improving their performance. Furthermore, we show that embedding networks suffer significantly less from catastrophic forgetting compared to classification networks when learning new tasks. |
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November 2019 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Joost Van de Weijer;Yongmei Cheng |
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978-84-121011-3-3 |
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LAMP; 600.120 |
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Admin @ si @ Yu2019 |
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3394 |
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Author |
Lu Yu; Bartlomiej Twardowski; Xialei Liu; Luis Herranz; Kai Wang; Yongmai Cheng; Shangling Jui; Joost Van de Weijer |
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Title |
Semantic Drift Compensation for Class-Incremental Learning of Embeddings |
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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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Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes. In this setting, networks suffer from catastrophic forgetting which refers to the drastic drop in performance on previous tasks. The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes. Embedding networks have the advantage that new classes can be naturally included into the network without adding new weights. Therefore, we study incremental learning for embedding networks. In addition, we propose a new method to estimate the drift, called semantic drift, of features and compensate for it without the need of any exemplars. We approximate the drift of previous tasks based on the drift that is experienced by current task data. We perform experiments on fine-grained datasets, CIFAR100 and ImageNet-Subset. We demonstrate that embedding networks suffer significantly less from catastrophic forgetting. We outperform existing methods which do not require exemplars and obtain competitive results compared to methods which store exemplars. Furthermore, we show that our proposed SDC when combined with existing methods to prevent forgetting consistently improves results. |
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Virtual CVPR |
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LAMP; 600.141; 601.309; 602.200; 600.120 |
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Admin @ si @ YTL2020 |
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3422 |
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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 |
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Journal Article |
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Year |
2018 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVAP |
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Volume |
29 |
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2 |
Pages |
361-373 |
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Keywords |
Color name; Discriminative descriptors; Image classification; Re-identification; Tracking |
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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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Admin @ si @ YYW2018 |
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3087 |
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Author |
Lu Yu; Vacit Oguz Yazici; Xialei Liu; Joost Van de Weijer; Yongmei Cheng; Arnau Ramisa |
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Title |
Learning Metrics from Teachers: Compact Networks for Image Embedding |
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Conference Article |
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Year |
2019 |
Publication |
32nd IEEE Conference on Computer Vision and Pattern Recognition |
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2907-2916 |
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Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to use network distillation to efficiently compute image embeddings with small networks. Network distillation has been successfully applied to improve image classification, but has hardly been explored for metric learning. To do so, we propose two new loss functions that model the
communication of a deep teacher network to a small student network. We evaluate our system in several datasets, including CUB-200-2011, Cars-196, Stanford Online Products and show that embeddings computed using small student networks perform significantly better than those computed using standard networks of similar size. Results on a very compact network (MobileNet-0.25), which can be
used on mobile devices, show that the proposed method can greatly improve Recall@1 results from 27.5% to 44.6%. Furthermore, we investigate various aspects of distillation for embeddings, including hint and attention layers, semisupervised learning and cross quality distillation. |
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Long beach; California; june 2019 |
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LAMP; 600.109; 600.120 |
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Call Number |
Admin @ si @ YYL2019 |
Serial |
3281 |
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Author |
Lu Yu; Xialei Liu; Joost Van de Weijer |
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Title |
Self-Training for Class-Incremental Semantic Segmentation |
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Journal Article |
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Year |
2022 |
Publication |
IEEE Transactions on Neural Networks and Learning Systems |
Abbreviated Journal |
TNNLS |
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Class-incremental learning; Self-training; Semantic segmentation. |
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In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art methods. |
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LAMP; 600.147; 611.008; |
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Admin @ si @ YLW2022 |
Serial |
3745 |
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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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Year |
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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LAMP; 600.109; 602.200; 600.120 |
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Admin @ si @ YCW2018 |
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3243 |
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Author |
Lubomir Latchev; Maya Dimitrova; David Rotger |
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Title |
A Classifier of Technical Diagnostic States of Electrocardiograph |
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Miscellaneous |
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2006 |
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International Conference on Computer Systems and Technologies (CompSysTech´06), 15.1–15.6 |
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University of Veliko Tarnovo (Bulgaria) |
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Admin @ si @ LDR2006 |
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774 |
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Author |
Luca Ginanni Corradini; Simone Balocco; Luciano Maresca; Silvio Vitale; Matteo Stefanini |
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Title |
Anatomical Modifications After Stent Implantation: A Comparative Analysis Between CGuard, Wallstent, and Roadsaver Carotid Stents |
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2023 |
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Journal of Endovascular Therapy |
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30 |
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1 |
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18-24 |
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Ginanni Corradini L, Balocco S, Maresca L, Vitale S, Stefanini M. |
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Abstract
Purpose:
Carotid revascularization can be associated with modifications of the vascular geometry, which may lead to complications. The changes on the vessel angulation before and after a carotid WallStent (WS) implantation are compared against 2 new dual-layer devices, CGuard (CG) and RoadSaver (RS).
Materials and Methods:
The study prospectively recruited 217 consecutive patients (112 GC, 73 WS, and 32 RS, respectively). Angiography projections were explored and the one having a higher arterial angle was selected as a basal view. After stent implantation, a stent control angiography was performed selecting the projection having the maximal angle. The same procedure is followed in all the 3 stent types to guarantee comparable conditions. The angulation changes on the stented segments were quantified from both angiographies. The statistical analysis quantitatively compared the pre-and post-angles for the 3 stent types. The results are qualitatively illustrated using boxplots. Finally, the relation between pre- and post-angles measurements is analyzed using linear regression.
Results:
For CG, no statistical difference in the axial vessel geometry between the basal and postprocedural angles was found. For WS and RS, statistical difference was found between pre- and post-angles. The regression analysis shows that CG induces lower changes from the original curvature with respect to WS and RS.
Conclusion:
Based on our results, CG determines minor changes over the basal morphology than WS and RS stents. Hence, CG respects better the native vessel anatomy than the other stents.
Level of Evidence: Level 4, Case Series. |
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Admin @ si @ GBM2023 |
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4006 |
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Author |
Luis Herranz; Shuqiang Jiang; Ruihan Xu |
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Title |
Modeling Restaurant Context for Food Recognition |
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Journal Article |
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2017 |
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IEEE Transactions on Multimedia |
Abbreviated Journal |
TMM |
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19 |
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2 |
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430 - 440 |
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Food photos are widely used in food logs for diet monitoring and in social networks to share social and gastronomic experiences. A large number of these images are taken in restaurants. Dish recognition in general is very challenging, due to different cuisines, cooking styles, and the intrinsic difficulty of modeling food from its visual appearance. However, contextual knowledge can be crucial to improve recognition in such scenario. In particular, geocontext has been widely exploited for outdoor landmark recognition. Similarly, we exploit knowledge about menus and location of restaurants and test images. We first adapt a framework based on discarding unlikely categories located far from the test image. Then, we reformulate the problem using a probabilistic model connecting dishes, restaurants, and locations. We apply that model in three different tasks: dish recognition, restaurant recognition, and location refinement. Experiments on six datasets show that by integrating multiple evidences (visual, location, and external knowledge) our system can boost the performance in all tasks. |
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LAMP; 600.120 |
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Admin @ si @ HJX2017 |
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2965 |
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Luis Herranz; Weiqing Min; Shuqiang Jiang |
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Food recognition and recipe analysis: integrating visual content, context and external knowledge |
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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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Admin @ si @ HMJ2018 |
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3250 |
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M. Altillawi; S. Li; S.M. Prakhya; Z. Liu; Joan Serrat |
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Implicit Learning of Scene Geometry From Poses for Global Localization |
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2024 |
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IEEE Robotics and Automation Letters |
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ROBOTAUTOMLET |
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9 |
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2 |
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955-962 |
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Localization; Localization and mapping; Deep learning for visual perception; Visual learning |
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Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this letter, we propose to utilize these minimal available labels (i.e., poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations ( X, Y, Z coordinates ) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods' pose accuracy on all datasets. |
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2377-3766 |
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ADAS |
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3857 |
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M. Bressan |
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Un analisis de viabilidad para la confeccion semisupervisada de un mapa de usos del suelo de Catalunya |
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2001 |
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CVC Technical Report #58 |
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CVC (UAB) |
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Admin @ si @ Bre2001 |
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182 |
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Author |
M. Bressan |
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Title |
Independent modes of variation in Point Distribution models |
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Report |
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2000 |
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CVC Technical Report #48 |
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no |
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Admin @ si @ Bre2000 |
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349 |
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Author |
M. Bressan; David Guillamet; Jordi Vitria |
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Title |
Using a local ICA Representation of High Dimensional Data for Object Recognition and Classification. |
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Miscellaneous |
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2001 |
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Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). |
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Hawaii |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ BGV2001 |
Serial |
75 |
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Author |
M. Bressan; David Guillamet; Jordi Vitria |
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Title |
Using an ICA representation of local color histograms for object recognition. |
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Miscellaneous |
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2000 |
Publication |
Butlleti de l´ ACIA, 22:300–307. |
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OR;MV |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ BGV2000 |
Serial |
338 |
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| Permanent link to this record |
