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Author | Gabriel Villalonga; Antonio Lopez | ||||
Title | Co-Training for On-Board Deep Object Detection | Type | Journal Article | ||
Year | 2020 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
Volume | Issue | Pages | 194441 - 194456 | ||
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Abstract | Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ ViL2020 | Serial | 3488 | ||
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Author | Wenjuan Gong; W.Zhang; Jordi Gonzalez; Y.Ren; Z.Li | ||||
Title | Enhanced Asymmetric Bilinear Model for Face Recognition | Type | Journal Article | ||
Year | 2015 | Publication | International Journal of Distributed Sensor Networks | Abbreviated Journal | IJDSN |
Volume | Issue | Pages | Article ID 218514 | ||
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Abstract | Bilinear models have been successfully applied to separate two factors, for example, pose variances and different identities in face recognition problems. Asymmetric model is a type of bilinear model which models a system in the most concise way. But seldom there are works exploring the applications of asymmetric bilinear model on face recognition problem with illumination changes. In this work, we propose enhanced asymmetric model for illumination-robust face recognition. Instead of initializing the factor probabilities randomly, we initialize them with nearest neighbor method and optimize them for the test data. Above that, we update the factor model to be identified. We validate the proposed method on a designed data sample and extended Yale B dataset. The experiment results show that the enhanced asymmetric models give promising results and good recognition accuracies. | ||||
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Notes | ISE; 600.063; 600.078 | Approved | no | ||
Call Number | Admin @ si @ GZG2015 | Serial | 2592 | ||
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Author | Penny Tarling; Mauricio Cantor; Albert Clapes; Sergio Escalera | ||||
Title | Deep learning with self-supervision and uncertainty regularization to count fish in underwater images | Type | Journal Article | ||
Year | 2022 | Publication | PloS One | Abbreviated Journal | Plos |
Volume | 17 | Issue | 5 | Pages | e0267759 |
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Abstract | Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data. | ||||
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Publisher | Public Library of Science | Place of Publication | Editor | ||
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Notes | HuPBA | Approved | no | ||
Call Number | Admin @ si @ TCC2022 | Serial | 3743 | ||
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Author | Josep Llados; Marçal Rusiñol; Alicia Fornes; David Fernandez; Anjan Dutta | ||||
Title | On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents | Type | Journal Article | ||
Year | 2012 | Publication | International Journal of Pattern Recognition and Artificial Intelligence | Abbreviated Journal | IJPRAI |
Volume | 26 | Issue | 5 | Pages | 1263002-126027 |
Keywords | Handwriting recognition; word spotting; historical documents; feature representation; shape descriptors Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025 | ||||
Abstract | 0,624 JCR
Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images. |
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Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ LRF2012 | Serial | 2128 | ||
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Author | Santiago Segui; Laura Igual; Jordi Vitria | ||||
Title | Bagged One Class Classifiers in the Presence of Outliers | Type | Journal Article | ||
Year | 2013 | Publication | International Journal of Pattern Recognition and Artificial Intelligence | Abbreviated Journal | IJPRAI |
Volume | 27 | Issue | 5 | Pages | 1350014-1350035 |
Keywords | One-class Classifier; Ensemble Methods; Bagging and Outliers | ||||
Abstract | The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers.
However, their application to one-class classification has been rather limited. In this paper, we present a new ensemble method based on a non-parametric weighted bagging strategy for one-class classification, to improve accuracy in the presence of outliers. While the standard bagging strategy assumes a uniform data distribution, the method we propose here estimates a probability density based on a forest structure of the data. This assumption allows the estimation of data distribution from the computation of simple univariate and bivariate kernel densities. Experiments using original and noisy versions of 20 different datasets show that bagging ensemble methods applied to different one-class classifiers outperform base one-class classification methods. Moreover, we show that, in noisy versions of the datasets, the non-parametric weighted bagging strategy we propose outperforms the classical bagging strategy in a statistically significant way. |
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Notes | OR; 600.046;MV | Approved | no | ||
Call Number | Admin @ si @ SIV2013 | Serial | 2256 | ||
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Author | Jaume Gibert; Ernest Valveny; Horst Bunke | ||||
Title | Embedding of Graphs with Discrete Attributes Via Label Frequencies | Type | Journal Article | ||
Year | 2013 | Publication | International Journal of Pattern Recognition and Artificial Intelligence | Abbreviated Journal | IJPRAI |
Volume | 27 | Issue | 3 | Pages | 1360002-1360029 |
Keywords | Discrete attributed graphs; graph embedding; graph classification | ||||
Abstract | Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient. | ||||
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Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ GVB2013 | Serial | 2305 | ||
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Author | Onur Ferhat; Fernando Vilariño | ||||
Title | Low Cost Eye Tracking: The Current Panorama | Type | Journal Article | ||
Year | 2016 | Publication | Computational Intelligence and Neuroscience | Abbreviated Journal | CIN |
Volume | Issue | Pages | Article ID 8680541 | ||
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Abstract | Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools. | ||||
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Notes | MV; 605.103; 600.047; 600.097;SIAI | Approved | no | ||
Call Number | Admin @ si @ FeV2016 | Serial | 2744 | ||
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Author | Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat | ||||
Title | Monitoring war destruction from space using machine learning | Type | Journal Article | ||
Year | 2021 | Publication | Proceedings of the National Academy of Sciences of the United States of America | Abbreviated Journal | PNAS |
Volume | 118 | Issue | 23 | Pages | e2025400118 |
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Abstract | Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ MGH2021 | Serial | 3584 | ||
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