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Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu
Title New Opportunities for Computer Vision-Based Assistive Technology Systems for the Visually Impaired Type Journal Article
Year 2014 Publication Computer Abbreviated Journal COMP
Volume 47 Issue 4 Pages 52-58
Keywords
Abstract Computing advances and increased smartphone use gives technology system designers greater flexibility in exploiting computer vision to support visually impaired users. Understanding these users' needs will certainly provide insight for the development of improved usability of computing devices.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0018-9162 ISBN Medium
Area Expedition Conference
Notes (down) LAMP; Approved no
Call Number Admin @ si @ TSR2014a Serial 2317
Permanent link to this record
 
 
Author Bogdan Raducanu; Fadi Dornaika
Title Embedding new observations via sparse-coding for non-linear manifold learning Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 1 Pages 480-492
Keywords
Abstract Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the K-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on six public face datasets. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes (down) LAMP; Approved no
Call Number Admin @ si @ RaD2013b Serial 2316
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Author Cesar Isaza; Joaquin Salas; Bogdan Raducanu
Title Rendering ground truth data sets to detect shadows cast by static objects in outdoors Type Journal Article
Year 2014 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 70 Issue 1 Pages 557-571
Keywords Synthetic ground truth data set; Sun position; Shadow detection; Static objects shadow detection
Abstract In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically.
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1380-7501 ISBN Medium
Area Expedition Conference
Notes (down) LAMP; Approved no
Call Number Admin @ si @ ISR2014 Serial 2229
Permanent link to this record
 
 
Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu
Title Robust Head Gestures Recognition for Assistive Technology Type Book Chapter
Year 2014 Publication Pattern Recognition Abbreviated Journal
Volume 8495 Issue Pages 152-161
Keywords
Abstract This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.
Address
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-319-07490-0 Medium
Area Expedition Conference
Notes (down) LAMP; Approved no
Call Number Admin @ si @ TSR2014b Serial 2505
Permanent link to this record
 
 
Author Manuel Graña; Bogdan Raducanu
Title Special Issue on Bioinspired and knowledge based techniques and applications Type Journal Article
Year 2015 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume Issue Pages 1-3
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes (down) LAMP; Approved no
Call Number Admin @ si @ GrR2015 Serial 2598
Permanent link to this record
 
 
Author Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika
Title Facial Expression Recognition based on Multi-view Observations with Application to Social Robotics Type Conference Article
Year 2014 Publication 1st Workshop on Computer Vision for Affective Computing Abbreviated Journal
Volume Issue Pages 1-8
Keywords
Abstract Human-robot interaction is a hot topic nowadays in the social robotics community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this paper, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, view- and texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial
expression.
Address Singapore; November 2014
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ACCV
Notes (down) LAMP; Approved no
Call Number Admin @ si @ RBD2014 Serial 2599
Permanent link to this record
 
 
Author Fadi Dornaika; Bogdan Raducanu; Alireza Bosaghzadeh
Title Facial expression recognition based on multi observations with application to social robotics Type Book Chapter
Year 2015 Publication Emotional and Facial Expressions: Recognition, Developmental Differences and Social Importance Abbreviated Journal
Volume Issue Pages 153-166
Keywords
Abstract Human-robot interaction is a hot topic nowadays in the social robotics
community. One crucial aspect is represented by the affective communication
which comes encoded through the facial expressions. In this chapter, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, viewand texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial
expression.
Address
Corporate Author Thesis
Publisher Nova Science publishers Place of Publication Editor Bruce Flores
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes (down) LAMP; Approved no
Call Number Admin @ si @ DRB2015 Serial 2720
Permanent link to this record
 
 
Author Javad Zolfaghari Bengar
Title Reducing Label Effort with Deep Active Learning Type Book Whole
Year 2021 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Deep convolutional neural networks (CNNs) have achieved superior performance in many visual recognition applications, such as image classification, detection and segmentation. Training deep CNNs requires huge amounts of labeled data, which is expensive and labor intensive to collect. Active learning is a paradigm aimed at reducing the annotation effort by training the model on actively selected
informative and/or representative samples. In this thesis we study several aspects of active learning including video object detection for autonomous driving systems, image classification on balanced and imbalanced datasets and the incorporation of self-supervised learning in active learning. We briefly describe our approach in each of these areas to reduce the labeling effort.
In chapter two we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our criterion is based on the estimated number of errors in terms of false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active
learning for video object detection in road scenes. Finally, we show that our
approach outperforms active learning baselines tested on two outdoor datasets.
In the next chapter we address the well-known problem of over confidence in the neural networks. As an alternative to network confidence, we propose a new informativeness-based active learning method that captures the learning dynamics of neural network with a metric called label-dispersion. This metric is low when the network consistently assigns the same label to the sample during the course of training and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.
In chapter four, we tackle the problem of sampling bias in active learning methods on imbalanced datasets. Active learning is generally studied on balanced datasets where an equal amount of images per class is available. However, real-world datasets suffer from severe imbalanced classes, the so called longtail distribution. We argue that this further complicates the active learning process, since the imbalanced data pool can result in suboptimal classifiers. To address this problem in the context of active learning, we propose a general optimization framework that explicitly takes class-balancing into account. Results on three datasets show that the method is general (it can be combined with most existing active learning algorithms) and can be effectively applied to boost the performance of both informative and representative-based active learning methods. In addition, we show that also on balanced datasets our method generally results in a performance gain.
Another paradigm to reduce the annotation effort is self-training that learns from a large amount of unlabeled data in an unsupervised way and fine-tunes on few labeled samples. Recent advancements in self-training have achieved very impressive results rivaling supervised learning on some datasets. In the last chapter we focus on whether active learning and self supervised learning can benefit from each other.
We study object recognition datasets with several labeling budgets for the evaluations. Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high.
Address December 2021
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Joost Van de Weijer;Bogdan Raducanu
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-122714-9-2 Medium
Area Expedition Conference
Notes (down) LAMP; Approved no
Call Number Admin @ si @ Zol2021 Serial 3609
Permanent link to this record
 
 
Author Javad Zolfaghari Bengar; Joost Van de Weijer; Bartlomiej Twardowski; Bogdan Raducanu
Title Reducing Label Effort: Self- Supervised Meets Active Learning Type Conference Article
Year 2021 Publication International Conference on Computer Vision Workshops Abbreviated Journal
Volume Issue Pages 1631-1639
Keywords
Abstract Active learning is a paradigm aimed at reducing the annotation effort by training the model on actively selected informative and/or representative samples. Another paradigm to reduce the annotation effort is self-training that learns from a large amount of unlabeled data in an unsupervised way and fine-tunes on few labeled samples. Recent developments in self-training have achieved very impressive results rivaling supervised learning on some datasets. The current work focuses on whether the two paradigms can benefit from each other. We studied object recognition datasets including CIFAR10, CIFAR100 and Tiny ImageNet with several labeling budgets for the evaluations. Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high. The performance gap between active learning trained either with self-training or from scratch diminishes as we approach to the point where almost half of the dataset is labeled.
Address October 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICCVW
Notes (down) LAMP; Approved no
Call Number Admin @ si @ ZVT2021 Serial 3672
Permanent link to this record
 
 
Author Javad Zolfaghari Bengar; Bogdan Raducanu; Joost Van de Weijer
Title When Deep Learners Change Their Mind: Learning Dynamics for Active Learning Type Conference Article
Year 2021 Publication 19th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal
Volume 13052 Issue 1 Pages 403-413
Keywords
Abstract Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.
Address September 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CAIP
Notes (down) LAMP; Approved no
Call Number Admin @ si @ ZRV2021 Serial 3673
Permanent link to this record
 
 
Author AN Ruchai; VI Kober; KA Dorofeev; VN Karnaukhov; Mikhail Mozerov
Title Classification of breast abnormalities using a deep convolutional neural network and transfer learning Type Journal Article
Year 2021 Publication Journal of Communications Technology and Electronics Abbreviated Journal
Volume 66 Issue 6 Pages 778–783
Keywords
Abstract A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes (down) LAMP; Approved no
Call Number Admin @ si @ RKD2022 Serial 3680
Permanent link to this record
 
 
Author Alex Gomez-Villa; Bartlomiej Twardowski; Lu Yu; Andrew Bagdanov; Joost Van de Weijer
Title Continually Learning Self-Supervised Representations With Projected Functional Regularization Type Conference Article
Year 2022 Publication CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) Abbreviated Journal
Volume Issue Pages 3866-3876
Keywords Computer vision; Conferences; Self-supervised learning; Image representation; Pattern recognition
Abstract Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable to acquire new knowledge incrementally – they are, in fact, mostly used only as a pre-training phase over IID data. In this work we investigate self-supervised methods in continual learning regimes without any replay
mechanism. We show that naive functional regularization,also known as feature distillation, leads to lower plasticity and limits continual learning performance. Instead, we propose Projected Functional Regularization in which a separate temporal projection network ensures that the newly learned feature space preserves information of the previous one, while at the same time allowing for the learning of new features. This prevents forgetting while maintaining the plasticity of the learner. Comparison with other incremental learning approaches applied to self-supervision demonstrates that our method obtains competitive performance in
different scenarios and on multiple datasets.
Address New Orleans, USA; 20 June 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes (down) LAMP: 600.147; 600.120 Approved no
Call Number Admin @ si @ GTY2022 Serial 3704
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Author Carola Figueroa Flores; Bogdan Raducanu; David Berga; Joost Van de Weijer
Title Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains Type Conference Article
Year 2021 Publication 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal
Volume 4 Issue Pages 163-171
Keywords
Abstract arXiv:2007.12562
Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classification. In the current paper, we propose an approach which does not require explicit saliency maps to improve image classification, but they are learned implicitely, during the training of an end-to-end image classification task. We show that our approach obtains similar results as the case when the saliency maps are provided explicitely. Combining RGB data with saliency maps represents a significant advantage for object recognition, especially for the case when training data is limited. We validate our method on several datasets for fine-grained classification tasks (Flowers, Birds and Cars). In addition, we show that our saliency estimation method, which is trained without any saliency groundtruth data, obtains competitive results on real image saliency benchmark (Toronto), and outperforms deep saliency models with synthetic images (SID4VAM).
Address Virtual; February 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference VISAPP
Notes (down) LAMP Approved no
Call Number Admin @ si @ FRB2021c Serial 3540
Permanent link to this record
 
 
Author Albin Soutif; Marc Masana; Joost Van de Weijer; Bartlomiej Twardowski
Title On the importance of cross-task features for class-incremental learning Type Conference Article
Year 2021 Publication Theory and Foundation of continual learning workshop of ICML Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks. The main difference with task-incremental learning, where a task-ID is available at inference time, is that the learner also needs to perform crosstask discrimination, i.e. distinguish between classes that have not been seen together. Approaches to tackle this problem are numerous and mostly make use of an external memory (buffer) of non-negligible size. In this paper, we ablate the learning of crosstask features and study its influence on the performance of basic replay strategies used for class-IL. We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance. Our experimental results show that future algorithms for class-incremental learning should not only prevent forgetting, but also aim to improve the quality of the cross-task features. This is especially important when the number of classes per task is small.
Address Virtual; July 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICMLW
Notes (down) LAMP Approved no
Call Number Admin @ si @ SMW2021 Serial 3588
Permanent link to this record
 
 
Author Fei Yang
Title Towards Practical Neural Image Compression Type Book Whole
Year 2021 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Images and videos are pervasive in our life and communication. With advances in smart and portable devices, high capacity communication networks and high definition cinema, image and video compression are more relevant than ever. Traditional block-based linear transform codecs such as JPEG, H.264/AVC or the recent H.266/VVC are carefully designed to meet not only the rate-distortion criteria, but also the practical requirements of applications.
Recently, a new paradigm based on deep neural networks (i.e., neural image/video compression) has become increasingly popular due to its ability to learn powerful nonlinear transforms and other coding tools directly from data instead of being crafted by humans, as was usual in previous coding formats. While achieving excellent rate-distortion performance, these approaches are still limited mostly to research environments due to heavy models and other practical limitations, such as being limited to function on a particular rate and due to high memory and computational cost. In this thesis, we study these practical limitations, and designing more practical neural image compression approaches.
After analyzing the differences between traditional and neural image compression, our first contribution is the modulated autoencoder (MAE), a framework that includes a mechanism to provide multiple rate-distortion options within a single model with comparable performance to independent models. In a second contribution, we propose the slimmable compressive autoencoder (SlimCAE), which in addition to variable rate, can optimize the complexity of the model and thus reduce significantly the memory and computational burden.
Modern generative models can learn custom image transformation directly from suitable datasets following encoder-decoder architectures, task known as image-to-image (I2I) translation. Building on our previous work, we study the problem of distributed I2I translation, where the latent representation is transmitted through a binary channel and decoded in a remote receiving side. We also propose a variant that can perform both translation and the usual autoencoding functionality.
Finally, we also consider neural video compression, where the autoencoder is typically augmented with temporal prediction via motion compensation. One of the main bottlenecks of that framework is the optical flow module that estimates the displacement to predict the next frame. Focusing on this module, we propose a method that improves the accuracy of the optical flow estimation and a simplified variant that reduces the computational cost.
Key words: neural image compression, neural video compression, optical flow, practical neural image compression, compressive autoencoders, image-to-image translation, deep learning.
Address December 2021
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Luis Herranz;Mikhail Mozerov;Yongmei Cheng
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-122714-7-8 Medium
Area Expedition Conference
Notes (down) LAMP Approved no
Call Number Admin @ si @ Yan2021 Serial 3608
Permanent link to this record