| Records |
Author  |
Marçal Rusiñol; Josep Llados |
| Title |
Symbol Spotting in Technical Drawings Using Vectorial Signatures |
Type |
Miscellaneous |
| Year |
2005 |
Publication |
6th IAPR International Workshop on Graphics Recognition (GREC 2005), 35–45 |
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| Address |
Hong Kong |
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DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RuL2005 |
Serial |
579 |
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| Author |
Marcin Przewiezlikowski; Mateusz Pyla; Bartosz Zielinski; Bartłomiej Twardowski; Jacek Tabor; Marek Smieja |
| Title |
Augmentation-aware Self-supervised Learning with Guided Projector |
Type |
Miscellaneous |
| Year |
2023 |
Publication |
arxiv |
Abbreviated Journal |
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| Abstract |
Self-supervised learning (SSL) is a powerful technique for learning robust representations from unlabeled data. By learning to remain invariant to applied data augmentations, methods such as SimCLR and MoCo are able to reach quality on par with supervised approaches. However, this invariance may be harmful to solving some downstream tasks which depend on traits affected by augmentations used during pretraining, such as color. In this paper, we propose to foster sensitivity to such characteristics in the representation space by modifying the projector network, a common component of self-supervised architectures. Specifically, we supplement the projector with information about augmentations applied to images. In order for the projector to take advantage of this auxiliary conditioning when solving the SSL task, the feature extractor learns to preserve the augmentation information in its representations. Our approach, coined Conditional Augmentation-aware Self-supervised Learning (CASSLE), is directly applicable to typical joint-embedding SSL methods regardless of their objective functions. Moreover, it does not require major changes in the network architecture or prior knowledge of downstream tasks. In addition to an analysis of sensitivity towards different data augmentations, we conduct a series of experiments, which show that CASSLE improves over various SSL methods, reaching state-of-the-art performance in multiple downstream tasks. |
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LAMP |
Approved |
no |
| Call Number |
Admin @ si @ PPZ2023 |
Serial |
3971 |
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| Author |
Marco Cotogni; Fei Yang; Claudio Cusano; Andrew Bagdanov; Joost Van de Weijer |
| Title |
Exemplar-free Continual Learning of Vision Transformers via Gated Class-Attention and Cascaded Feature Drift Compensation |
Type |
Miscellaneous |
| Year |
2023 |
Publication |
ARXIV |
Abbreviated Journal |
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| Abstract |
We propose a new method for exemplar-free class incremental training of ViTs. The main challenge of exemplar-free continual learning is maintaining plasticity of the learner without causing catastrophic forgetting of previously learned tasks. This is often achieved via exemplar replay which can help recalibrate previous task classifiers to the feature drift which occurs when learning new tasks. Exemplar replay, however, comes at the cost of retaining samples from previous tasks which for many applications may not be possible. To address the problem of continual ViT training, we first propose gated class-attention to minimize the drift in the final ViT transformer block. This mask-based gating is applied to class-attention mechanism of the last transformer block and strongly regulates the weights crucial for previous tasks. Importantly, gated class-attention does not require the task-ID during inference, which distinguishes it from other parameter isolation methods. Secondly, we propose a new method of feature drift compensation that accommodates feature drift in the backbone when learning new tasks. The combination of gated class-attention and cascaded feature drift compensation allows for plasticity towards new tasks while limiting forgetting of previous ones. Extensive experiments performed on CIFAR-100, Tiny-ImageNet and ImageNet100 demonstrate that our exemplar-free method obtains competitive results when compared to rehearsal based ViT methods. |
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LAMP |
Approved |
no |
| Call Number |
Admin @ si @ CYC2023 |
Serial |
3981 |
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| Author |
Marco Cotogni; Fei Yang; Claudio Cusano; Andrew Bagdanov; Joost Van de Weijer |
| Title |
Gated Class-Attention with Cascaded Feature Drift Compensation for Exemplar-free Continual Learning of Vision Transformers |
Type |
Miscellaneous |
| Year |
2022 |
Publication |
Arxiv |
Abbreviated Journal |
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Issue |
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Pages |
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| Keywords |
Marco Cotogni, Fei Yang, Claudio Cusano, Andrew D. Bagdanov, Joost van de Weijer |
| Abstract |
We propose a new method for exemplar-free class incremental training of ViTs. The main challenge of exemplar-free continual learning is maintaining plasticity of the learner without causing catastrophic forgetting of previously learned tasks. This is often achieved via exemplar replay which can help recalibrate previous task classifiers to the feature drift which occurs when learning new tasks. Exemplar replay, however, comes at the cost of retaining samples from previous tasks which for many applications may not be possible. To address the problem of continual ViT training, we first propose gated class-attention to minimize the drift in the final ViT transformer block. This mask-based gating is applied to class-attention mechanism of the last transformer block and strongly regulates the weights crucial for previous tasks. Importantly, gated class-attention does not require the task-ID during inference, which distinguishes it from other parameter isolation methods. Secondly, we propose a new method of feature drift compensation that accommodates feature drift in the backbone when learning new tasks. The combination of gated class-attention and cascaded feature drift compensation allows for plasticity towards new tasks while limiting forgetting of previous ones. Extensive experiments performed on CIFAR-100, Tiny-ImageNet and ImageNet100 demonstrate that our exemplar-free method obtains competitive results when compared to rehearsal based ViT methods. |
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LAMP; no proj |
Approved |
no |
| Call Number |
Admin @ si @ CYC2022 |
Serial |
3827 |
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| Author |
Marco Pedersoli |
| Title |
A Multiresolution Cascade for Human Detection |
Type |
Miscellaneous |
| Year |
2008 |
Publication |
CVC Technical Report #126 |
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Barcelona, Spain |
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ISE |
Approved |
no |
| Call Number |
Admin @ si @ Ped2008 |
Serial |
1148 |
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| Author |
Maria Vanrell; Felipe Lumbreras; A. Pujol; Ramon Baldrich; Josep Llados; Juan J. Villanueva |
| Title |
Colour Normalisation Based on Background Information. |
Type |
Miscellaneous |
| Year |
2001 |
Publication |
Proceeding ICIP 2001, IEEE International Conference on Image Processing |
Abbreviated Journal |
ICIP 2001 |
| Volume |
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Issue |
1 |
Pages |
874–877 |
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| Address |
Grecia. |
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| Notes |
ADAS;DAG;CIC |
Approved |
no |
| Call Number |
ADAS @ adas @ VLP2001 |
Serial |
167 |
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| Author |
Marwa Dhiaf; Mohamed Ali Souibgui; Kai Wang; Yuyang Liu; Yousri Kessentini; Alicia Fornes; Ahmed Cheikh Rouhou |
| Title |
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition |
Type |
Miscellaneous |
| Year |
2023 |
Publication |
Arxiv |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Abstract |
Self-supervised learning has recently emerged as a strong alternative in document analysis. These approaches are now capable of learning high-quality image representations and overcoming the limitations of supervised methods, which require a large amount of labeled data. However, these methods are unable to capture new knowledge in an incremental fashion, where data is presented to the model sequentially, which is closer to the realistic scenario. In this paper, we explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition, as an example of sequence recognition. Our method consists in adding intermediate layers called adapters for each task, and efficiently distilling knowledge from the previous model while learning the current task. Our proposed framework is efficient in both computation and memory complexity. To demonstrate its effectiveness, we evaluate our method by transferring the learned model to diverse text recognition downstream tasks, including Latin and non-Latin scripts. As far as we know, this is the first application of continual self-supervised learning for handwritten text recognition. We attain state-of-the-art performance on English, Italian and Russian scripts, whilst adding only a few parameters per task. The code and trained models will be publicly available. |
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DAG |
Approved |
no |
| Call Number |
Admin @ si @ DSW2023 |
Serial |
3851 |
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| Author |
Mateusz Pyla; Kamil Deja; Bartłomiej Twardowski; Tomasz Trzcinski |
| Title |
Bayesian Flow Networks in Continual Learning |
Type |
Miscellaneous |
| Year |
2023 |
Publication |
arxiv |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian inference which make them suitable in the context of continual learning. We delve into the mechanics behind BFNs and conduct the experiments to empirically verify the generative capabilities on non-stationary data. |
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LAMP |
Approved |
no |
| Call Number |
Admin @ si @ PDT2023 |
Serial |
3972 |
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| Author |
Matthias S. Keil; Jordi Vitria |
| Title |
Does the brain generate representations of smooth brightness gradients? A novel account for Mach bands, Chevreul’s illusion, and a variant of the Ehrenstein disk |
Type |
Miscellaneous |
| Year |
2005 |
Publication |
European Conference on Visual Perception |
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OR;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ KeV2005b |
Serial |
607 |
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| Author |
Maya Dimitrova; Ch. Roumenin; Petia Radeva; David Rotger; Juan J. Villanueva |
| Title |
Multimodal Intelligent System for Cardiovascular Diagnosis |
Type |
Miscellaneous |
| Year |
2003 |
Publication |
Automation and Informatics, any XXXVII, num. 3 |
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MILAB |
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no |
| Call Number |
BCNPCL @ bcnpcl @ DRR2003 |
Serial |
374 |
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| Author |
Maya Dimitrova; I. Terziev; Petia Radeva; Juan J. Villanueva |
| Title |
Java-Servlet Technology for Building New Web Document Classifiers |
Type |
Miscellaneous |
| Year |
2004 |
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Varna (Bulgaria) |
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MILAB |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ DTR2004 |
Serial |
476 |
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| Author |
Maya Dimitrova; N. Kushmerick; Petia Radeva; Juan J. Villanueva |
| Title |
User Assesment of a Visual Genre Classifier |
Type |
Miscellaneous |
| Year |
2003 |
Publication |
Proceedings of the 3rd IASTED Int. Conference Visualization, Imaging and Image Processing |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ DKR2003 |
Serial |
372 |
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| Author |
Maya Dimitrova; Petia Radeva; David Rotger; D. Boyadjiev; Juan J. Villanueva |
| Title |
Advanced Cardiological Diagnosis via Intelligent Image Analysis |
Type |
Miscellaneous |
| Year |
2004 |
Publication |
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Varna (Bulgaria) |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ DRR2004 |
Serial |
477 |
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Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Syeda Furruka Banu; Farhan Akram; Forhad U. H. Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig |
| Title |
MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network |
Type |
Miscellaneous |
| Year |
2019 |
Publication |
Arxiv |
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CoRR abs/1907.00856
Skin lesion segmentation in dermoscopic images is a challenge due to their blurry and irregular boundaries. Most of the segmentation approaches based on deep learning are time and memory consuming due to the hundreds of millions of parameters. Consequently, it is difficult to apply them to real dermatoscope devices with limited GPU and memory resources. In this paper, we propose a lightweight and efficient Generative Adversarial Networks (GAN) model, called MobileGAN for skin lesion segmentation. More precisely, the MobileGAN combines 1D non-bottleneck factorization networks with position and channel attention modules in a GAN model. The proposed model is evaluated on the test dataset of the ISBI 2017 challenges and the validation dataset of ISIC 2018 challenges. Although the proposed network has only 2.35 millions of parameters, it is still comparable with the state-of-the-art. The experimental results show that our MobileGAN obtains comparable performance with an accuracy of 97.61%. |
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| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ MRA2019 |
Serial |
3384 |
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| Author |
Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Antonio Moreno; Petia Radeva; Domenec Puig |
| Title |
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network. |
Type |
Miscellaneous |
| Year |
2018 |
Publication |
Arxiv |
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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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MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ KJR2018 |
Serial |
3235 |
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