| Records |
| Author |
Sergio Escalera; Petia Radeva |
Title  |
Fast greyscale road sign model matching and recognition |
Type |
Miscellaneous |
| Year |
2004 |
Publication |
CCIA, IOS Press |
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Barcelona, Spain |
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HuPBA; MILAB |
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no |
| Call Number |
BCNPCL @ bcnpcl @ EsR2004 |
Serial |
469 |
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| Author |
Jaume Amores; N. Sebe; Petia Radeva |
| Title |
Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors |
Type |
Miscellaneous |
| Year |
2005 |
Publication |
IEEE Computer Society, International Conference on Computer Vision and Pattern Recognition (CVPR’05), 2(2):769–774 |
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San Diego, CA (USA) |
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MILAB |
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no |
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ADAS @ adas @ ASR2005a |
Serial |
541 |
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| Author |
Xavier Baro; Jordi Vitria |
| Title |
Feature Selection with Non-Parametric Mutual Information for Adaboost Learning |
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Miscellaneous |
| Year |
2005 |
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OR;HuPBA;MV |
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no |
| Call Number |
BCNPCL @ bcnpcl @ BaV2005a |
Serial |
582 |
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| Author |
M. Bressan; Jordi Vitria |
| Title |
Feature Subset Selection in an ICA Space |
Type |
Miscellaneous |
| Year |
2002 |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ BrV2002b |
Serial |
277 |
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| Author |
Francesco Fabbri; Xianghang Liu; Jack R. McKenzie; Bartlomiej Twardowski; Tri Kurniawan Wijaya |
| Title |
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems |
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Miscellaneous |
| Year |
2023 |
Publication |
ARXIV |
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| Abstract |
Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy. Instead of sending private data to a central server as in traditional approaches, FL decentralizes computations: devices train locally and share updates with a global server. A primary challenge in this setting is achieving fast and accurate model training – vital for recommendation systems where delays can compromise user engagement. This paper introduces FedFNN, an algorithm that accelerates decentralized model training. In FL, only a subset of users are involved in each training epoch. FedFNN employs supervised learning to predict weight updates from unsampled users, using updates from the sampled set. Our evaluations, using real and synthetic data, show: 1. FedFNN achieves training speeds 5x faster than leading methods, maintaining or improving accuracy; 2. the algorithm's performance is consistent regardless of client cluster variations; 3. FedFNN outperforms other methods in scenarios with limited client availability, converging more quickly. |
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LAMP |
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no |
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Admin @ si @ FLM2023 |
Serial |
3980 |
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| Author |
Luis Herranz; Weiqing Min; Shuqiang Jiang |
| Title |
Food recognition and recipe analysis: integrating visual content, context and external knowledge |
Type |
Miscellaneous |
| Year |
2018 |
Publication |
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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| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ HMJ2018 |
Serial |
3250 |
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| Author |
S. Gonzalez; A. Martinez |
| Title |
Fundamentos de la Vision aplicada a la Robotica Autonoma. |
Type |
Miscellaneous |
| Year |
1997 |
Publication |
Fundamentos de la Vision aplicada a la Robotica Autonoma. |
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no |
| Call Number |
Admin @ si @ GoM1997 |
Serial |
204 |
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| Author |
O. Fors; Xavier Otazu; J. Nuñez |
| Title |
Fusion Mediante Wavelets de Imagenes Spot-pan y del Satelite Tailandes TMSAT. |
Type |
Miscellaneous |
| Year |
2001 |
Publication |
Teledeteccion, Medio Ambiente y Cambio Global, IX Congreso Nacional de Teledeteccion, 546–550. |
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Lleida |
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CIC |
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no |
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CAT @ cat @ FON2001 |
Serial |
94 |
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| Author |
M. Gonzalez-Audicana; Xavier Otazu; O. Fors; R Garcia; J. Nuñez |
| Title |
Fusion of different spatial and spectral resolution images: development, apllication and comparison of new methods based on wavelets. |
Type |
Miscellaneous |
| Year |
2002 |
Publication |
Proceedings of the 1st. International Symposium Recent Advances in Quantitative Remote Sensing. |
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CIC |
Approved |
no |
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CAT @ cat @ GOF2002 |
Serial |
291 |
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| Author |
Robert Benavente; Maria Vanrell |
| Title |
Fuzzy Colour Naming Based on Sigmoid Membership Functions. |
Type |
Miscellaneous |
| Year |
2004 |
Publication |
CGIV 2004 Second European Conference on Colour in Graphics, Imaging and Vision, 135:139 |
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Aachen (Germany) |
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CIC |
Approved |
no |
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CAT @ cat @ BeV2004 |
Serial |
441 |
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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 |
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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 |
Daniel Marczak; Grzegorz Rypesc; Sebastian Cygert; Tomasz Trzcinski; Bartłomiej Twardowski |
| Title |
Generalized Continual Category Discovery |
Type |
Miscellaneous |
| Year |
2023 |
Publication |
arxiv |
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Most of Continual Learning (CL) methods push the limit of supervised learning settings, where an agent is expected to learn new labeled tasks and not forget previous knowledge. However, these settings are not well aligned with real-life scenarios, where a learning agent has access to a vast amount of unlabeled data encompassing both novel (entirely unlabeled) classes and examples from known classes. Drawing inspiration from Generalized Category Discovery (GCD), we introduce a novel framework that relaxes this assumption. Precisely, in any task, we allow for the existence of novel and known classes, and one must use continual version of unsupervised learning methods to discover them. We call this setting Generalized Continual Category Discovery (GCCD). It unifies CL and GCD, bridging the gap between synthetic benchmarks and real-life scenarios. With a series of experiments, we present that existing methods fail to accumulate knowledge from subsequent tasks in which unlabeled samples of novel classes are present. In light of these limitations, we propose a method that incorporates both supervised and unsupervised signals and mitigates the forgetting through the use of centroid adaptation. Our method surpasses strong CL methods adopted for GCD techniques and presents a superior representation learning performance. |
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LAMP |
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no |
| Call Number |
Admin @ si @ MRC2023 |
Serial |
3985 |
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| Author |
V. Valev; B. Sankur; Petia Radeva |
| Title |
Generalized Non-Reducible Descriptors. |
Type |
Miscellaneous |
| Year |
1997 |
Publication |
Technical Report. |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ VSR1997 |
Serial |
65 |
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| Author |
Josep Llados; Young-Bin Kwon |
| Title |
Graphics Recognition. Recent Advances and Perspectives |
Type |
Miscellaneous |
| Year |
2004 |
Publication |
LNCS 3080, ISBN: 3–540–22478–5 |
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Springer-Verlag |
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DAG |
Approved |
no |
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DAG @ dag @ LlK2004 |
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515 |
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| Author |
Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal |
| Title |
GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation |
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Miscellaneous |
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2024 |
Publication |
Arxiv |
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Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and complex models, while achieving high accuracy, can be computationally expensive and memory-intensive, making them impractical for deployment on resource constrained devices. Knowledge distillation allows us to create small and more efficient models that retain much of the performance of their larger counterparts. Here we present a graph-based knowledge distillation framework to correctly identify and localize the document objects in a document image. Here, we design a structured graph with nodes containing proposal-level features and edges representing the relationship between the different proposal regions. Also, to reduce text bias an adaptive node sampling strategy is designed to prune the weight distribution and put more weightage on non-text nodes. We encode the complete graph as a knowledge representation and transfer it from the teacher to the student through the proposed distillation loss by effectively capturing both local and global information concurrently. Extensive experimentation on competitive benchmarks demonstrates that the proposed framework outperforms the current state-of-the-art approaches. The code will be available at: this https URL. |
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DAG |
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no |
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Admin @ si @ BBL2024b |
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4023 |
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