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Author Sergio Escalera; Petia Radeva edit  url
openurl 
  Title (up) Fast greyscale road sign model matching and recognition Type Miscellaneous
  Year 2004 Publication CCIA, IOS Press Abbreviated Journal  
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  Address Barcelona, Spain  
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  Area Expedition Conference  
  Notes HuPBA; MILAB Approved no  
  Call Number BCNPCL @ bcnpcl @ EsR2004 Serial 469  
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Author Jaume Amores; N. Sebe; Petia Radeva edit  url
openurl 
  Title (up) 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 Abbreviated Journal  
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  Address San Diego, CA (USA)  
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  Notes MILAB Approved no  
  Call Number ADAS @ adas @ ASR2005a Serial 541  
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Author Xavier Baro; Jordi Vitria edit  openurl
  Title (up) Feature Selection with Non-Parametric Mutual Information for Adaboost Learning Type Miscellaneous
  Year 2005 Publication Abbreviated Journal  
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  Area Expedition Conference  
  Notes OR;HuPBA;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BaV2005a Serial 582  
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Author M. Bressan; Jordi Vitria edit  openurl
  Title (up) Feature Subset Selection in an ICA Space Type Miscellaneous
  Year 2002 Publication Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BrV2002b Serial 277  
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Author Francesco Fabbri; Xianghang Liu; Jack R. McKenzie; Bartlomiej Twardowski; Tri Kurniawan Wijaya edit   pdf
url  openurl
  Title (up) FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems Type Miscellaneous
  Year 2023 Publication ARXIV Abbreviated Journal  
  Volume Issue Pages  
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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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  Notes LAMP Approved no  
  Call Number Admin @ si @ FLM2023 Serial 3980  
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Author Luis Herranz; Weiqing Min; Shuqiang Jiang edit  openurl
  Title (up) Food recognition and recipe analysis: integrating visual content, context and external knowledge Type Miscellaneous
  Year 2018 Publication Arxiv Abbreviated Journal  
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  Abstract 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 edit  openurl
  Title (up) Fundamentos de la Vision aplicada a la Robotica Autonoma. Type Miscellaneous
  Year 1997 Publication Fundamentos de la Vision aplicada a la Robotica Autonoma. Abbreviated Journal  
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  Notes Approved no  
  Call Number Admin @ si @ GoM1997 Serial 204  
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Author O. Fors; Xavier Otazu; J. Nuñez edit  openurl
  Title (up) 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. Abbreviated Journal  
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  Address Lleida  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ FON2001 Serial 94  
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Author M. Gonzalez-Audicana; Xavier Otazu; O. Fors; R Garcia; J. Nuñez edit  openurl
  Title (up) 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. Abbreviated Journal  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ GOF2002 Serial 291  
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Author Robert Benavente; Maria Vanrell edit  openurl
  Title (up) 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 Abbreviated Journal  
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  Address Aachen (Germany)  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ BeV2004 Serial 441  
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Author Marco Cotogni; Fei Yang; Claudio Cusano; Andrew Bagdanov; Joost Van de Weijer edit   pdf
openurl 
  Title (up) Gated Class-Attention with Cascaded Feature Drift Compensation for Exemplar-free Continual Learning of Vision Transformers Type Miscellaneous
  Year 2022 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  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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  Notes 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 edit   pdf
url  openurl
  Title (up) Generalized Continual Category Discovery Type Miscellaneous
  Year 2023 Publication arxiv Abbreviated Journal  
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  Abstract 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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  Notes LAMP Approved no  
  Call Number Admin @ si @ MRC2023 Serial 3985  
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Author V. Valev; B. Sankur; Petia Radeva edit  openurl
  Title (up) Generalized Non-Reducible Descriptors. Type Miscellaneous
  Year 1997 Publication Technical Report. Abbreviated Journal  
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  Notes MILAB Approved no  
  Call Number BCNPCL @ bcnpcl @ VSR1997 Serial 65  
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Author Josep Llados; Young-Bin Kwon edit  openurl
  Title (up) Graphics Recognition. Recent Advances and Perspectives Type Miscellaneous
  Year 2004 Publication LNCS 3080, ISBN: 3–540–22478–5 Abbreviated Journal  
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  Address Springer-Verlag  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ LlK2004 Serial 515  
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Author Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal edit   pdf
url  openurl
  Title (up) GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation Type Miscellaneous
  Year 2024 Publication Arxiv Abbreviated Journal  
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  Abstract 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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  Notes DAG Approved no  
  Call Number Admin @ si @ BBL2024b Serial 4023  
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