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Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal |
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Title |
GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation |
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Miscellaneous |
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2024 |
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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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Admin @ si @ BBL2024b |
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4023 |
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Author |
Jose Elias Yauri; Aura Hernandez-Sabate; Pau Folch; Debora Gil |
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Title |
Mental Workload Detection Based on EEG Analysis |
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Conference Article |
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Year |
2021 |
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Artificial Intelligent Research and Development. Proceedings 23rd International Conference of the Catalan Association for Artificial Intelligence. |
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339 |
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268-277 |
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Cognitive states; Mental workload; EEG analysis; Neural Networks. |
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The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement.
Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training.
In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation. |
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Virtual; October 20-22 2021 |
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CCIA |
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IAM; 600.139; 600.118; 600.145 |
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Admin @ si @ |
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3723 |
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David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri |
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Title |
Blood Detection In IVUS Longitudinal Cuts Using AdaBoost With a Novel Feature Stability Criterion |
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Conference Article |
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2007 |
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Artificial Intelligence Research and Development. Proceedings of the 10th International Conference of the ACIA |
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163 |
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197–204 |
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978-1-58603-798-7 |
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CCIA’07 |
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MILAB |
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BCNPCL @ bcnpcl @ RRF2007a |
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831 |
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Author |
Eduard Vazquez; Francesc Tous; Ramon Baldrich; Maria Vanrell |
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Title |
n-Dimensional Distribution Reduction Preserving its Structure |
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2006 |
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Artificial Intelligence Research and Development, M. Polit et al. (Eds.), 146: 167–175 |
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IOS Press |
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CAT @ cat @ VTB2006a |
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681 |
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Agata Lapedriza; Jordi Vitria |
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Experimental Study of the Usefulness of External Face Features for Face Classification |
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2005 |
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Artificial Intelligence Research and Development, IOS Press, 99–106 |
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Amsterdam |
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OR;MV |
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BCNPCL @ bcnpcl @ LaV2005 |
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610 |
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Author |
Bogdan Raducanu; Jordi Vitria |
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Title |
Real-Time Face Tracking for Context-Aware Computing |
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2005 |
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Artificial Intelligence Research and Development, IOS Press, 91–98 |
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Amsterdam |
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OR;MV |
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BCNPCL @ bcnpcl @ RaV2005b |
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616 |
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Javier Vazquez; Maria Vanrell; Anna Salvatella; Eduard Vazquez |
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A colour space based on the image content |
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2007 |
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Artificial Intelligence Research and Development, C. Angulo and L. Godo, pp 205–212 IOS Press |
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CAT @ cat @ VVS2007 |
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829 |
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Author |
Meritxell Vinyals; Arnau Ramisa; Ricardo Toledo |
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Title |
An Evaluation of an Object Recognition Schema using Multiple Region Detectors |
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2007 |
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Artificial Intelligence Research and Development, 163:213–222, ISBN: 978–1–58603–798–7, Proceedings of the 10th International Conference of the ACIA (CCIA’07) |
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ADAS |
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Admin @ si @ VRT2007 |
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898 |
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Author |
Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Using the Average Landmark Vector Method for Robot Homing |
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Conference Article |
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2007 |
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Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA |
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163 |
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331–338 |
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978–1–58603–798–7 |
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CCIA’07 |
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RV;ADAS |
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Admin @ si @ GRL2007 |
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899 |
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Jordina Torrents-Barrena; Aida Valls; Petia Radeva; Meritxell Arenas; Domenec Puig |
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Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis |
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2015 |
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Artificial Intelligence Research and Development |
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277 |
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247 - 256 |
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Breast cancer disease has recently been classified into four subtypes regarding the molecular properties of the affected tumor region. For each patient, an accurate diagnosis of the specific type is vital to decide the most appropriate therapy in order to enhance life prospects. Nowadays, advanced therapeutic diagnosis research is focused on gene selection methods, which are not robust enough. Hence, we hypothesize that computer vision algorithms can offer benefits to address the problem of discriminating among them through X-Ray images. In this paper, we propose a novel approach driven by texture feature descriptors and machine learning techniques. First, we segment the tumour part through an active contour technique and then, we perform a complete fractal analysis to collect qualitative information of the region of interest in the feature extraction stage. Finally, several supervised and unsupervised classifiers are used to perform multiclass classification of the aforementioned data. The experimental results presented in this paper support that it is possible to establish a relation between each tumor subtype and the extracted features of the patterns revealed on mammograms. |
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IOS Press |
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Frontiers in Artificial Intelligence and Applications |
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MILAB |
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no |
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Admin @ si @TVR2015 |
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2780 |
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Author |
Carles Onielfa; Carles Casacuberta; Sergio Escalera |
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Title |
Influence in Social Networks Through Visual Analysis of Image Memes |
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Conference Article |
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2022 |
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Artificial Intelligence Research and Development |
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356 |
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71-80 |
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Memes evolve and mutate through their diffusion in social media. They have the potential to propagate ideas and, by extension, products. Many studies have focused on memes, but none so far, to our knowledge, on the users that post them, their relationships, and the reach of their influence. In this article, we define a meme influence graph together with suitable metrics to visualize and quantify influence between users who post memes, and we also describe a process to implement our definitions using a new approach to meme detection based on text-to-image area ratio and contrast. After applying our method to a set of users of the social media platform Instagram, we conclude that our metrics add information to already existing user characteristics. |
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HuPBA; no menciona |
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no |
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Admin @ si @ OCE2022 |
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3799 |
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Author |
Patricia Suarez; Henry Velesaca; Dario Carpio; Angel Sappa |
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Corn kernel classification from few training samples |
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2023 |
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Artificial Intelligence in Agriculture |
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9 |
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89-99 |
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This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types. |
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MSIAU |
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Admin @ si @ SVC2023 |
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3892 |
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Author |
Jaume Amores |
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Title |
Multiple Instance Classification: review, taxonomy and comparative study |
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Journal Article |
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2013 |
Publication |
Artificial Intelligence |
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AI |
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201 |
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81-105 |
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Multi-instance learning; Codebook; Bag-of-Words |
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Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL
methods. |
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Elsevier Science Publishers Ltd. Essex, UK |
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0004-3702 |
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ADAS; 601.042; 600.057 |
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Admin @ si @ Amo2013 |
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2273 |
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Author |
Jordi Gonzalez; Javier Varona; Xavier Roca; Juan J. Villanueva |
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Title |
Analysis of Human Walking Based on aSpaces |
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Miscellaneous |
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2004 |
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Articulated Motion and Deformable Objects, Third International Workshop, (AMDO 2004), Lecture Notes in Computer Science, F.J. Perales, B.A. Draper (Eds.), 3179:177–188 |
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Springer-Verlag, Berlin, Heidelberg |
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ISE |
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ISE @ ise @ GVR2004a |
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492 |
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Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
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3D Human Walking Modelling |
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2004 |
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Articulated Motion and Deformable Objects, Third International Workshop, (AMDO 2004), Lecture Notes in Computer Science, F.J. Perales, B.A. Draper (Eds.), 3179:111–122 |
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Springer-Verlag, Berlin, Heidelberg |
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no |
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Call Number |
ADAS @ adas @ SAM2004b |
Serial |
494 |
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