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Author |
Wenjuan Gong; Yue Zhang; Wei Wang; Peng Cheng; Jordi Gonzalez |
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Title |
Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition |
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Journal Article |
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Year |
2023 |
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ACM Transactions on Multimedia Computing, Communications, and Applications |
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TMCCA |
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20 |
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2 |
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1–20 |
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Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
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ISE |
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Admin @ si @ GZW2023 |
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3862 |
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Author |
Cristina Palmero; Oleg V Komogortsev; Sergio Escalera; Sachin S Talathi |
![goto web page url](img/www.gif)
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Title |
Multi-Rate Sensor Fusion for Unconstrained Near-Eye Gaze Estimation |
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Conference Article |
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Year |
2023 |
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Proceedings of the 2023 Symposium on Eye Tracking Research and Applications |
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1-8 |
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The power requirements of video-oculography systems can be prohibitive for high-speed operation on portable devices. Recently, low-power alternatives such as photosensors have been evaluated, providing gaze estimates at high frequency with a trade-off in accuracy and robustness. Potentially, an approach combining slow/high-fidelity and fast/low-fidelity sensors should be able to exploit their complementarity to track fast eye motion accurately and robustly. To foster research on this topic, we introduce OpenSFEDS, a near-eye gaze estimation dataset containing approximately 2M synthetic camera-photosensor image pairs sampled at 500 Hz under varied appearance and camera position. We also formulate the task of sensor fusion for gaze estimation, proposing a deep learning framework consisting in appearance-based encoding and temporal eye-state dynamics. We evaluate several single- and multi-rate fusion baselines on OpenSFEDS, achieving 8.7% error decrease when tracking fast eye movements with a multi-rate approach vs. a gaze forecasting approach operating with a low-speed sensor alone. |
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Tubingen; Germany; May 2023 |
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ETRA |
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HUPBA |
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no |
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Admin @ si @ PKE2023 |
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3923 |
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Author |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li |
![goto web page url](img/www.gif)
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Title |
Face Anti-spoofing Progress Driven by Academic Challenges |
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Year |
2023 |
Publication |
Advances in Face Presentation Attack Detection |
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With the ubiquity of facial authentication systems and the prevalence of security cameras around the world, the impact that facial presentation attack techniques may have is huge. However, research progress in this field has been slowed by a number of factors, including the lack of appropriate and realistic datasets, ethical and privacy issues that prevent the recording and distribution of facial images, the little attention that the community has given to potential ethnic biases among others. This chapter provides an overview of contributions derived from the organization of academic challenges in the context of face anti-spoofing detection. Specifically, we discuss the limitations of benchmarks and summarize our efforts in trying to boost research by the community via the participation in academic challenges |
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HUPBA |
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no |
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Admin @ si @ WGE2023c |
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3957 |
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Author |
Patricia Suarez; Dario Carpio; Angel Sappa |
![goto web page url](img/www.gif)
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Title |
Enhancement of guided thermal image super-resolution approaches |
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Journal Article |
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Year |
2024 |
Publication |
Neurocomputing |
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NEUCOM |
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Volume |
573 |
Issue |
127197 |
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1-17 |
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Guided image processing techniques are widely used to extract meaningful information from a guiding image and facilitate the enhancement of the guided one. This paper specifically addresses the challenge of guided thermal image super-resolution, where a low-resolution thermal image is enhanced using a high-resolution visible spectrum image. We propose a new strategy that enhances outcomes from current guided super-resolution methods. This is achieved by transforming the initial guiding data into a representation resembling a thermal-like image, which is more closely in sync with the intended output. Experimental results with upscale factors of 8 and 16, demonstrate the outstanding performance of our approach in guided thermal image super-resolution obtained by mapping the original guiding information to a thermal-like image representation. |
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MSIAU |
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no |
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Admin @ si @ SCS2024 |
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3998 |
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Author |
Mathieu Nicolas Delalandre; Jean-Yves Ramel; Ernest Valveny; Muhammad Muzzamil Luqman |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
A Performance Characterization Algorithm for Symbol Localization |
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Conference Article |
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Year |
2009 |
Publication |
8th IAPR International Workshop on Graphics Recognition |
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In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols). |
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La Rochelle; July 2009 |
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Springer |
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GREC |
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DAG |
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no |
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DAG @ dag @ DRV2009 |
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1443 |
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Author |
Alex Pardo; Albert Clapes; Sergio Escalera; Oriol Pujol |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Actions in Context: System for people with Dementia |
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Conference Article |
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Year |
2013 |
Publication |
2nd International Workshop on Citizen Sensor Networks (Citisen2013) at the European Conference on Complex Systems |
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3-14 |
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Multi-modal data Fusion; Computer vision; Wearable sensors; Gesture recognition; Dementia |
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Abstract |
In the next forty years, the number of people living with dementia is expected to triple. In the last stages, people affected by this disease become dependent. This hinders the autonomy of the patient and has a huge social impact in time, money and effort. Given this scenario, we propose an ubiquitous system capable of recognizing daily specific actions. The system fuses and synchronizes data obtained from two complementary modalities – ambient and egocentric. The ambient approach consists in a fixed RGB-Depth camera for user and object recognition and user-object interaction, whereas the egocentric point of view is given by a personal area network (PAN) formed by a few wearable sensors and a smartphone, used for gesture recognition. The system processes multi-modal data in real-time, performing paralleled task recognition and modality synchronization, showing high performance recognizing subjects, objects, and interactions, showing its reliability to be applied in real case scenarios. |
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Barcelona; September 2013 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-04177-3 |
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ECCS |
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HUPBA;MILAB |
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Admin @ si @ PCE2013 |
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2354 |
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Author |
Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection |
Type |
Conference Article |
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2015 |
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Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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3-12 |
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SSL; Pedestrian Detection |
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Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. |
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Santiago de Compostela; España; June 2015 |
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IbPRIA |
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ADAS; 600.057; 600.054; 600.076 |
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GRV2015; ADAS @ adas @ GRV2015 |
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2454 |
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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Spotting Graphical Symbols in Camera-Acquired Documents in Real Time |
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Book Chapter |
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2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
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3-10 |
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In this paper we present a system devoted to spot graphical symbols in camera-acquired document images. The system is based on the extraction and further matching of ORB compact local features computed over interest key-points. Then, the FLANN indexing framework based on approximate nearest neighbor search allows to efficiently match local descriptors between the captured scene and the graphical models. Finally, the RANSAC algorithm is used in order to compute the homography between the spotted symbol and its appearance in the document image. The proposed approach is efficient and is able to work in real time. |
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Springer Berlin Heidelberg |
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Bart Lamiroy; Jean-Marc Ogier |
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LNCS |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; 600.045; 600.055; 600.061; 600.077 |
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Admin @ si @ RKL2014 |
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2700 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Action Recognition by Pairwise Proximity Function Support Vector Machines with Dynamic Time Warping Kernels |
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Conference Article |
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2016 |
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29th Canadian Conference on Artificial Intelligence |
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9673 |
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3-14 |
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In the context of human action recognition using skeleton data, the 3D trajectories of joint points may be considered as multi-dimensional time series. The traditional recognition technique in the literature is based on time series dis(similarity) measures (such as Dynamic Time Warping). For these general dis(similarity) measures, k-nearest neighbor algorithms are a natural choice. However, k-NN classifiers are known to be sensitive to noise and outliers. In this paper, a new class of Support Vector Machine that is applicable to trajectory classification, such as action recognition, is developed by incorporating an efficient time-series distances measure into the kernel function. More specifically, the derivative of Dynamic Time Warping (DTW) distance measure is employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite (PSD) kernels in the SVM formulation. The recognition results of the proposed technique on two action recognition datasets demonstrates the ourperformance of our methodology compared to the state-of-the-art methods. Remarkably, we obtained 89 % accuracy on the well-known MSRAction3D dataset using only 3D trajectories of body joints obtained by Kinect |
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Victoria; Canada; May 2016 |
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Springer International Publishing |
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AI |
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HuPBA;MILAB; |
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no |
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Admin @ si @ BGE2016b |
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2770 |
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Author |
Alvaro Peris; Marc Bolaños; Petia Radeva; Francisco Casacuberta |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Video Description Using Bidirectional Recurrent Neural Networks |
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Conference Article |
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2016 |
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25th International Conference on Artificial Neural Networks |
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2 |
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3-11 |
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Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks |
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Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames. |
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Barcelona; September 2016 |
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ICANN |
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MILAB; |
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no |
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Admin @ si @ PBR2016 |
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2833 |
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Permanent link to this record |
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Author |
Giuseppe De Gregorio; Sanket Biswas; Mohamed Ali Souibgui; Asma Bensalah; Josep Llados; Alicia Fornes; Angelo Marcelli |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts |
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Conference Article |
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2022 |
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Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
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13639 |
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N-gram spotting; Few-shot learning; Multimodal understanding; Historical handwritten collections |
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Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction. |
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December 04 – 07, 2022; Hyderabad, India |
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ICFHR |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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no |
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Admin @ si @ GBS2022 |
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3733 |
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Author |
Francesc Net; Marc Folia; Pep Casals; Lluis Gomez |
![goto web page url](img/www.gif)
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Title |
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections |
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Conference Article |
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2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
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14191 |
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3-17 |
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Image deduplication; Near-duplicate images detection; Transductive Learning; Photographic Archives; Deep Learning |
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Abstract |
This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use case differs from laboratory settings as the deployment dataset is available in advance, allowing the use of transductive learning. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). Our approach involves pre-training a deep neural network on a large dataset and then fine-tuning the network on the unlabeled target collection with self-supervised learning. The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset. |
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San Jose; CA; USA; August 2023 |
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Admin @ si @ NFC2023 |
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3859 |
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Albert Tatjer; Bhalaji Nagarajan; Ricardo Marques; Petia Radeva |
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CCLM: Class-Conditional Label Noise Modelling |
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2023 |
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11th Iberian Conference on Pattern Recognition and Image Analysis |
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14062 |
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3-14 |
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The performance of deep neural networks highly depends on the quality and volume of the training data. However, cost-effective labelling processes such as crowdsourcing and web crawling often lead to data with noisy (i.e., wrong) labels. Making models robust to this label noise is thus of prime importance. A common approach is using loss distributions to model the label noise. However, the robustness of these methods highly depends on the accuracy of the division of training set into clean and noisy samples. In this work, we dive in this research direction highlighting the existing problem of treating this distribution globally and propose a class-conditional approach to split the clean and noisy samples. We apply our approach to the popular DivideMix algorithm and show how the local treatment fares better with respect to the global treatment of loss distribution. We validate our hypothesis on two popular benchmark datasets and show substantial improvements over the baseline experiments. We further analyze the effectiveness of the proposal using two different metrics – Noise Division Accuracy and Classiness. |
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Alicante; Spain; June 2023 |
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IbPRIA |
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MILAB |
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Admin @ si @ TNM2023 |
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3925 |
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Josep Llados; J. Lopez-Krahe; D. Archambault |
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Special Issue on Information Technologies for Visually Impaired People |
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2007 |
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Novatica |
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DAG @ dag @ LLA2007a |
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903 |
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Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva |
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Traffic-Sign Recognition Systems |
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2011 |
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SpringerBriefs in Computer Science |
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5-13 |
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Springer London |
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978-1-4471-2244-9 |
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MILAB; OR;HuPBA;MV |
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Admin @ si @ EBP2011 |
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1801 |
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