| Records |
| Author |
Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca |
| Title |
Human Body Pose Estimation in Multi-view Environments |
Type |
Book Chapter |
| Year |
2022 |
Publication  |
ICT Applications for Smart Cities. Intelligent Systems Reference Library |
Abbreviated Journal |
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| Volume |
224 |
Issue |
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Pages |
79-99 |
| Keywords |
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| Abstract |
This chapter tackles the challenging problem of human pose estimation in multi-view environments to handle scenes with self-occlusions. The proposed approach starts by first estimating the camera pose—extrinsic parameters—in multi-view scenarios; due to few real image datasets, different virtual scenes are generated by using a special simulator, for training and testing the proposed convolutional neural network based approaches. Then, these extrinsic parameters are used to establish the relation between different cameras into the multi-view scheme, which captures the pose of the person from different points of view at the same time. The proposed multi-view scheme allows to robustly estimate human body joints’ position even in situations where they are occluded. This would help to avoid possible false alarms in behavioral analysis systems of smart cities, as well as applications for physical therapy, safe moving assistance for the elderly among other. The chapter concludes by presenting experimental results in real scenes by using state-of-the-art and the proposed multi-view approaches. |
| Address |
September 2022 |
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| Publisher |
Springer |
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ISRL |
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ISBN |
978-3-031-06306-0 |
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| Notes |
MSIAU; MACO |
Approved |
no |
| Call Number |
Admin @ si @ CSV2022b |
Serial |
3810 |
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| Author |
Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu |
| Title |
Waste Classification with Small Datasets and Limited Resources |
Type |
Book Chapter |
| Year |
2022 |
Publication |
ICT Applications for Smart Cities. Intelligent Systems Reference Library |
Abbreviated Journal |
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| Volume |
224 |
Issue |
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Pages |
185-203 |
| Keywords |
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| Abstract |
Automatic waste recycling has become a very important societal challenge nowadays, raising people’s awareness for a cleaner environment and a more sustainable lifestyle. With the transition to Smart Cities, and thanks to advanced ICT solutions, this problem has received a new impulse. The waste recycling focus has shifted from general waste treating facilities to an individual responsibility, where each person should become aware of selective waste separation. The surge of the mobile devices, accompanied by a significant increase in computation power, has potentiated and facilitated this individual role. An automated image-based waste classification mechanism can help with a more efficient recycling and a reduction of contamination from residuals. Despite the good results achieved with the deep learning methodologies for this task, the Achille’s heel is that they require large neural networks which need significant computational resources for training and therefore are not suitable for mobile devices. To circumvent this apparently intractable problem, we will rely on knowledge distillation in order to transfer the network’s knowledge from a larger network (called ‘teacher’) to a smaller, more compact one, (referred as ‘student’) and thus making it possible the task of image classification on a device with limited resources. For evaluation, we considered as ‘teachers’ large architectures such as InceptionResNet or DenseNet and as ‘students’, several configurations of the MobileNets. We used the publicly available TrashNet dataset to demonstrate that the distillation process does not significantly affect system’s performance (e.g. classification accuracy) of the student network. |
| Address |
September 2022 |
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Springer |
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ISRL |
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978-3-031-06306-0 |
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LAMP |
Approved |
no |
| Call Number |
Admin @ si @ |
Serial |
3813 |
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| Author |
Miguel Oliveira; V.Santos; Angel Sappa |
| Title |
Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition |
Type |
Conference Article |
| Year |
2012 |
Publication |
IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles |
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Algarve; Portugal |
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PPNIV |
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ADAS |
Approved |
no |
| Call Number |
Admin @ si @ OSS2012c |
Serial |
2159 |
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| Author |
Parichehr Behjati Ardakani; Pau Rodriguez; Carles Fernandez; Armin Mehri; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez |
| Title |
Frequency-based Enhancement Network for Efficient Super-Resolution |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
10 |
Issue |
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Pages |
57383-57397 |
| Keywords |
Deep learning; Frequency-based methods; Lightweight architectures; Single image super-resolution |
| Abstract |
Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of high-frequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low- and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model — Frequency-based Enhancement Network (FENet) — based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-of-the-art SR algorithms in terms of visual quality, memory footprint, and inference time. The code is available at https://github.com/pbehjatii/FENet |
| Address |
18 May 2022 |
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| Publisher |
IEEE |
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ISE |
Approved |
no |
| Call Number |
Admin @ si @ BRF2022a |
Serial |
3747 |
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| Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Debora Gil |
| Title |
Continuous head pose estimation using manifold subspace embedding and multivariate regression |
Type |
Journal Article |
| Year |
2018 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
6 |
Issue |
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Pages |
18325 - 18334 |
| Keywords |
Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression |
| Abstract |
In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees. |
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Edition |
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| ISSN |
2169-3536 |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ DMH2018b |
Serial |
3091 |
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| Author |
Jianzhy Guo; Zhen Lei; Jun Wan; Egils Avots; Noushin Hajarolasvadi; Boris Knyazev; Artem Kuharenko; Julio C. S. Jacques Junior; Xavier Baro; Hasan Demirel; Sergio Escalera; Juri Allik; Gholamreza Anbarjafari |
| Title |
Dominant and Complementary Emotion Recognition from Still Images of Faces |
Type |
Journal Article |
| Year |
2018 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
6 |
Issue |
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Pages |
26391 - 26403 |
| Keywords |
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| Abstract |
Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition. |
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| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ GLW2018 |
Serial |
3122 |
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| Author |
Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
| Title |
Recognizing Food Places in Egocentric Photo-Streams Using Multi-Scale Atrous Convolutional Networks and Self-Attention Mechanism |
Type |
Journal Article |
| Year |
2019 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
7 |
Issue |
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Pages |
39069-39082 |
| Keywords |
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| Abstract |
Wearable sensors (e.g., lifelogging cameras) represent very useful tools to monitor people's daily habits and lifestyle. Wearable cameras are able to continuously capture different moments of the day of their wearers, their environment, and interactions with objects, people, and places reflecting their personal lifestyle. The food places where people eat, drink, and buy food, such as restaurants, bars, and supermarkets, can directly affect their daily dietary intake and behavior. Consequently, developing an automated monitoring system based on analyzing a person's food habits from daily recorded egocentric photo-streams of the food places can provide valuable means for people to improve their eating habits. This can be done by generating a detailed report of the time spent in specific food places by classifying the captured food place images to different groups. In this paper, we propose a self-attention mechanism with multi-scale atrous convolutional networks to generate discriminative features from image streams to recognize a predetermined set of food place categories. We apply our model on an egocentric food place dataset called “EgoFoodPlaces” that comprises of 43 392 images captured by 16 individuals using a lifelogging camera. The proposed model achieved an overall classification accuracy of 80% on the “EgoFoodPlaces” dataset, respectively, outperforming the baseline methods, such as VGG16, ResNet50, and InceptionV3. |
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| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ SRA2019 |
Serial |
3296 |
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| Author |
Jiaolong Xu; Liang Xiao; Antonio Lopez |
| Title |
Self-supervised Domain Adaptation for Computer Vision Tasks |
Type |
Journal Article |
| Year |
2019 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
7 |
Issue |
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Pages |
156694 - 156706 |
| Keywords |
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| Abstract |
Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at this link. https://github.com/Jiaolong/self-supervised-da. |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ XXL2019 |
Serial |
3302 |
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| Author |
Fei Yang; Yongmei Cheng; Joost Van de Weijer; Mikhail Mozerov |
| Title |
Improved Discrete Optical Flow Estimation With Triple Image Matching Cost |
Type |
Journal Article |
| Year |
2020 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
8 |
Issue |
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Pages |
17093 - 17102 |
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| Abstract |
Approaches that use more than two consecutive video frames in the optical flow estimation have a long research history. However, almost all such methods utilize extra information for a pre-processing flow prediction or for a post-processing flow correction and filtering. In contrast, this paper differs from previously developed techniques. We propose a new algorithm for the likelihood function calculation (alternatively the matching cost volume) that is used in the maximum a posteriori estimation. We exploit the fact that in general, optical flow is locally constant in the sense of time and the likelihood function depends on both the previous and the future frame. Implementation of our idea increases the robustness of optical flow estimation. As a result, our method outperforms 9% over the DCFlow technique, which we use as prototype for our CNN based computation architecture, on the most challenging MPI-Sintel dataset for the non-occluded mask metric. Furthermore, our approach considerably increases the accuracy of the flow estimation for the matching cost processing, consequently outperforming the original DCFlow algorithm results up to 50% in occluded regions and up to 9% in non-occluded regions on the MPI-Sintel dataset. The experimental section shows that the proposed method achieves state-of-the-arts results especially on the MPI-Sintel dataset. |
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| Notes |
LAMP; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ YCW2020 |
Serial |
3345 |
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| Author |
Alejandro Cartas; Petia Radeva; Mariella Dimiccoli |
| Title |
Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications |
Type |
Journal Article |
| Year |
2020 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
8 |
Issue |
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Pages |
77344 - 77363 |
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| Abstract |
Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications. |
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MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ CRD2020 |
Serial |
3436 |
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| Author |
Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Esmitt Ramirez; Carles Sanchez |
| Title |
Intraoperative Extraction of Airways Anatomy in VideoBronchoscopy |
Type |
Journal Article |
| Year |
2020 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
8 |
Issue |
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Pages |
159696 - 159704 |
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A main bottleneck in bronchoscopic biopsy sampling is to efficiently reach the lesion navigating across bronchial levels. Any guidance system should be able to localize the scope position during the intervention with minimal costs and alteration of clinical protocols. With the final goal of an affordable image-based guidance, this work presents a novel strategy to extract and codify the anatomical structure of bronchi, as well as, the scope navigation path from videobronchoscopy. Experiments using interventional data show that our method accurately identifies the bronchial structure. Meanwhile, experiments using simulated data verify that the extracted navigation path matches the 3D route. |
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| Notes |
IAM; 600.139; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ GEB2020 |
Serial |
3467 |
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| Author |
Gabriel Villalonga; Antonio Lopez |
| Title |
Co-Training for On-Board Deep Object Detection |
Type |
Journal Article |
| Year |
2020 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
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Issue |
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Pages |
194441 - 194456 |
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Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation. |
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ADAS; 600.118 |
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no |
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Admin @ si @ ViL2020 |
Serial |
3488 |
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| Author |
Xavier Soria; Gonzalo Pomboza-Junez; Angel Sappa |
| Title |
LDC: Lightweight Dense CNN for Edge Detection |
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Journal Article |
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2022 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
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10 |
Issue |
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Pages |
68281-68290 |
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This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC |
| Address |
27 June 2022 |
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IEEE |
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MSIAU; MACO; 600.160; 600.167 |
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no |
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Admin @ si @ SPS2022 |
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3751 |
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| Author |
Alina Matei; Andreea Glavan; Petia Radeva; Estefania Talavera |
| Title |
Towards Eating Habits Discovery in Egocentric Photo-Streams |
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Journal Article |
| Year |
2021 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
9 |
Issue |
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Pages |
17495-17506 |
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| Abstract |
Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioral pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals. |
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MILAB; no proj |
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no |
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Admin @ si @ MGR2021 |
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3637 |
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| Author |
Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez |
| Title |
Logo Detection With No Priors |
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Journal Article |
| Year |
2021 |
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IEEE Access |
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ACCESS |
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9 |
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106998-107011 |
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| Abstract |
In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors. |
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ISE |
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Admin @ si @ VGR2021 |
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3664 |
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