| Records |
| Author |
Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez |
| Title |
Semantic Monocular Depth Estimation Based on Artificial Intelligence |
Type |
Journal Article |
| Year |
2020 |
Publication |
IEEE Intelligent Transportation Systems Magazine |
Abbreviated Journal |
ITSM |
| Volume |
13 |
Issue |
4 |
Pages |
99-103 |
| Keywords |
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| Abstract |
Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation. |
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| Notes |
ADAS; 600.124; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ GUH2019 |
Serial |
3306 |
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| Author |
Estefania Talavera; Maria Leyva-Vallina; Md. Mostafa Kamal Sarker; Domenec Puig; Nicolai Petkov; Petia Radeva |
| Title |
Hierarchical approach to classify food scenes in egocentric photo-streams |
Type |
Journal Article |
| Year |
2020 |
Publication |
IEEE Journal of Biomedical and Health Informatics |
Abbreviated Journal |
J-BHI |
| Volume |
24 |
Issue |
3 |
Pages |
866 - 877 |
| Keywords |
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| Abstract |
Recent studies have shown that the environment where people eat can affect their nutritional behaviour. In this work, we provide automatic tools for a personalised analysis of a person's health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, that is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56\% and 65\%, respectively, clearly outperforming the baseline methods. |
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| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ TLM2020 |
Serial |
3380 |
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| Author |
Albert Clapes; Julio C. S. Jacques Junior; Carla Morral; Sergio Escalera |
| Title |
ChaLearn LAP 2020 Challenge on Identity-preserved Human Detection: Dataset and Results |
Type |
Conference Article |
| Year |
2020 |
Publication |
15th IEEE International Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
801-808 |
| Keywords |
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| Abstract |
This paper summarizes the ChaLearn Looking at People 2020 Challenge on Identity-preserved Human Detection (IPHD). For the purpose, we released a large novel dataset containing more than 112K pairs of spatiotemporally aligned depth and thermal frames (and 175K instances of humans) sampled from 780 sequences. The sequences contain hundreds of non-identifiable people appearing in a mix of in-the-wild and scripted scenarios recorded in public and private places. The competition was divided into three tracks depending on the modalities exploited for the detection: (1) depth, (2) thermal, and (3) depth-thermal fusion. Color was also captured but only used to facilitate the groundtruth annotation. Still the temporal synchronization of three sensory devices is challenging, so bad temporal matches across modalities can occur. Hence, the labels provided should considered “weak”, although test frames were carefully selected to minimize this effect and ensure the fairest comparison of the participants’ results. Despite this added difficulty, the results got by the participants demonstrate current fully-supervised methods can deal with that and achieve outstanding detection performance when measured in terms of AP@0.50. |
| Address |
Virtual; November 2020 |
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FG |
| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ CJM2020 |
Serial |
3501 |
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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 |
| Keywords |
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| Abstract |
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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Conference |
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| Notes |
IAM; 600.139; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ GEB2020 |
Serial |
3467 |
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| Author |
Lorenzo Porzi; Markus Hofinger; Idoia Ruiz; Joan Serrat; Samuel Rota Bulo; Peter Kontschieder |
| Title |
Learning Multi-Object Tracking and Segmentation from Automatic Annotations |
Type |
Conference Article |
| Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
6845-6854 |
| Keywords |
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| Abstract |
In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of expensive and time-consuming manual annotation approaches. We leverage state-of-the-art instance segmentation results in combination with optical flow predictions, also trained on automatically harvested training data. Our second major contribution is MOTSNet – a deep learning, tracking-by-detection architecture for MOTS – deploying a novel mask-pooling layer for improved object association over time. Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1.9%/+7.5% on cars/pedestrians), and MOTSNet improves by +4.1% over previously best methods on the MOTSChallenge dataset. Our most impressive finding is that we can improve over previous best-performing works, even in complete absence of manually annotated MOTS training data. |
| Address |
virtual; June 2020 |
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Conference |
CVPR |
| Notes |
ADAS; 600.124; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ PHR2020 |
Serial |
3402 |
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| Author |
Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski |
| Title |
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch |
Type |
Conference Article |
| Year |
2020 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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Pages |
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| Address |
Aspen; Colorado; USA; March 2020 |
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Conference |
WACV |
| Notes |
MSIAU; 600.122; 600.130 |
Approved |
no |
| Call Number |
Admin @ si @ RMP2020 |
Serial |
3291 |
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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 |
| Keywords |
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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 |
Angel Morera; Angel Sanchez; A. Belen Moreno; Angel Sappa; Jose F. Velez |
| Title |
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities |
Type |
Journal Article |
| Year |
2020 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
| Volume |
20 |
Issue |
16 |
Pages |
4587 |
| Keywords |
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| Abstract |
This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included. |
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| Notes |
MSIAU; 600.130; 601.349; 600.122 |
Approved |
no |
| Call Number |
Admin @ si @ MSM2020 |
Serial |
3452 |
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| Author |
Thomas B. Moeslund; Sergio Escalera; Gholamreza Anbarjafari; Kamal Nasrollahi; Jun Wan |
| Title |
Statistical Machine Learning for Human Behaviour Analysis |
Type |
Journal Article |
| Year |
2020 |
Publication |
Entropy |
Abbreviated Journal |
ENTROPY |
| Volume |
25 |
Issue |
5 |
Pages |
530 |
| Keywords |
action recognition; emotion recognition; privacy-aware |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MEA2020 |
Serial |
3441 |
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| Author |
Khalid El Asnaoui; Petia Radeva |
| Title |
Automatically Assess Day Similarity Using Visual Lifelogs |
Type |
Journal Article |
| Year |
2020 |
Publication |
International Journal of Intelligent Systems |
Abbreviated Journal |
IJIS |
| Volume |
29 |
Issue |
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Pages |
298–310 |
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| Abstract |
Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera and which produce a large number of images everyday. Automatically characterizing the experience and extracting patterns of behavior of individuals from this huge collection of unlabeled and unstructured egocentric data present major challenges and require novel and efficient algorithmic solutions. The main goal of this work is to propose a new method to automatically assess day similarity from the lifelogging images of a person. We propose a technique to measure the similarity between images based on the Swain’s distance and generalize it to detect the similarity between daily visual data. To this purpose, we apply the dynamic time warping (DTW) combined with the Swain’s distance for final day similarity estimation. For validation, we apply our technique on the Egocentric Dataset of University of Barcelona (EDUB) of 4912 daily images acquired by four persons with preliminary encouraging results. |
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| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
AsR2020 |
Serial |
3409 |
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| Author |
Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell |
| Title |
Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects |
Type |
Journal Article |
| Year |
2020 |
Publication |
Journal of the Optical Society of America A |
Abbreviated Journal |
JOSA A |
| Volume |
37 |
Issue |
1 |
Pages |
1-15 |
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| Abstract |
Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results. |
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| Notes |
CIC; 600.140; 600.12; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ SBV2019 |
Serial |
3311 |
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| Author |
Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe |
| Title |
Retrieval Guided Unsupervised Multi-domain Image to Image Translation |
Type |
Conference Article |
| Year |
2020 |
Publication |
28th ACM International Conference on Multimedia |
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Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data. |
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ACM |
| Notes |
DAG; 600.121 |
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no |
| Call Number |
Admin @ si @ GLN2020 |
Serial |
3497 |
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| Author |
Xiangyang Li; Luis Herranz; Shuqiang Jiang |
| Title |
Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition |
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Journal |
| Year |
2020 |
Publication |
ACM Transactions on Data Science |
Abbreviated Journal |
ACM |
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In recent years, convolutional neural networks (CNNs) have achieved impressive performance for various visual recognition scenarios. CNNs trained on large labeled datasets can not only obtain significant performance on most challenging benchmarks but also provide powerful representations, which can be used to a wide range of other tasks. However, the requirement of massive amounts of data to train deep neural networks is a major drawback of these models, as the data available is usually limited or imbalanced. Fine-tuning (FT) is an effective way to transfer knowledge learned in a source dataset to a target task. In this paper, we introduce and systematically investigate several factors that influence the performance of fine-tuning for visual recognition. These factors include parameters for the retraining procedure (e.g., the initial learning rate of fine-tuning), the distribution of the source and target data (e.g., the number of categories in the source dataset, the distance between the source and target datasets) and so on. We quantitatively and qualitatively analyze these factors, evaluate their influence, and present many empirical observations. The results reveal insights into what fine-tuning changes CNN parameters and provide useful and evidence-backed intuitions about how to implement fine-tuning for computer vision tasks. |
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| Notes |
LAMP; 600.141; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ LHJ2020 |
Serial |
3423 |
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| Author |
Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora |
| Title |
Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images |
Type |
Book Chapter |
| Year |
2020 |
Publication |
Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends |
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World Scientific |
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ISBN |
978-981-120-323-7 |
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| Notes |
DAG; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ FLP2020 |
Serial |
3350 |
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| Author |
David Berga; Xavier Otazu |
| Title |
Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1 |
Type |
Journal Article |
| Year |
2020 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
| Volume |
417 |
Issue |
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270-289 |
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Previous studies suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return, oculomotor and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks. Results show that our model outpeforms other biologically inspired models of saliency prediction while predicting visual saccade sequences with the same model. We also show how temporal and spatial characteristics of saccade amplitude and inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts. |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
NEUROBIT |
Approved |
no |
| Call Number |
Admin @ si @ BeO2020c |
Serial |
3444 |
| Permanent link to this record |
