| Records |
| Author |
Debora Gil; Guillermo Torres |
| Title |
A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
Type |
Conference Article |
| Year |
2020 |
Publication |
34th International Congress and Exhibition on Computer Assisted Radiology & Surgery |
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Virtual; June 2020 |
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CARS |
Notes  |
IAM; 600.139; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ GiT2020 |
Serial |
3472 |
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| Author |
Guillermo Torres; Debora Gil |
| Title |
A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
Type |
Journal Article |
| Year |
2020 |
Publication |
International Journal of Computer Assisted Radiology and Surgery |
Abbreviated Journal |
IJCAR |
| Volume |
15 |
Issue |
1 |
Pages |
S154-55 |
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IAM |
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no |
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Admin @ si @ ToG2020 |
Serial |
3590 |
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| Author |
Wenlong Deng; Yongli Mou; Takahiro Kashiwa; Sergio Escalera; Kohei Nagai; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger |
| Title |
Vision based Pixel-level Bridge Structural Damage Detection Using a Link ASPP Network |
Type |
Journal Article |
| Year |
2020 |
Publication |
Automation in Construction |
Abbreviated Journal |
AC |
| Volume |
110 |
Issue |
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Pages |
102973 |
| Keywords |
Semantic image segmentation; Deep learning |
| Abstract |
Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface structural damage detection, such as delamination and rebar exposure. It is well known that the quality of a deep learning model is highly dependent on the quality of the training dataset. Bridge damage detection, our application domain, has the following main challenges: (i) labeling the damages requires knowledgeable civil engineering professionals, which makes it difficult to collect a large annotated dataset; (ii) the damage area could be very small, whereas the background area is large, which creates an unbalanced training environment; (iii) due to the difficulty to exactly determine the extension of the damage, there is often a variation among different labelers who perform pixel-wise labeling. In this paper, we propose a novel model for bridge structural damage detection to address the first two challenges. This paper follows the idea of an atrous spatial pyramid pooling (ASPP) module that is designed as a novel network for bridge damage detection. Further, we introduce the weight balanced Intersection over Union (IoU) loss function to achieve accurate segmentation on a highly unbalanced small dataset. The experimental results show that (i) the IoU loss function improves the overall performance of damage detection, as compared to cross entropy loss or focal loss, and (ii) the proposed model has a better ability to detect a minority class than other light segmentation networks. |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ DMK2020 |
Serial |
3314 |
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
| Title |
Hand sign language recognition using multi-view hand skeleton |
Type |
Journal Article |
| Year |
2020 |
Publication |
Expert Systems With Applications |
Abbreviated Journal |
ESWA |
| Volume |
150 |
Issue |
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Pages |
113336 |
| Keywords |
Multi-view hand skeleton; Hand sign language recognition; 3DCNN; Hand pose estimation; RGB video; Hand action recognition |
| Abstract |
Hand sign language recognition from video is a challenging research area in computer vision, which performance is affected by hand occlusion, fast hand movement, illumination changes, or background complexity, just to mention a few. In recent years, deep learning approaches have achieved state-of-the-art results in the field, though previous challenges are not completely solved. In this work, we propose a novel deep learning-based pipeline architecture for efficient automatic hand sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from RGB input videos. We use a CNN-based model which estimates the 3D hand keypoints from 2D input frames. After that, we connect these estimated keypoints to build the hand skeleton by using midpoint algorithm. In order to obtain a more discriminative representation of hands, we project 3D hand skeleton into three views surface images. We further employ the heatmap image of detected keypoints as input for refinement in a stacked fashion. We apply 3DCNNs on the stacked features of hand, including pixel level, multi-view hand skeleton, and heatmap features, to extract discriminant local spatio-temporal features from these stacked inputs. The outputs of the 3DCNNs are fused and fed to a LSTM to model long-term dynamics of hand sign gestures. Analyzing 2DCNN vs. 3DCNN using different number of stacked inputs into the network, we demonstrate that 3DCNN better capture spatio-temporal dynamics of hands. To the best of our knowledge, this is the first time that this multi-modal and multi-view set of hand skeleton features are applied for hand sign language recognition. Furthermore, we present a new large-scale hand sign language dataset, namely RKS-PERSIANSIGN, including 10′000 RGB videos of 100 Persian sign words. Evaluation results of the proposed model on three datasets, NYU, First-Person, and RKS-PERSIANSIGN, indicate that our model outperforms state-of-the-art models in hand sign language recognition, hand pose estimation, and hand action recognition. |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ RKE2020a |
Serial |
3411 |
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| Author |
Shifeng Zhang; Ajian Liu; Jun Wan; Yanyan Liang; Guogong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li |
| Title |
CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing |
Type |
Journal |
| Year |
2020 |
Publication |
IEEE Transactions on Biometrics, Behavior, and Identity Science |
Abbreviated Journal |
TTBIS |
| Volume |
2 |
Issue |
2 |
Pages |
182 - 193 |
| Keywords |
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| Abstract |
Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities ( i.e. , RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2019?authuser=0 |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ ZLW2020 |
Serial |
3412 |
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| Author |
Zhengying Liu; Zhen Xu; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu |
| Title |
Towards automated computer vision: analysis of the AutoCV challenges 2019 |
Type |
Journal Article |
| Year |
2020 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
135 |
Issue |
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Pages |
196-203 |
| Keywords |
Computer vision; AutoML; Deep learning |
| Abstract |
We present the results of recent challenges in Automated Computer Vision (AutoCV, renamed here for clarity AutoCV1 and AutoCV2, 2019), which are part of a series of challenge on Automated Deep Learning (AutoDL). These two competitions aim at searching for fully automated solutions for classification tasks in computer vision, with an emphasis on any-time performance. The first competition was limited to image classification while the second one included both images and videos. Our design imposed to the participants to submit their code on a challenge platform for blind testing on five datasets, both for training and testing, without any human intervention whatsoever. Winning solutions adopted deep learning techniques based on already published architectures, such as AutoAugment, MobileNet and ResNet, to reach state-of-the-art performance in the time budget of the challenge (only 20 minutes of GPU time). The novel contributions include strategies to deliver good preliminary results at any time during the learning process, such that a method can be stopped early and still deliver good performance. This feature is key for the adoption of such techniques by data analysts desiring to obtain rapidly preliminary results on large datasets and to speed up the development process. The soundness of our design was verified in several aspects: (1) Little overfitting of the on-line leaderboard providing feedback on 5 development datasets was observed, compared to the final blind testing on the 5 (separate) final test datasets, suggesting that winning solutions might generalize to other computer vision classification tasks; (2) Error bars on the winners’ performance allow us to say with confident that they performed significantly better than the baseline solutions we provided; (3) The ranking of participants according to the any-time metric we designed, namely the Area under the Learning Curve, was different from that of the fixed-time metric, i.e. AUC at the end of the fixed time budget. We released all winning solutions under open-source licenses. At the end of the AutoDL challenge series, all data of the challenge will be made publicly available, thus providing a collection of uniformly formatted datasets, which can serve to conduct further research, particularly on meta-learning. |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LXE2020 |
Serial |
3427 |
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| Author |
Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez |
| Title |
Computing the Testing Error Without a Testing Set |
Type |
Conference Article |
| Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
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Issue |
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| Abstract |
Oral. Paper award nominee.
Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trailand-error. That is, typically, researchers will derive many DNN architectures (i.e., topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-thetesting-data is almost impossible. Using a sequestered testing dataset may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset. Specifically, we derive a number of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. This allows us to compute the DNN’s testing error on unseen samples, even when we do not have access to them. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach. |
| Address |
Virtual CVPR |
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Conference |
CVPR |
| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ CEM2020 |
Serial |
3437 |
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| Author |
Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz |
| Title |
Gate-Shift Networks for Video Action Recognition |
Type |
Conference Article |
| Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
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Deep 3D CNNs for video action recognition are designed to learn powerful representations in the joint spatio-temporal feature space. In practice however, because of the large number of parameters and computations involved, they may under-perform in the lack of sufficiently large datasets for training them at scale. In this paper we introduce spatial gating in spatial-temporal decomposition of 3D kernels. We implement this concept with Gate-Shift Module (GSM). GSM is lightweight and turns a 2D-CNN into a highly efficient spatio-temporal feature extractor. With GSM plugged in, a 2D-CNN learns to adaptively route features through time and combine them, at almost no additional parameters and computational overhead. We perform an extensive evaluation of the proposed module to study its effectiveness in video action recognition, achieving state-of-the-art results on Something Something-V1 and Diving48 datasets, and obtaining competitive results on EPIC-Kitchens with far less model complexity. |
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Virtual CVPR |
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CVPR |
| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ SEL2020 |
Serial |
3438 |
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| Author |
Meysam Madadi; Hugo Bertiche; Sergio Escalera |
| Title |
SMPLR: Deep learning based SMPL reverse for 3D human pose and shape recovery |
Type |
Journal Article |
| Year |
2020 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
106 |
Issue |
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Pages |
107472 |
| Keywords |
Deep learning; 3D Human pose; Body shape; SMPL; Denoising autoencoder; Volumetric stack hourglass |
| Abstract |
In this paper we propose to embed SMPL within a deep-based model to accurately estimate 3D pose and shape from a still RGB image. We use CNN-based 3D joint predictions as an intermediate representation to regress SMPL pose and shape parameters. Later, 3D joints are reconstructed again in the SMPL output. This module can be seen as an autoencoder where the encoder is a deep neural network and the decoder is SMPL model. We refer to this as SMPL reverse (SMPLR). By implementing SMPLR as an encoder-decoder we avoid the need of complex constraints on pose and shape. Furthermore, given that in-the-wild datasets usually lack accurate 3D annotations, it is desirable to lift 2D joints to 3D without pairing 3D annotations with RGB images. Therefore, we also propose a denoising autoencoder (DAE) module between CNN and SMPLR, able to lift 2D joints to 3D and partially recover from structured error. We evaluate our method on SURREAL and Human3.6M datasets, showing improvement over SMPL-based state-of-the-art alternatives by about 4 and 12 mm, respectively. |
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HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MBE2020 |
Serial |
3439 |
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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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HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ MEA2020 |
Serial |
3441 |
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| Author |
Ajian Liu; Xuan Li; Jun Wan; Yanyan Liang; Sergio Escalera; Hugo Jair Escalante; Meysam Madadi; Yi Jin; Zhuoyuan Wu; Xiaogang Yu; Zichang Tan; Qi Yuan; Ruikun Yang; Benjia Zhou; Guodong Guo; Stan Z. Li |
| Title |
Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review |
Type |
Journal Article |
| Year |
2020 |
Publication |
IET Biometrics |
Abbreviated Journal |
BIO |
| Volume |
10 |
Issue |
1 |
Pages |
24-43 |
| Keywords |
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| Abstract |
Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1,607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face anti-spoofing. We organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. |
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HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ LLW2020b |
Serial |
3523 |
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| Author |
Sergio Escalera; Ralf Herbrich |
| Title |
The NeurIPS’18 Competition: From Machine Learning to Intelligent Conversations |
Type |
Book Whole |
| Year |
2020 |
Publication |
The Springer Series on Challenges in Machine Learning |
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Issue |
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Pages |
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| Abstract |
This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility. |
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Sergio Escalera; Ralf Hebrick |
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2520-1328 |
ISBN |
978-3-030-29134-1 |
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HuPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ HeE2020 |
Serial |
3328 |
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| Author |
Yunan Li; Jun Wan; Qiguang Miao; Sergio Escalera; Huijuan Fang; Huizhou Chen; Xiangda Qi; Guodong Guo |
| Title |
CR-Net: A Deep Classification-Regression Network for Multimodal Apparent Personality Analysis |
Type |
Journal Article |
| Year |
2020 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
| Volume |
128 |
Issue |
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Pages |
2763–2780 |
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| Abstract |
First impressions strongly influence social interactions, having a high impact in the personal and professional life. In this paper, we present a deep Classification-Regression Network (CR-Net) for analyzing the Big Five personality problem and further assisting on job interview recommendation in a first impressions setup. The setup is based on the ChaLearn First Impressions dataset, including multimodal data with video, audio, and text converted from the corresponding audio data, where each person is talking in front of a camera. In order to give a comprehensive prediction, we analyze the videos from both the entire scene (including the person’s motions and background) and the face of the person. Our CR-Net first performs personality trait classification and applies a regression later, which can obtain accurate predictions for both personality traits and interview recommendation. Furthermore, we present a new loss function called Bell Loss to address inaccurate predictions caused by the regression-to-the-mean problem. Extensive experiments on the First Impressions dataset show the effectiveness of our proposed network, outperforming the state-of-the-art. |
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HuPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ LWM2020 |
Serial |
3413 |
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
| Title |
Video-based Isolated Hand Sign Language Recognition Using a Deep Cascaded Model |
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Journal Article |
| Year |
2020 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
79 |
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22965–22987 |
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| Abstract |
In this paper, we propose an efficient cascaded model for sign language recognition taking benefit from spatio-temporal hand-based information using deep learning approaches, especially Single Shot Detector (SSD), Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM), from videos. Our simple yet efficient and accurate model includes two main parts: hand detection and sign recognition. Three types of spatial features, including hand features, Extra Spatial Hand Relation (ESHR) features, and Hand Pose (HP) features, have been fused in the model to feed to LSTM for temporal features extraction. We train SSD model for hand detection using some videos collected from five online sign dictionaries. Our model is evaluated on our proposed dataset (Rastgoo et al., Expert Syst Appl 150: 113336, 2020), including 10’000 sign videos for 100 Persian sign using 10 contributors in 10 different backgrounds, and isoGD dataset. Using the 5-fold cross-validation method, our model outperforms state-of-the-art alternatives in sign language recognition |
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HuPBA; no menciona |
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Admin @ si @ RKE2020b |
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3442 |
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
| Title |
Hand pose aware multimodal isolated sign language recognition |
Type |
Journal Article |
| Year |
2020 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
80 |
Issue |
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127–163 |
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| Abstract |
Isolated hand sign language recognition from video is a challenging research area in computer vision. Some of the most important challenges in this area include dealing with hand occlusion, fast hand movement, illumination changes, or background complexity. While most of the state-of-the-art results in the field have been achieved using deep learning-based models, the previous challenges are not completely solved. In this paper, we propose a hand pose aware model for isolated hand sign language recognition using deep learning approaches from two input modalities, RGB and depth videos. Four spatial feature types: pixel-level, flow, deep hand, and hand pose features, fused from both visual modalities, are input to LSTM for temporal sign recognition. While we use Optical Flow (OF) for flow information in RGB video inputs, Scene Flow (SF) is used for depth video inputs. By including hand pose features, we show a consistent performance improvement of the sign language recognition model. To the best of our knowledge, this is the first time that this discriminant spatiotemporal features, benefiting from the hand pose estimation features and multi-modal inputs, are fused for isolated hand sign language recognition. We perform a step-by-step analysis of the impact in terms of recognition performance of the hand pose features, different combinations of the spatial features, and different recurrent models, especially LSTM and GRU. Results on four public datasets confirm that the proposed model outperforms the current state-of-the-art models on Montalbano II, MSR Daily Activity 3D, and CAD-60 datasets with a relative accuracy improvement of 1.64%, 6.5%, and 7.6%. Furthermore, our model obtains a competitive results on isoGD dataset with only 0.22% margin lower than the current state-of-the-art model. |
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HUPBA; no menciona |
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Admin @ si @ RKE2020 |
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3524 |
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