| Records |
| Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz |
| Title |
Combining Models from Multiple Sources for RGB-D Scene Recognition |
Type |
Conference Article |
| Year |
2017 |
Publication |
26th International Joint Conference on Artificial Intelligence |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
4523-4529 |
| Keywords |
Robotics and Vision; Vision and Perception |
| Abstract |
Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at high-levels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities. |
| Address |
Melbourne; Australia; August 2017 |
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Conference |
IJCAI |
| Notes |
LAMP; 600.120 |
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no |
Call Number  |
Admin @ si @ SJH2017b |
Serial |
2966 |
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| Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz; Chengpeng Chen |
| Title |
Learning Effective RGB-D Representations for Scene Recognition |
Type |
Journal Article |
| Year |
2019 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
| Volume |
28 |
Issue |
2 |
Pages |
980-993 |
| Keywords |
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| Abstract |
Deep convolutional networks can achieve impressive results on RGB scene recognition thanks to large data sets such as places. In contrast, RGB-D scene recognition is still underdeveloped in comparison, due to two limitations of RGB-D data we address in this paper. The first limitation is the lack of depth data for training deep learning models. Rather than fine tuning or transferring RGB-specific features, we address this limitation by proposing an architecture and a two-step training approach that directly learns effective depth-specific features using weak supervision via patches. The resulting RGB-D model also benefits from more complementary multimodal features. Another limitation is the short range of depth sensors (typically 0.5 m to 5.5 m), resulting in depth images not capturing distant objects in the scenes that RGB images can. We show that this limitation can be addressed by using RGB-D videos, where more comprehensive depth information is accumulated as the camera travels across the scenes. Focusing on this scenario, we introduce the ISIA RGB-D video data set to evaluate RGB-D scene recognition with videos. Our video recognition architecture combines convolutional and recurrent neural networks that are trained in three steps with increasingly complex data to learn effective features (i.e., patches, frames, and sequences). Our approach obtains the state-of-the-art performances on RGB-D image (NYUD2 and SUN RGB-D) and video (ISIA RGB-D) scene recognition. |
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LAMP; 600.141; 600.120 |
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no |
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Admin @ si @ SJH2019 |
Serial |
3247 |
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| Author |
Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
| Title |
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network |
Type |
Conference Article |
| Year |
2018 |
Publication |
21st International Conference of the Catalan Association for Artificial Intelligence |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
365-372 |
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| Abstract |
Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. |
| Address |
Roses; catalonia; October 2018 |
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CCIA |
| Notes |
MILAB; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ SJR2018 |
Serial |
3113 |
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| Author |
Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes |
| Title |
A conditional GAN based approach for distorted camera captured documents recovery |
Type |
Conference Article |
| Year |
2020 |
Publication |
4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence |
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| Address |
Virtual; December 2020 |
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Conference |
MedPRAI |
| Notes |
DAG; 600.121 |
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no |
| Call Number |
Admin @ si @ SKF2020 |
Serial |
3450 |
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| Author |
Ariel Amato; Angel Sappa; Alicia Fornes; Felipe Lumbreras; Josep Llados |
| Title |
Divide and Conquer: Atomizing and Parallelizing A Task in A Mobile Crowdsourcing Platform |
Type |
Conference Article |
| Year |
2013 |
Publication |
2nd International ACM Workshop on Crowdsourcing for Multimedia |
Abbreviated Journal |
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Issue |
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Pages |
21-22 |
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In this paper we present some conclusions about the advantages of having an efficient task formulation when a crowdsourcing platform is used. In particular we show how the task atomization and distribution can help to obtain results in an efficient way. Our proposal is based on a recursive splitting of the original task into a set of smaller and simpler tasks. As a result both more accurate and faster solutions are obtained. Our evaluation is performed on a set of ancient documents that need to be digitized. |
| Address |
Barcelona; October 2013 |
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ISBN |
978-1-4503-2396-3 |
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Conference |
CrowdMM |
| Notes |
ADAS; ISE; DAG; 600.054; 600.055; 600.045; 600.061; 602.006 |
Approved |
no |
| Call Number |
Admin @ si @ SLA2013 |
Serial |
2335 |
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| Author |
Joan Serrat; Felipe Lumbreras; Francisco Blanco; Manuel Valiente; Montserrat Lopez-Mesas |
| Title |
myStone: A system for automatic kidney stone classification |
Type |
Journal Article |
| Year |
2017 |
Publication |
Expert Systems with Applications |
Abbreviated Journal |
ESA |
| Volume |
89 |
Issue |
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Pages |
41-51 |
| Keywords |
Kidney stone; Optical device; Computer vision; Image classification |
| Abstract |
Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size. |
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| Notes |
ADAS; MSIAU; 603.046; 600.122; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ SLB2017 |
Serial |
3026 |
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| Author |
Damian Sojka; Yuyang Liu; Dipam Goswami; Sebastian Cygert; Bartłomiej Twardowski; Joost van de Weijer |
| Title |
Technical Report for ICCV 2023 Visual Continual Learning Challenge: Continuous Test-time Adaptation for Semantic Segmentation |
Type |
Miscellaneous |
| Year |
2023 |
Publication |
Arxiv |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
The goal of the challenge is to develop a test-time adaptation (TTA) method, which could adapt the model to gradually changing domains in video sequences for semantic segmentation task. It is based on a synthetic driving video dataset – SHIFT. The source model is trained on images taken during daytime in clear weather. Domain changes at test-time are mainly caused by varying weather conditions and times of day. The TTA methods are evaluated in each image sequence (video) separately, meaning the model is reset to the source model state before the next sequence. Images come one by one and a prediction has to be made at the arrival of each frame. Each sequence is composed of 401 images and starts with the source domain, then gradually drifts to a different one (changing weather or time of day) until the middle of the sequence. In the second half of the sequence, the domain gradually shifts back to the source one. Ground truth data is available only for the validation split of the SHIFT dataset, in which there are only six sequences that start and end with the source domain. We conduct an analysis specifically on those sequences. Ground truth data for test split, on which the developed TTA methods are evaluated for leader board ranking, are not publicly available.
The proposed solution secured a 3rd place in a challenge and received an innovation award. Contrary to the solutions that scored better, we did not use any external pretrained models or specialized data augmentations, to keep the solutions as general as possible. We have focused on analyzing the distributional shift and developing a method that could adapt to changing data dynamics and generalize across different scenarios. |
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LAMP |
Approved |
no |
| Call Number |
Admin @ si @ SLG2023 |
Serial |
3993 |
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| Author |
Joan Serrat; Felipe Lumbreras; Antonio Lopez |
| Title |
Cost estimation of custom hoses from STL files and CAD drawings |
Type |
Journal Article |
| Year |
2013 |
Publication |
Computers in Industry |
Abbreviated Journal |
COMPUTIND |
| Volume |
64 |
Issue |
3 |
Pages |
299-309 |
| Keywords |
On-line quotation; STL format; Regression; Gaussian process |
| Abstract |
We present a method for the cost estimation of custom hoses from CAD models. They can come in two formats, which are easy to generate: a STL file or the image of a CAD drawing showing several orthogonal projections. The challenges in either cases are, first, to obtain from them a high level 3D description of the shape, and second, to learn a regression function for the prediction of the manufacturing time, based on geometric features of the reconstructed shape. The chosen description is the 3D line along the medial axis of the tube and the diameter of the circular sections along it. In order to extract it from STL files, we have adapted RANSAC, a robust parametric fitting algorithm. As for CAD drawing images, we propose a new technique for 3D reconstruction from data entered on any number of orthogonal projections. The regression function is a Gaussian process, which does not constrain the function to adopt any specific form and is governed by just two parameters. We assess the accuracy of the manufacturing time estimation by k-fold cross validation on 171 STL file models for which the time is provided by an expert. The results show the feasibility of the method, whereby the relative error for 80% of the testing samples is below 15%. |
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Elsevier |
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ADAS; 600.057; 600.054; 605.203 |
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no |
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Admin @ si @ SLL2013; ADAS @ adas @ |
Serial |
2161 |
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| Author |
Hans Stadthagen-Gonzalez; Luis Lopez; M. Carmen Parafita; C. Alejandro Parraga |
| Title |
Using two-alternative forced choice tasks and Thurstone law of comparative judgments for code-switching research |
Type |
Book Chapter |
| Year |
2018 |
Publication |
Linguistic Approaches to Bilingualism |
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Issue |
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Pages |
67-97 |
| Keywords |
two-alternative forced choice and Thurstone's law; acceptability judgment; code-switching |
| Abstract |
This article argues that 2-alternative forced choice tasks and Thurstone’s law of comparative judgments (Thurstone, 1927) are well suited to investigate code-switching competence by means of acceptability judgments. We compare this method with commonly used Likert scale judgments and find that the 2-alternative forced choice task provides granular details that remain invisible in a Likert scale experiment. In order to compare and contrast both methods, we examined the syntactic phenomenon usually referred to as the Adjacency Condition (AC) (apud Stowell, 1981), which imposes a condition of adjacency between verb and object. Our interest in the AC comes from the fact that it is a subtle feature of English grammar which is absent in Spanish, and this provides an excellent springboard to create minimal code-switched pairs that allow us to formulate a clear research question that can be tested using both methods. |
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NEUROBIT; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ SLP2018 |
Serial |
2994 |
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| Author |
Joan Serrat; Felipe Lumbreras; Idoia Ruiz |
| Title |
Learning to measure for preshipment garment sizing |
Type |
Journal Article |
| Year |
2018 |
Publication |
Measurement |
Abbreviated Journal |
MEASURE |
| Volume |
130 |
Issue |
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Pages |
327-339 |
| Keywords |
Apparel; Computer vision; Structured prediction; Regression |
| Abstract |
Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively. |
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ADAS; MSIAU; 600.122; 600.118 |
Approved |
no |
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Admin @ si @ SLR2018 |
Serial |
3128 |
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| Author |
Xavier Soria; Yachuan Li; Mohammad Rouhani; Angel Sappa |
| Title |
Tiny and Efficient Model for the Edge Detection Generalization |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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Most high-level computer vision tasks rely on low-level image operations as their initial processes. Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis. In this work we address the edge detection considering three main objectives: simplicity, efficiency, and generalization since current state-of-the-art (SOTA) edge detection models are increased in complexity for better accuracy. To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light convolutional neural network with only 58K parameters, less than 0:2% of the state-of-the-art models. Training on the BIPED dataset takes less than 30 minutes, with each epoch requiring less than 5 minutes. Our proposed model is easy to train and it quickly converges within very first few epochs, while the predicted edge-maps are crisp and of high quality. Additionally, we propose a new dataset to test the generalization of edge detection, which comprises samples from popular images used in edge detection and image segmentation. The source code is available in https://github.com/xavysp/TEED. |
| Address |
Paris; France; October 2023 |
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ICCVW |
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MSIAU |
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no |
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Admin @ si @ SLR2023 |
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3941 |
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Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich |
| Title |
DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition |
Type |
Conference Article |
| Year |
2015 |
Publication |
Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II |
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9475 |
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463-473 |
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Projector-camera systems; Feature descriptors; Object recognition |
| Abstract |
Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection. |
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Springer International Publishing |
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LNCS |
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0302-9743 |
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978-3-319-27862-9 |
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ISVC |
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CIC |
Approved |
no |
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Admin @ si @ SMG2015 |
Serial |
2736 |
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| Author |
D. Smith |
| Title |
Solving the mean string problem for 2D shapes |
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Report |
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1999 |
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CVC Technical Report #36 |
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CVC (UAB) |
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no |
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Admin @ si @ Smi1999 |
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195 |
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David Sanchez-Mendoza; David Masip; Agata Lapedriza |
| Title |
Emotion recognition from mid-level features |
Type |
Journal Article |
| Year |
2015 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
67 |
Issue |
Part 1 |
Pages |
66–74 |
| Keywords |
Facial expression; Emotion recognition; Action units; Computer vision |
| Abstract |
In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception. |
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Elsevier B.V. |
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0167-8655 |
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OR;MV |
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no |
| Call Number |
Admin @ si @ SML2015 |
Serial |
2746 |
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| Author |
Daniel Sanchez; Meysam Madadi; Marc Oliu; Sergio Escalera |
| Title |
Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation |
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Conference Article |
| Year |
2019 |
Publication |
14th IEEE International Conference on Automatic Face and Gesture Recognition |
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| Abstract |
While many individual tasks in the domain of human analysis have recently received an accuracy boost from deep learning approaches, multi-task learning has mostly been ignored due to a lack of data. New synthetic datasets are being released, filling this gap with synthetic generated data. In this work, we analyze four related human analysis tasks in still images in a multi-task scenario by leveraging such datasets. Specifically, we study the correlation of 2D/3D pose estimation, body part segmentation and full-body depth estimation. These tasks are learned via the well-known Stacked Hourglass module such that each of the task-specific streams shares information with the others. The main goal is to analyze how training together these four related tasks can benefit each individual task for a better generalization. Results on the newly released SURREAL dataset show that all four tasks benefit from the multi-task approach, but with different combinations of tasks: while combining all four tasks improves 2D pose estimation the most, 2D pose improves neither 3D pose nor full-body depth estimation. On the other hand 2D parts segmentation can benefit from 2D pose but not from 3D pose. In all cases, as expected, the maximum improvement is achieved on those human body parts that show more variability in terms of spatial distribution, appearance and shape, e.g. wrists and ankles. |
| Address |
Lille; France; May 2019 |
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FG |
| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ SMO2019 |
Serial |
3326 |
| Permanent link to this record |
