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Author |
Eirikur Agustsson; Radu Timofte; Sergio Escalera; Xavier Baro; Isabelle Guyon; Rasmus Rothe |
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Title |
Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database |
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Conference Article |
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Year |
2017 |
Publication |
12th IEEE International Conference on Automatic Face and Gesture Recognition |
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After decades of research, the real (biological) age estimation from a single face image reached maturity thanks to the availability of large public face databases and impressive accuracies achieved by recently proposed methods.
The estimation of “apparent age” is a related task concerning the age perceived by human observers. Significant advances have been also made in this new research direction with the recent Looking At People challenges. In this paper we make several contributions to age estimation research. (i) We introduce APPA-REAL, a large face image database with both real and apparent age annotations. (ii) We study the relationship between real and apparent age. (iii) We develop a residual age regression method to further improve the performance. (iv) We show that real age estimation can be successfully tackled as an apparent age estimation followed by an apparent to real age residual regression. (v) We graphically reveal the facial regions on which the CNN focuses in order to perform apparent and real age estimation tasks. |
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Washington;USA; May 2017 |
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HUPBA; no menciona |
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no |
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Admin @ si @ ATE2017 |
Serial |
3013 |
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Author |
Alejandro Ariza-Casabona; Bartlomiej Twardowski; Tri Kurniawan Wijaya |
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Title |
Exploiting Graph Structured Cross-Domain Representation for Multi-domain Recommendation |
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Conference Article |
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Year |
2023 |
Publication |
European Conference on Information Retrieval – ECIR 2023: Advances in Information Retrieval |
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Volume |
13980 |
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49–65 |
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Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At the same time, treating domains as separate input sources becomes a limitation as it does not capture the interplay that naturally exists between domains. In this work, we efficiently learn multi-domain representation of sequential users’ interactions using graph neural networks. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec (short for Multi-dom Ain Graph-based Recommender). To better capture all relations in a multi-domain setting, we learn two graph-based sequential representations simultaneously: domain-guided for recent user interest, and general for long-term interest. This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods. Furthermore, we provide an ablation study and discuss further extensions of our method. |
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ECIR |
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LAMP |
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no |
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Admin @ si @ ATK2023 |
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3933 |
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Author |
David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras |
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Title |
Efficient Object Pixel-Level Categorization using Bag of Features: Advances in Visual Computing |
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Conference Article |
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Year |
2009 |
Publication |
5th International Symposium on Visual Computing |
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5875 |
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44–55 |
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In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost. |
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Las Vegas, USA |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-10330-8 |
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ISVC |
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ADAS |
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no |
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Call Number |
Admin @ si @ ATR2009a |
Serial |
1246 |
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Author |
David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras |
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Title |
Visual Registration Method For A Low Cost Robot: Computer Vision Systems |
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Conference Article |
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Year |
2009 |
Publication |
7th International Conference on Computer Vision Systems |
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Volume |
5815 |
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204–214 |
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An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most methods estimate the transformation relating the maps from matches established between low level feature extracted from sensor data. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by the Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated in a typical office environment showing good performance. |
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Belgica |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04666-7 |
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ICVS |
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ADAS |
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no |
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Admin @ si @ ATR2009b |
Serial |
1247 |
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Author |
Jon Almazan; Ernest Valveny; Alicia Fornes |
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Title |
Deforming the Blurred Shape Model for Shape Description and Recognition |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
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Volume |
6669 |
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1-8 |
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This paper presents a new model for the description and recognition of distorted shapes, where the image is represented by a pixel density distribution based on the Blurred Shape Model combined with a non-linear image deformation model. This leads to an adaptive structure able to capture elastic deformations in shapes. This method has been evaluated using thee different datasets where deformations are present, showing the robustness and good performance of the new model. Moreover, we show that incorporating deformation and flexibility, the new model outperforms the BSM approach when classifying shapes with high variability of appearance. |
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Las Palmas de Gran Canaria. Spain |
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Springer-Verlag |
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Berlin |
Editor |
Jordi Vitria; Joao Miguel Raposo; Mario Hernandez |
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IbPRIA |
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DAG; |
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no |
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Call Number |
Admin @ si @ AVF2011 |
Serial |
1732 |
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Author |
Parichehr Behjati Ardakani; Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Jordi Gonzalez |
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Title |
Catastrophic interference in Disguised Face Recognition |
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Conference Article |
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2019 |
Publication |
9th Iberian Conference on Pattern Recognition and Image Analysis |
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11868 |
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64-75 |
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Neural network forgetness; Face recognition; Disguised Faces |
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It is commonly known the natural tendency of artificial neural networks to completely and abruptly forget previously known information when learning new information. We explore this behaviour in the context of Face Verification on the recently proposed Disguised Faces in the Wild dataset (DFW). We empirically evaluate several commonly used DCNN architectures on Face Recognition and distill some insights about the effect of sequential learning on distinct identities from different datasets, showing that the catastrophic forgetness phenomenon is present even in feature embeddings fine-tuned on different tasks from the original domain. |
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IbPRIA |
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ISE; 600.098; 600.119 |
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no |
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Admin @ si @ AVG2019 |
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3416 |
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Author |
Juan Andrade; T. Alejandra Vidal; A. Sanfeliu |
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Title |
Stochastic state estimation for simultaneous localization and map building in mobile robotics |
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Book Chapter |
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2005 |
Publication |
Vedran Kordic, Aleksandar Lazinica, and Munir Merdan (Eds.), Cutting Edge Robotics, Advanced Robotic Systems Press, 3.3:223–242 |
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no |
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Admin @ si @ AVS2005a |
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565 |
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Author |
Juan Andrade; T. Alejandra Vidal; A. Sanfeliu |
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Title |
Multirobot C-SLAM: Simultaneous localization, control, and mapping |
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Miscellaneous |
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2005 |
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in Proc. IEEE ICRA05 Workshop on Network Robot Systems |
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Barcelona (Spain) |
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no |
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Admin @ si @ AVS2005b |
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549 |
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Author |
Juan Andrade; T. Alejandra Vidal; A. Sanfeliu |
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Title |
Unscented transformation of vehicle states in SLAM |
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Miscellaneous |
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2005 |
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Proceedings of the IEEE International Conference on Robotics and Automation, 324–329 |
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Barcelona (Spain) |
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no |
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Admin @ si @ AVS2005c |
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591 |
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Author |
Aymen Azaza; Joost Van de Weijer; Ali Douik; Marc Masana |
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Title |
Context Proposals for Saliency Detection |
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Journal Article |
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Year |
2018 |
Publication |
Computer Vision and Image Understanding |
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CVIU |
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174 |
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1-11 |
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Abstract |
One of the fundamental properties of a salient object region is its contrast
with the immediate context. The problem is that numerous object regions
exist which potentially can all be salient. One way to prevent an exhaustive
search over all object regions is by using object proposal algorithms. These
return a limited set of regions which are most likely to contain an object. Several saliency estimation methods have used object proposals. However, they focus on the saliency of the proposal only, and the importance of its immediate context has not been evaluated.
In this paper, we aim to improve salient object detection. Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation. We propose several saliency features which are computed from the context proposals. In the experiments, we evaluate five object proposal methods for the task of saliency segmentation, and find that Multiscale Combinatorial Grouping outperforms the others. Furthermore, experiments show that the proposed context features improve performance, and that our method matches results on the FT datasets and obtains competitive results on three other datasets (PASCAL-S, MSRA-B and ECSSD). |
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LAMP; 600.109; 600.109; 600.120 |
Approved |
no |
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Call Number |
Admin @ si @ AWD2018 |
Serial |
3241 |
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Author |
Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana |
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Title |
Saliency from High-Level Semantic Image Features |
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2020 |
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SN Computer Science |
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SN |
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1 |
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4 |
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1-12 |
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Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS). |
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LAMP; 600.120; 600.109; 600.106 |
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no |
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Admin @ si @ AWD2020 |
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3503 |
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Author |
Aitor Alvarez-Gila; Joost Van de Weijer; Estibaliz Garrote |
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Title |
Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB |
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Conference Article |
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2017 |
Publication |
1st International Workshop on Physics Based Vision meets Deep Learning |
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Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer.
Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However,
most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44:7% and a Relative RMSE drop of 47:0% on the ICVL natural hyperspectral image dataset. |
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Venice; Italy; October 2017 |
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ICCV-PBDL |
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LAMP; 600.109; 600.106; 600.120 |
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no |
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Admin @ si @ AWG2017 |
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2969 |
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Author |
Aitor Alvarez-Gila; Joost Van de Weijer; Yaxing Wang; Estibaliz Garrote |
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Title |
MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation |
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Conference Article |
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2022 |
Publication |
29th IEEE International Conference on Image Processing |
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multi-view; cross-view; semantic segmentation; synthetic dataset |
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We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view datasets, MVMO features wide baselines between cameras and high density of objects, which lead to large disparities, heavy occlusions and view-dependent object appearance. Single view semantic segmentation is hindered by self and inter-object occlusions that could benefit from additional viewpoints. Therefore, we expect that MVMO will propel research in multi-view semantic segmentation and cross-view semantic transfer. We also provide baselines that show that new research is needed in such fields to exploit the complementary information of multi-view setups 1 . |
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Bordeaux; France; October2022 |
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ICIP |
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LAMP |
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no |
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Admin @ si @ AWW2022 |
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3781 |
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Author |
Aymen Azaza |
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Title |
Context, Motion and Semantic Information for Computational Saliency |
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Book Whole |
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2018 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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The main objective of this thesis is to highlight the salient object in an image or in a video sequence. We address three important—but in our opinion
insufficiently investigated—aspects of saliency detection. Firstly, we start
by extending previous research on saliency which explicitly models the information provided from the context. Then, we show the importance of
explicit context modelling for saliency estimation. Several important works
in saliency are based on the usage of object proposals. However, these methods
focus on the saliency of the object proposal itself and ignore the context.
To introduce context in such saliency approaches, we couple every object
proposal with its direct context. This allows us to evaluate the importance
of the immediate surround (context) for its saliency. We propose several
saliency features which are computed from the context proposals including
features based on omni-directional and horizontal context continuity. Secondly,
we investigate the usage of top-downmethods (high-level semantic
information) for the task of saliency prediction since most computational
methods are bottom-up or only include few semantic classes. We propose
to consider a wider group of object classes. These objects represent important
semantic information which we will exploit in our saliency prediction
approach. Thirdly, we develop a method to detect video saliency by computing
saliency from supervoxels and optical flow. In addition, we apply the
context features developed in this thesis for video saliency detection. The
method combines shape and motion features with our proposed context
features. To summarize, we prove that extending object proposals with their
direct context improves the task of saliency detection in both image and
video data. Also the importance of the semantic information in saliency
estimation is evaluated. Finally, we propose a newmotion feature to detect
saliency in video data. The three proposed novelties are evaluated on standard
saliency benchmark datasets and are shown to improve with respect to
state-of-the-art. |
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Address |
October 2018 |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Joost Van de Weijer;Ali Douik |
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978-84-945373-9-4 |
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Notes |
LAMP; 600.120 |
Approved |
no |
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Call Number |
Admin @ si @ Aza2018 |
Serial |
3218 |
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Author |
Lluis Barcelo; X. Binefa |
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Title |
Bayesian Video Mosaicing with Moving Objects. |
Type |
Miscellaneous |
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Year |
2001 |
Publication |
Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:91–96. |
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no |
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Call Number |
Admin @ si @ BaB2001 |
Serial |
72 |
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