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Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati Ardakani; Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Thermal Image Super-Resolution Challenge – PBVS 2020 |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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2020 |
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16h IEEE Workshop on Perception Beyond the Visible Spectrum |
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This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with state-of-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, mid-resolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 super-resolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered high-resolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase. |
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MSIAU; ISE; 600.119; 600.122 |
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Admin @ si @ RSV2020 |
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3431 |
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Author |
Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2020 |
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15th International Conference on Computer Vision Theory and Applications |
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This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The transfer learning consists of first training the network using pairs of images from the virtual-world scenario
considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight
of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose estimation accuracy using the proposed model, as well as further improvements when the transfer learning strategy (synthetic-world data transfer learning real-world data) is considered to tackle the limitation on the
training due to the reduced number of pairs of real-images on most of the public data sets. |
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Valletta; Malta; February 2020 |
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VISAPP |
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MSIAU; 600.130; 601.349; 600.122 |
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Admin @ si @ CSV2020 |
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3433 |
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Author |
Xavier Soria; Edgar Riba; Angel Sappa |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2020 |
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IEEE Winter Conference on Applications of Computer Vision |
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This paper proposes a Deep Learning based edge detector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed approach generates thin edge-maps that are plausible for human eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contribution, a large dataset with carefully annotated edges has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing improvements with the proposed method when F-measure of ODS and OIS are considered. |
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Aspen; USA; March 2020 |
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WACV |
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MSIAU; 600.130; 601.349; 600.122 |
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Admin @ si @ SRS2020 |
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3434 |
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Author |
Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Computing the Testing Error Without a Testing Set |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2020 |
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33rd IEEE Conference on Computer Vision and Pattern Recognition |
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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. |
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Virtual CVPR |
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HuPBA; no proj |
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Admin @ si @ CEM2020 |
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3437 |
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Author |
Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Gate-Shift Networks for Video Action Recognition |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2020 |
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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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HuPBA; no proj |
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no |
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Admin @ si @ SEL2020 |
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3438 |
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Author |
Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Uncertainty Modeling and Deep Learning Applied to Food Image Analysis |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2020 |
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13th International Joint Conference on Biomedical Engineering Systems and Technologies |
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Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis. |
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Villetta; Malta; February 2020 |
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BIODEVICES |
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MILAB |
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no |
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Admin @ si @ ANK2020 |
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3526 |
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Author |
Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes |
![download PDF file pdf](img/file_PDF.gif)
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A conditional GAN based approach for distorted camera captured documents recovery |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2020 |
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4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence |
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Virtual; December 2020 |
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MedPRAI |
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DAG; 600.121 |
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Admin @ si @ SKF2020 |
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3450 |
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Author |
Fernando Vilariño |
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Title |
Unveiling the Social Impact of AI |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2020 |
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Workshop at Digital Living Lab Days Conference |
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September 2020 |
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MV; DAG; 600.121; 600.140;SIAI |
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Admin @ si @ Vil2020 |
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3459 |
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Author |
Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell; Dimitris Samaras |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Light Direction and Color Estimation from Single Image with Deep Regression |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2020 |
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London Imaging Conference |
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We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source. Apart from showing good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves good performance when it is applied to real scenes. |
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Virtual; September 2020 |
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LIM |
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CIC; 600.118; 600.140; |
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Admin @ si @ SBV2020 |
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3460 |
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Sagnik Das; Hassan Ahmed Sial; Ke Ma; Ramon Baldrich; Maria Vanrell; Dimitris Samaras |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Intrinsic Decomposition of Document Images In-the-Wild |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2020 |
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31st British Machine Vision Conference |
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Automatic document content processing is affected by artifacts caused by the shape
of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised
methods on real data are impossible due to the large amount of data needed. Hence, the
current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 21% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW. |
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Virtual; September 2020 |
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BMVC |
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CIC; 600.087; 600.140; 600.118 |
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Admin @ si @ DSM2020 |
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3461 |
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Xinhang Song; Haitao Zeng; Sixian Zhang; Luis Herranz; Shuqiang Jiang |
![goto web page url](img/www.gif)
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Title |
Generalized Zero-shot Learning with Multi-source Semantic Embeddings for Scene Recognition |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2020 |
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28th ACM International Conference on Multimedia |
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Recognizing visual categories from semantic descriptions is a promising way to extend the capability of a visual classifier beyond the concepts represented in the training data (i.e. seen categories). This problem is addressed by (generalized) zero-shot learning methods (GZSL), which leverage semantic descriptions that connect them to seen categories (e.g. label embedding, attributes). Conventional GZSL are designed mostly for object recognition. In this paper we focus on zero-shot scene recognition, a more challenging setting with hundreds of categories where their differences can be subtle and often localized in certain objects or regions. Conventional GZSL representations are not rich enough to capture these local discriminative differences. Addressing these limitations, we propose a feature generation framework with two novel components: 1) multiple sources of semantic information (i.e. attributes, word embeddings and descriptions), 2) region descriptions that can enhance scene discrimination. To generate synthetic visual features we propose a two-step generative approach, where local descriptions are sampled and used as conditions to generate visual features. The generated features are then aggregated and used together with real features to train a joint classifier. In order to evaluate the proposed method, we introduce a new dataset for zero-shot scene recognition with multi-semantic annotations. Experimental results on the proposed dataset and SUN Attribute dataset illustrate the effectiveness of the proposed method. |
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Virtual; October 2020 |
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ACM |
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LAMP; 600.141; 600.120 |
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Admin @ si @ SZZ2020 |
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3465 |
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Author |
Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Bookworm continual learning: beyond zero-shot learning and continual learning |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2020 |
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Workshop TASK-CV 2020 |
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We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem. |
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Virtual; August 2020 |
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ECCVW |
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Notes |
LAMP; 600.141; 600.120 |
Approved |
no |
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Call Number |
Admin @ si @ WHD2020 |
Serial |
3466 |
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Permanent link to this record |
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Author |
Debora Gil; Guillermo Torres |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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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 |
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Notes |
IAM; 600.139; 600.145 |
Approved |
no |
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Call Number |
Admin @ si @ GiT2020 |
Serial |
3472 |
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Permanent link to this record |
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Author |
Debora Gil; Oriol Ramos Terrades; Raquel Perez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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Year |
2020 |
Publication |
Women in Geometry and Topology |
Abbreviated Journal |
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Address |
Barcelona; September 2019 |
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Notes |
IAM; DAG; 600.139; 600.145; 600.121 |
Approved |
no |
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Call Number |
Admin @ si @ GRP2020 |
Serial |
3473 |
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Permanent link to this record |
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Author |
Riccardo Del Chiaro; Bartlomiej Twardowski; Andrew Bagdanov; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Recurrent attention to transient tasks for continual image captioning |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
Conference Article |
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Year |
2020 |
Publication |
34th Conference on Neural Information Processing Systems |
Abbreviated Journal |
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Abstract |
Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little attention has been focused on continual learning of recurrent models applied to problems like image captioning. In this paper we take a systematic look at continual learning of LSTM-based models for image captioning. We propose an attention-based approach that explicitly accommodates the transient nature of vocabularies in continual image captioning tasks -- i.e. that task vocabularies are not disjoint. We call our method Recurrent Attention to Transient Tasks (RATT), and also show how to adapt continual learning approaches based on weight egularization and knowledge distillation to recurrent continual learning problems. We apply our approaches to incremental image captioning problem on two new continual learning benchmarks we define using the MS-COCO and Flickr30 datasets. Our results demonstrate that RATT is able to sequentially learn five captioning tasks while incurring no forgetting of previously learned ones. |
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Address |
virtual; December 2020 |
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Conference |
NEURIPS |
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Notes |
LAMP; 600.120 |
Approved |
no |
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Call Number |
Admin @ si @ CTB2020 |
Serial |
3484 |
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Permanent link to this record |