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Author |
Lu Yu; Bartlomiej Twardowski; Xialei Liu; Luis Herranz; Kai Wang; Yongmai Cheng; Shangling Jui; Joost Van de Weijer |
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Title |
Semantic Drift Compensation for Class-Incremental Learning of Embeddings |
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Conference Article |
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2020 |
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33rd IEEE Conference on Computer Vision and Pattern Recognition |
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Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes. In this setting, networks suffer from catastrophic forgetting which refers to the drastic drop in performance on previous tasks. The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes. Embedding networks have the advantage that new classes can be naturally included into the network without adding new weights. Therefore, we study incremental learning for embedding networks. In addition, we propose a new method to estimate the drift, called semantic drift, of features and compensate for it without the need of any exemplars. We approximate the drift of previous tasks based on the drift that is experienced by current task data. We perform experiments on fine-grained datasets, CIFAR100 and ImageNet-Subset. We demonstrate that embedding networks suffer significantly less from catastrophic forgetting. We outperform existing methods which do not require exemplars and obtain competitive results compared to methods which store exemplars. Furthermore, we show that our proposed SDC when combined with existing methods to prevent forgetting consistently improves results. |
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Virtual CVPR |
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LAMP; 600.141; 601.309; 602.200; 600.120 |
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Admin @ si @ YTL2020 |
Serial |
3422 |
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Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
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Title |
Distilling Content from Style for Handwritten Word Recognition |
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Conference Article |
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Year |
2020 |
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17th International Conference on Frontiers in Handwriting Recognition |
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Despite the latest transcription accuracies reached using deep neural network architectures, handwritten text recognition still remains a challenging problem, mainly because of the large inter-writer style variability. Both augmenting the training set with artificial samples using synthetic fonts, and writer adaptation techniques have been proposed to yield more generic approaches aimed at dodging style unevenness. In this work, we take a step closer to learn style independent features from handwritten word images. We propose a novel method that is able to disentangle the content and style aspects of input images by jointly optimizing a generative process and a handwritten
word recognizer. The generator is aimed at transferring writing style features from one sample to another in an image-to-image translation approach, thus leading to a learned content-centric features that shall be independent to writing style attributes.
Our proposed recognition model is able then to leverage such writer-agnostic features to reach better recognition performances. We advance over prior training strategies and demonstrate with qualitative and quantitative evaluations the performance of both
the generative process and the recognition efficiency in the IAM dataset. |
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Virtual ICFHR; September 2020 |
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ICFHR |
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DAG; 600.129; 600.140; 600.121 |
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no |
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Admin @ si @ KRR2020 |
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3425 |
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Author |
Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
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Title |
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images |
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2020 |
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16th European Conference on Computer Vision |
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Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images. |
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Virtual; August 2020 |
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ECCV |
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DAG; 600.140; 600.121; 600.129 |
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Call Number |
Admin @ si @ KPW2020 |
Serial |
3426 |
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Author |
Henry Velesaca; Steven Araujo; Patricia Suarez; Angel Sanchez; Angel Sappa |
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Title |
Off-the-Shelf Based System for Urban Environment Video Analytics |
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Conference Article |
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2020 |
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27th International Conference on Systems, Signals and Image Processing |
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greenhouse gases; carbon footprint; object detection; object tracking; website framework; off-the-shelf video analytics |
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Abstract |
This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to
public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach. |
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Virtual IWSSIP |
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IWSSIP |
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MSIAU; 600.130; 601.349; 600.122 |
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Call Number |
Admin @ si @ VAS2020 |
Serial |
3429 |
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Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla |
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Title |
Thermal Image Super-resolution: A Novel Architecture and Dataset |
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Conference Article |
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Year |
2020 |
Publication |
15th International Conference on Computer Vision Theory and Applications |
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111-119 |
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This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem.
The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are available. |
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Valletta; Malta; February 2020 |
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VISAPP |
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MSIAU; 600.130; 600.122 |
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Call Number |
Admin @ si @ RSV2020 |
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3432 |
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Author |
Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez |
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Title |
Computing the Testing Error Without a Testing Set |
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Conference Article |
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Year |
2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
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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. |
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Virtual CVPR |
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CVPR |
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HuPBA; no proj |
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no |
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Call Number |
Admin @ si @ CEM2020 |
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3437 |
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Author |
Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz |
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Title |
Gate-Shift Networks for Video Action Recognition |
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Conference Article |
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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 |
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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 |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li |
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Title |
Multi-modal Face Presentation Attach Detection |
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Book Whole |
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2020 |
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Synthesis Lectures on Computer Vision |
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13 |
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HuPBA |
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Admin @ si @ WGE2020 |
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3440 |
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Author |
Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes |
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A conditional GAN based approach for distorted camera captured documents recovery |
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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 |
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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 |
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Title |
Light Direction and Color Estimation from Single Image with Deep Regression |
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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 |
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Title |
Intrinsic Decomposition of Document Images In-the-Wild |
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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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Call Number |
Admin @ si @ DSM2020 |
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3461 |
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Author |
Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer |
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Title |
Bookworm continual learning: beyond zero-shot learning and continual learning |
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Conference Article |
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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 |
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Title |
A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
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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 |
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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 |
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Title |
Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution |
Type |
Conference Article |
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Year |
2020 |
Publication |
Women in Geometry and Topology |
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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 |
