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Author |
Lorenzo Porzi; Markus Hofinger; Idoia Ruiz; Joan Serrat; Samuel Rota Bulo; Peter Kontschieder |
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Title |
Learning Multi-Object Tracking and Segmentation from Automatic Annotations |
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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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6845-6854 |
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In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of expensive and time-consuming manual annotation approaches. We leverage state-of-the-art instance segmentation results in combination with optical flow predictions, also trained on automatically harvested training data. Our second major contribution is MOTSNet – a deep learning, tracking-by-detection architecture for MOTS – deploying a novel mask-pooling layer for improved object association over time. Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1.9%/+7.5% on cars/pedestrians), and MOTSNet improves by +4.1% over previously best methods on the MOTSChallenge dataset. Our most impressive finding is that we can improve over previous best-performing works, even in complete absence of manually annotated MOTS training data. |
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virtual; June 2020 |
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ADAS; 600.124; 600.118 |
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no |
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Admin @ si @ PHR2020 |
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3402 |
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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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2020 |
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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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IAM; 600.139; 600.145 |
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Admin @ si @ GiT2020 |
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3472 |
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Author |
Yaxing Wang; Salman Khan; Abel Gonzalez-Garcia; Joost Van de Weijer; Fahad Shahbaz Khan |
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Title |
Semi-supervised Learning for Few-shot Image-to-Image Translation |
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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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In the last few years, unpaired image-to-image translation has witnessed remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely on a large number of labeled images. Recently, some methods have tackled the challenging setting of few-shot image-to-image translation, reducing the labeled data requirements for the target domain during inference. In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training. To do so, we propose applying semi-supervised learning via a noise-tolerant pseudo-labeling procedure. We also apply a cycle consistency constraint to further exploit the information from unlabeled images, either from the same dataset or external. Additionally, we propose several structural modifications to facilitate the image translation task under these circumstances. Our semi-supervised method for few-shot image translation, called SEMIT, achieves excellent results on four different datasets using as little as 10% of the source labels, and matches the performance of the main fully-supervised competitor using only 20% labeled data. Our code and models are made public at: this https URL. |
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Virtual; June 2020 |
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LAMP; 600.120 |
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Admin @ si @ WKG2020 |
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3486 |
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Author |
Albin Soutif; Marc Masana; Joost Van de Weijer; Bartlomiej Twardowski |
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Title |
On the importance of cross-task features for class-incremental learning |
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Conference Article |
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2021 |
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Theory and Foundation of continual learning workshop of ICML |
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In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks. The main difference with task-incremental learning, where a task-ID is available at inference time, is that the learner also needs to perform crosstask discrimination, i.e. distinguish between classes that have not been seen together. Approaches to tackle this problem are numerous and mostly make use of an external memory (buffer) of non-negligible size. In this paper, we ablate the learning of crosstask features and study its influence on the performance of basic replay strategies used for class-IL. We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance. Our experimental results show that future algorithms for class-incremental learning should not only prevent forgetting, but also aim to improve the quality of the cross-task features. This is especially important when the number of classes per task is small. |
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Virtual; July 2021 |
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ICMLW |
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LAMP |
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no |
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Admin @ si @ SMW2021 |
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3588 |
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Author |
Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Sebastien Treguer |
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Title |
How far are we from true AutoML: reflection from winning solutions and results of AutoDL challenge |
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Conference Article |
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2020 |
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7th ICML Workshop on Automated Machine Learning |
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Following the completion of the AutoDL challenge (the final challenge in the ChaLearn
AutoDL challenge series 2019), we investigate winning solutions and challenge results to
answer an important motivational question: how far are we from achieving true AutoML?
On one hand, the winning solutions achieve good (accurate and fast) classification performance on unseen datasets. On the other hand, all winning solutions still contain a
considerable amount of hard-coded knowledge on the domain (or modality) such as image,
video, text, speech and tabular. This form of ad-hoc meta-learning could be replaced by
more automated forms of meta-learning in the future. Organizing a meta-learning challenge could help forging AutoML solutions that generalize to new unseen domains (e.g.
new types of sensor data) as well as gaining insights on the AutoML problem from a more
fundamental point of view. The datasets of the AutoDL challenge are a resource that can
be used for further benchmarks and the code of the winners has been outsourced, which is
a big step towards “democratizing” Deep Learning. |
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Virtual; July 2020 |
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ICML |
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HUPBA |
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no |
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Call Number |
Admin @ si @ LPX2020 |
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3502 |
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Permanent link to this record |
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Author |
Marc Masana; Bartlomiej Twardowski; Joost Van de Weijer |
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Title |
On Class Orderings for Incremental Learning |
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Conference Article |
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Year |
2020 |
Publication |
ICML Workshop on Continual Learning |
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The influence of class orderings in the evaluation of incremental learning has received very little attention. In this paper, we investigate the impact of class orderings for incrementally learned classifiers. We propose a method to compute various orderings for a dataset. The orderings are derived by simulated annealing optimization from the confusion matrix and reflect different incremental learning scenarios, including maximally and minimally confusing tasks. We evaluate a wide range of state-of-the-art incremental learning methods on the proposed orderings. Results show that orderings can have a significant impact on performance and the ranking of the methods. |
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Virtual; July 2020 |
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ICMLW |
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LAMP; 600.120 |
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no |
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Admin @ si @ MTW2020 |
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3505 |
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Author |
David Berga; Marc Masana; Joost Van de Weijer |
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Title |
Disentanglement of Color and Shape Representations for Continual Learning |
Type |
Conference Article |
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Year |
2020 |
Publication |
ICML Workshop on Continual Learning |
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We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjusting the network architecture. We tested classification accuracy and forgetting in a task-incremental setting with Oxford-102 Flowers dataset. We combine our method with Elastic Weight Consolidation, Learning without Forgetting, Synaptic Intelligence and Memory Aware Synapses, and show that feature disentanglement positively impacts continual learning performance. |
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Virtual; July 2020 |
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ICMLW |
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LAMP; 600.120 |
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no |
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Admin @ si @ BMW2020 |
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3506 |
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Author |
Mohamed Ali Souibgui; Ali Furkan Biten; Sounak Dey; Alicia Fornes; Yousri Kessentini; Lluis Gomez; Dimosthenis Karatzas; Josep Llados |
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Title |
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition |
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Conference Article |
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2022 |
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Winter Conference on Applications of Computer Vision |
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Document Analysis |
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Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). This appears, for example, in the case of historical ciphered manuscripts, which are usually written with invented alphabets to hide the content. Thus, in this paper we address this problem through a data generation technique based on Bayesian Program Learning (BPL). Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol from the desired alphabet. After generating symbols, we create synthetic lines to train state-of-the-art HTR architectures in a segmentation free fashion. Quantitative and qualitative analyses were carried out and confirm the effectiveness of the proposed method, achieving competitive results compared to the usage of real annotated data. |
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Virtual; January 2022 |
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WACV |
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DAG; 602.230; 600.140 |
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Admin @ si @ SBD2022 |
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3615 |
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Author |
Parichehr Behjati Ardakani; Pau Rodriguez; Armin Mehri; Isabelle Hupont; Carles Fernandez; Jordi Gonzalez |
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Title |
OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network |
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Conference Article |
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2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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2693-2702 |
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Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the depth and width of the networks increase, CNN-based SR methods have been faced with the challenge of computational complexity in practice. More- over, most SR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. To address these limitations we introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We make the following contributions: first, we introduce a lightweight feature extractor that enforces efficient reuse of information through a novel recursive structure of skip and dense connections. Second, to maximize the performance of the feature extractor, we propose a model agnostic reconstruction module that generates accurate high-resolution images from overscaled feature maps obtained from any SR architecture. Third, we introduce a multi-scale loss function to achieve generalization across scales. Experiments show that our proposal outperforms previous state-of-the-art approaches in standard benchmarks, while maintaining relatively low computation and memory requirements. |
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Virtual; January 2021 |
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WACV |
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ISE; 600.119; 600.098 |
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Admin @ si @ BRM2021 |
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3512 |
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Author |
Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor |
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Title |
A Few-shot Learning Approach for Historical Encoded Manuscript Recognition |
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Conference Article |
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2021 |
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25th International Conference on Pattern Recognition |
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5413-5420 |
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Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition. |
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Virtual; January 2021 |
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ICPR |
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DAG; 600.121; 600.140 |
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Admin @ si @ SFK2021 |
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3449 |
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