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Author (up) Adriana Romero; Carlo Gatta; Gustavo Camps-Valls edit   pdf
doi  openurl
  Title Unsupervised Deep Feature Extraction for Remote Sensing Image Classification Type Journal Article
  Year 2016 Publication IEEE Transaction on Geoscience and Remote Sensing Abbreviated Journal TGRS  
  Volume 54 Issue 3 Pages 1349 - 1362  
  Keywords  
  Abstract This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.  
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  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0196-2892 ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; 600.079;MILAB Approved no  
  Call Number Admin @ si @ RGC2016 Serial 2723  
Permanent link to this record
 
 
Author (up) Aitor Alvarez-Gila; Adrian Galdran; Estibaliz Garrote; Joost Van de Weijer edit   pdf
url  openurl
  Title Self-supervised blur detection from synthetically blurred scenes Type Journal Article
  Year 2019 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 92 Issue Pages 103804  
  Keywords  
  Abstract Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image.  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; 600.109; 600.120 Approved no  
  Call Number Admin @ si @ AGG2019 Serial 3301  
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Author (up) Akshita Gupta; Sanath Narayan; Salman Khan; Fahad Shahbaz Khan; Ling Shao; Joost Van de Weijer edit  doi
openurl 
  Title Generative Multi-Label Zero-Shot Learning Type Journal Article
  Year 2023 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 45 Issue 12 Pages 14611-14624  
  Keywords Generalized zero-shot learning; Multi-label classification; Zero-shot object detection; Feature synthesis  
  Abstract Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of zero-shot setting. When multiple objects occur jointly in a single image, a critical question is how to effectively fuse multi-class information. In this work, we introduce different fusion approaches at the attribute-level, feature-level and cross-level (across attribute and feature-levels) for synthesizing multi-label features from their corresponding multi-label class embeddings. To the best of our knowledge, our work is the first to tackle the problem of multi-label feature synthesis in the (generalized) zero-shot setting. Our cross-level fusion-based generative approach outperforms the state-of-the-art on three zero-shot benchmarks: NUS-WIDE, Open Images and MS COCO. Furthermore, we show the generalization capabilities of our fusion approach in the zero-shot detection task on MS COCO, achieving favorable performance against existing methods.  
  Address December 2023  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes LAMP; PID2021-128178OB-I00 Approved no  
  Call Number Admin @ si @ Serial 3853  
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Author (up) Alex Gomez-Villa; Adrian Martin; Javier Vazquez; Marcelo Bertalmio; Jesus Malo edit  url
doi  openurl
  Title On the synthesis of visual illusions using deep generative models Type Journal Article
  Year 2022 Publication Journal of Vision Abbreviated Journal JOV  
  Volume 22(8) Issue 2 Pages 1-18  
  Keywords  
  Abstract Visual illusions expand our understanding of the visual system by imposing constraints in the models in two different ways: i) visual illusions for humans should induce equivalent illusions in the model, and ii) illusions synthesized from the model should be compelling for human viewers too. These constraints are alternative strategies to find good vision models. Following the first research strategy, recent studies have shown that artificial neural network architectures also have human-like illusory percepts when stimulated with classical hand-crafted stimuli designed to fool humans. In this work we focus on the second (less explored) strategy: we propose a framework to synthesize new visual illusions using the optimization abilities of current automatic differentiation techniques. The proposed framework can be used with classical vision models as well as with more recent artificial neural network architectures. This framework, validated by psychophysical experiments, can be used to study the difference between a vision model and the actual human perception and to optimize the vision model to decrease this difference.  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; 600.161; 611.007 Approved no  
  Call Number Admin @ si @ GMV2022 Serial 3682  
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Author (up) AN Ruchai; VI Kober; KA Dorofeev; VN Karnaukhov; Mikhail Mozerov edit  url
doi  openurl
  Title Classification of breast abnormalities using a deep convolutional neural network and transfer learning Type Journal Article
  Year 2021 Publication Journal of Communications Technology and Electronics Abbreviated Journal  
  Volume 66 Issue 6 Pages 778–783  
  Keywords  
  Abstract A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database.  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ RKD2022 Serial 3680  
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