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Author Rahma Kalboussi; Aymen Azaza; Joost Van de Weijer; Mehrez Abdellaoui; Ali Douik edit  url
openurl 
  Title Object proposals for salient object segmentation in videos Type Journal Article
  Year (down) 2020 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 79 Issue 13 Pages 8677-8693  
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  Abstract Salient object segmentation in videos is generally broken up in a video segmentation part and a saliency assignment part. Recently, object proposals, which are used to segment the image, have had significant impact on many computer vision applications, including image segmentation, object detection, and recently saliency detection in still images. However, their usage has not yet been evaluated for salient object segmentation in videos. Therefore, in this paper, we investigate the application of object proposals to salient object segmentation in videos. In addition, we propose a new motion feature derived from the optical flow structure tensor for video saliency detection. Experiments on two standard benchmark datasets for video saliency show that the proposed motion feature improves saliency estimation results, and that object proposals are an efficient method for salient object segmentation. Results on the challenging SegTrack v2 and Fukuchi benchmark data sets show that we significantly outperform the state-of-the-art.  
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  Notes LAMP; 600.120 Approved no  
  Call Number KAW2020 Serial 3504  
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Author Xiangyang Li; Luis Herranz; Shuqiang Jiang edit   pdf
url  openurl
  Title Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition Type Journal
  Year (down) 2020 Publication ACM Transactions on Data Science Abbreviated Journal ACM  
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  Abstract In recent years, convolutional neural networks (CNNs) have achieved impressive performance for various visual recognition scenarios. CNNs trained on large labeled datasets can not only obtain significant performance on most challenging benchmarks but also provide powerful representations, which can be used to a wide range of other tasks. However, the requirement of massive amounts of data to train deep neural networks is a major drawback of these models, as the data available is usually limited or imbalanced. Fine-tuning (FT) is an effective way to transfer knowledge learned in a source dataset to a target task. In this paper, we introduce and systematically investigate several factors that influence the performance of fine-tuning for visual recognition. These factors include parameters for the retraining procedure (e.g., the initial learning rate of fine-tuning), the distribution of the source and target data (e.g., the number of categories in the source dataset, the distance between the source and target datasets) and so on. We quantitatively and qualitatively analyze these factors, evaluate their influence, and present many empirical observations. The results reveal insights into what fine-tuning changes CNN parameters and provide useful and evidence-backed intuitions about how to implement fine-tuning for computer vision tasks.  
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  Notes LAMP; 600.141; 600.120 Approved no  
  Call Number Admin @ si @ LHJ2020 Serial 3423  
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Author Yaxing Wang; Luis Herranz; Joost Van de Weijer edit   pdf
url  doi
openurl 
  Title Mix and match networks: multi-domain alignment for unpaired image-to-image translation Type Journal Article
  Year (down) 2020 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 128 Issue Pages 2849–2872  
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  Abstract This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities. We assume that only some of the pairwise translations have been seen (i.e. trained) and infer the remaining unseen translations (where training pairs are not available). We propose mix and match networks, an approach where multiple encoders and decoders are aligned in such a way that the desired translation can be obtained by simply cascading the source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen). The main challenge lies in the alignment of the latent representations at the bottlenecks of encoder-decoder pairs. We propose an architecture with several tools to encourage alignment, including autoencoders and robust side information and latent consistency losses. We show the benefits of our approach in terms of effectiveness and scalability compared with other pairwise image-to-image translation approaches. We also propose zero-pair cross-modal image translation, a challenging setting where the objective is inferring semantic segmentation from depth (and vice-versa) without explicit segmentation-depth pairs, and only from two (disjoint) segmentation-RGB and depth-RGB training sets. We observe that a certain part of the shared information between unseen modalities might not be reachable, so we further propose a variant that leverages pseudo-pairs which allows us to exploit this shared information between the unseen modalities  
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  Notes LAMP; 600.109; 600.106; 600.141; 600.120 Approved no  
  Call Number Admin @ si @ WHW2020 Serial 3424  
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Author 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 (down) 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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  Notes LAMP; 600.109; 600.120 Approved no  
  Call Number Admin @ si @ AGG2019 Serial 3301  
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Author Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu edit   pdf
url  openurl
  Title Saliency for fine-grained object recognition in domains with scarce training data Type Journal Article
  Year (down) 2019 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 94 Issue Pages 62-73  
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  Abstract This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate a large dataset. The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network’s performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline.  
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  Notes LAMP; OR; 600.109; 600.141; 600.120 Approved no  
  Call Number Admin @ si @ FGW2019 Serial 3264  
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