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Author Maria Elena Meza de Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas edit   pdf
url  openurl
  Title A Social-Aware Assistant to support individuals with visual impairments during social interaction: A systematic requirements analysis Type Journal Article
  Year 2019 Publication (down) International Journal of Human-Computer Studies Abbreviated Journal IJHC  
  Volume 122 Issue Pages 50-60  
  Keywords  
  Abstract Visual impairment affects the normal course of activities in everyday life including mobility, education, employment, and social interaction. Most of the existing technical solutions devoted to empowering the visually impaired people are in the areas of navigation (obstacle avoidance), access to printed information and object recognition. Less effort has been dedicated so far in developing solutions to support social interactions. In this paper, we introduce a Social-Aware Assistant (SAA) that provides visually impaired people with cues to enhance their face-to-face conversations. The system consists of a perceptive component (represented by smartglasses with an embedded video camera) and a feedback component (represented by a haptic belt). When the vision system detects a head nodding, the belt vibrates, thus suggesting the user to replicate (mirror) the gesture. In our experiments, sighted persons interacted with blind people wearing the SAA. We instructed the former to mirror the noddings according to the vibratory signal, while the latter interacted naturally. After the face-to-face conversation, the participants had an interview to express their experience regarding the use of this new technological assistant. With the data collected during the experiment, we have assessed quantitatively and qualitatively the device usefulness and user satisfaction.  
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  Notes LAMP; 600.109; 600.120 Approved no  
  Call Number Admin @ si @ MTR2019 Serial 3142  
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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 2020 Publication (down) International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 128 Issue Pages 2849–2872  
  Keywords  
  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 Yaxing Wang; Abel Gonzalez-Garcia; Chenshen Wu; Luis Herranz; Fahad Shahbaz Khan; Shangling Jui; Jian Yang; Joost Van de Weijer edit  url
openurl 
  Title MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains Type Journal Article
  Year 2024 Publication (down) International Journal of Computer Vision Abbreviated Journal IJCV  
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  Abstract Given the often enormous effort required to train GANs, both computationally as well as in dataset collection, the re-use of pretrained GANs largely increases the potential impact of generative models. Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods, such as mode collapse and lack of flexibility. Furthermore, to prevent overfitting on small target domains, we introduce sparse subnetwork selection, that restricts the set of trainable neurons to those that are relevant for the target dataset. We perform comprehensive experiments on several challenging datasets using various GAN architectures (BigGAN, Progressive GAN, and StyleGAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. MineGAN.  
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  Notes LAMP; MACO Approved no  
  Call Number Admin @ si @ WGW2024 Serial 3888  
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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 2019 Publication (down) 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 Carlo Gatta; Francesco Ciompi edit   pdf
doi  openurl
  Title Stacked Sequential Scale-Space Taylor Context Type Journal Article
  Year 2014 Publication (down) IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 36 Issue 8 Pages 1694-1700  
  Keywords  
  Abstract We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets.  
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  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; MILAB; 601.160; 600.079 Approved no  
  Call Number Admin @ si @ GaC2014 Serial 2466  
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