LAMP

Category: Publications

  • Mix and match networks: cross-modal alignment for zero-pair image-to-image translation

    Yaxing Wang, Luis Herranz, Joost van de Weijer Read Full Paper → 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 […]

  • Generative Feature Replay For Class-Incremental Learning

    Xialei Liu, Chenshen Wu, Mikel Menta, Luis Herranz, Bogdan Raducanu, Andrew D. Bagdanov, Shangling Jui, Joost van de Weijer Read Full Paper → Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We consider a class-incremental setting which means that the task-ID is unknown at inference time. […]

  • Semi-supervised Learning for Few-shot Image-to-Image Translation

    Yaxing Wang, Salman Khan, Abel Gonzalez-Garcia, Joost van de Weijer, Fahad Shahbaz Khan Read Full Paper → 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 […]

  • Semantic Drift Compensation for Class-Incremental Learning

    Lu Yu, Bartłomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, Joost van de Weijer Read Full Paper → 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 […]

  • Orderless Recurrent Models for Multi-label Classification

    Vacit Oguz Yazici, Abel Gonzalez-Garcia, Arnau Ramisa, Bartlomiej Twardowski, Joost van de Weijer Read Full Paper → Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task. Current approaches sort labels according to their frequency, typically ordering them in either […]

  • MineGAN: effective knowledge transfer from GANs to target domains with few images

    Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, Joost van de Weijer Read Full Paper → One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has […]

  • Multi-Modal Fusion for End-to-End RGB-T Tracking

    Lichao Zhang, Martin Danelljan, Abel Gonzalez-Garcia, Joost van de Weijer, Fahad Shahbaz Khan Read Full Paper → We propose an end-to-end tracking framework for fusing the RGB and TIR modalities in RGB-T tracking. Our baseline tracker is DiMP (Discriminative Model Prediction), which employs a carefully designed target prediction network trained end-to-end using a discriminative loss. We analyze the effectiveness […]

  • SID4VAM: A Benchmark Dataset With Synthetic Images for Visual Attention Modeling

    David Berga, Xose R. Fdez-Vidal, Xavier Otazu, Xose M. Pardo Read Full Paper → A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with […]

  • Active Learning for Deep Detection Neural Networks

    Hamed H. Aghdam, Abel Gonzalez-Garcia, Joost van de Weijer, Antonio M. López Read Full Paper → The cost of drawing object bounding boxes (i.e. labeling) for millions of images is prohibitively high. For instance, labeling pedestrians in a regular urban image could take 35 seconds on average. Active learning aims to reduce the cost of labeling by selecting […]

  • Learning the Model Update for Siamese Trackers

    Lichao Zhang, Abel Gonzalez-Garcia, Joost van de Weijer, Martin Danelljan, Fahad Shahbaz Khan Read Full Paper → Siamese approaches address the visual tracking problem by extracting an appearance template from the current frame, which is used to localize the target in the next frame. In general, this template is linearly combined with the accumulated template from the previous frame, […]