LAMP

Category: Publications

  • Memory Replay GANs: learning to generate images from new categories without forgetting

    Chenshen Wu, Luis Herranz, Xialei Liu, Yaxing Wang, Joost van de Weijer, Bogdan Raducanu Read Full Paper → Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. […]

  • Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

    Xialei Liu, Joost van de Weijer, Andrew D. Bagdanov Read Full Paper → For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which […]

  • Synthetic data generation for end-to-end thermal infrared tracking

    Lichao Zhang, Abel Gonzalez-Garcia, Joost van de Weijer, Martin Danelljan, Fahad Shahbaz Khan Read Full Paper → The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks for tracking in thermal infrared (TIR) images. Therefore, […]

  • Image-to-image translation for cross-domain disentanglement

    Abel Gonzalez-Garcia, Joost van de Weijer, Yoshua Bengio Read Full Paper → Deep image translation methods have recently shown excellent results, outputting high-quality images covering multiple modes of the data distribution. There has also been increased interest in disentangling the internal representations learned by deep methods to further improve their performance and achieve a finer control. In […]

  • Metric Learning for Novelty and Anomaly Detection

    Marc Masana, Idoia Ruiz, Joan Serrat, Joost van de Weijer, Antonio M. Lopez Read Full Paper → When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We […]

  • Transferring GANs: generating images from limited data

    Yaxing Wang, Chenshen Wu, Luis Herranz, Joost van de Weijer, Abel Gonzalez-Garcia, Bogdan Raducanu Read Full Paper → Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models. To the best of our knowledge this practice has not been studied within the context of generative deep […]

  • Context Proposals for Saliency Detection

    Aymen Azaza, Joost van de Weijer, Ali Douik, Marc Masana Read Full Paper → One of the fundamental properties of a salient object region is its contrast with the immediate context. The problem is that numerous object regions exist which potentially can all be salient. One way to prevent an exhaustive search over all object regions is by […]

  • Weakly Supervised Domain-Specific Color Naming Based on Attention

    Lu Yu, Yongmei Cheng, Joost van de Weijer Read Full Paper → The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an […]

  • Learning Illuminant Estimation from Object Recognition

    Marco Buzzelli, Joost van de Weijer, Raimondo Schettini Read Full Paper → In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, […]

  • Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification

    Rao Muhammad Anwer, Fahad Shahbaz Khan, Joost van de Weijer, Matthieu Molinier, Jorma Laaksonen Read Full Paper → Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless […]