LAMP

Category: Publications

  • 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, […]

  • Saliency for Fine-grained Object Recognition in Domains with Scarce Training Data

    Carola Figueroa Flores, Abel Gonzalez-García, Joost van de Weijer, Bogdan Raducanu Read Full Paper → 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 […]

  • Learning Metrics from Teachers: Compact Networks for Image Embedding

    Lu Yu, Vacit Oguz Yazici, Xialei Liu, Joost van de Weijer, Yongmei Cheng, Arnau Ramisa Read Full Paper → Metric learning networks are used to compute image embeddings, which are widely used in many applications such as image retrieval and face recognition. In this paper, we propose to use network distillation to efficiently compute image embeddings with small networks. Network […]

  • Learning effective RGB-D representations for scene recognition

    Xinhang Song, Shuqiang Jiang, Luis Herranz, Chengpeng Chen Read Full Paper → Deep convolutional networks can achieve impressive results on RGB scene recognition thanks to large data sets such as places. In contrast, RGB-D scene recognition is still underdeveloped in comparison, due to two limitations of RGB-D data we address in this paper. The first […]

  • Domain-adaptive deep network compression

    Marc Masana, Joost van de Weijer, Luis Herranz, Andrew D. Bagdanov, Jose M Alvarez Read Full Paper → Deep Neural Networks trained on large datasets can be easily transferred to new domains with far fewer labeled examples by a process called fine-tuning. This has the advantage that representations learned in the large source domain can be exploited on smaller […]

  • Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting

    Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M. Lopez, Andrew D. Bagdanov Read Full Paper → In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form […]

  • Mix and match networks: encoder-decoder alignment for zero-pair image translation

    Yaxing Wang, Joost van de Weijer, Luis Herranz Read Full Paper → We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be […]