LAMP

Category: Publications

  • Does Multimodality Help Human and Machine for Translation and Image Captioning?

    Ozan Caglayan, Walid Aransa, Yaxing Wang, Marc Masana, Mercedes García-Martínez, Fethi Bougares, Loïc Barrault, Joost van de Weijer Read Full Paper → This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed […]

  • On-the-fly Network Pruning for Object Detection

    Marc Masana, Joost van de Weijer, Andrew D. Bagdanov Read Full Paper → Object detection with deep neural networks is often performed by passing a few thousand candidate bounding boxes through a deep neural network for each image. These bounding boxes are highly correlated since they originate from the same image. In this paper we investigate how […]

  • Hierarchical part detection with deep neural networks

    Esteve Cervantes; Long Long Yu; Andrew D. Bagdanov; Marc Masana; Joost van de Weijer Read Full Paper → Part detection is an important aspect of object recognition. Most approaches apply object proposals to generate hundreds of possible part bounding box candidates which are then evaluated by part classifiers. Recently several methods have investigated directly regressing to a limited set […]

  • FitNets: Hints for Thin Deep Nets

    Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, Yoshua Bengio Read Full Paper → While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student […]

  • Unrolling loopy top-down semantic feedback in convolutional deep networks

    Carlo Gatta, Adriana Romero, Joost van de Weijer Read Full Paper → In this paper, we propose a novel way to perform top-down semantic feedback in convolutional deep networks for efficient and accurate image parsing. We also show how to add global appearance/semantic features, which have shown to improve image parsing performance in state-of-the-art methods, […]

  • Accurate Stereo Matching by Two-Step Energy Minimization

    Mikhail G. Mozerov; Joost van de Weijer Read Full Paper → In stereo matching, cost-filtering methods and energy-minimization algorithms are considered as two different techniques. Due to their global extent, energy-minimization methods obtain good stereo matching results. However, they tend to fail in occluded regions, in which cost-filtering approaches obtain better results. In this paper, we […]

  • Semantic Pyramids for Gender and Action Recognition

    Fahad Shahbaz Khan; Joost van de Weijer; Rao Muhammad Anwer; Michael Felsberg; Carlo Gatta Read Full Paper → Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single […]

  • Leveraging local neighborhood topology for large scale person re-identification

    Svebor Karaman, Giuseppe Lisanti, Andrew D. Bagdanov, Alberto Del Bimbo Read Full Paper → In this paper we describe a semi-supervised approach to person re-identification that combines discriminative models of person identity with a Conditional Random Field (CRF) to exploit the local manifold approximation induced by the nearest neighbor graph in feature space. The linear […]

  • Unsupervised Scene Adaptation for Faster Multi-scale Pedestrian Detection

    Federico Bartoli; Giuseppe Lisanti; Svebor Karaman; Andrew D. Bagdanov; Alberto Del Bimbo Read Full Paper → In this paper we describe an approach to automatically improving the efficiency of soft cascade-based person detectors. Our technique addresses the two fundamental bottlenecks in cascade detectors: the number of weak classifiers that need to be evaluated in each cascade, and the total […]

  • Fisher Vectors over Random Density Forests for Object Recognition

    Claudio Baecchi; Francesco Turchini; Lorenzo Seidenari; Andrew D. Bagdanov; Alberto Del Bimbo Read Full Paper → In this paper we describe a Fisher vector encoding of images over Random Density Forests. Random Density Forests (RDFs) are an unsupervised variation of Random Decision Forests for density estimation. In this work we train RDFs by splitting at each node in order […]