LAMP

Category: Publications

  • 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 […]

  • Scale Coding Bag-of-Words for Action Recognition

    Fahad Shahbaz Khan; Joost Van De Weijer; Andrew D. Bagdanov; Michael Felsberg Read Full Paper → Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the […]

  • Adaptive Color Attributes for Real-Time Visual Tracking

    Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg, Joost van de Weijer Read Full Paper → Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined […]

  • Stacked Sequential Scale-SpaceTaylor Context

    Carlo Gatta; Francesco Ciompi Read Full Paper → 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 […]

  • Local Pyramidal Descriptors for Image Recognition

    Lorenzo Seidenari; Giuseppe Serra; Andrew D. Bagdanov; Alberto Del Bimbo Read Full Paper → In this paper, we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be […]