LAMP

Tag: TIP

  • Six papers accepted in CVPR’18, NeurIPS’18, TIP, ICCV’17, and ICPR’18

  • 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 […]

  • One paper accepted in IEEE TIP

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

  • 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, […]

  • One paper accepted in IEEE TIP

  • Improved Recursive Geodesic Distance Computation for Edge Preserving Filter

    Mikhail G. Mozerov; Joost van de Weijer Read Full Paper → All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this […]

  • One paper accepted at IEEE TIP

  • 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 […]

  • Big news month!

  • 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 […]