LAMP

Tag: CVPR 2018

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

  • 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 […]

  • Four paper accepted in CVPR 2018

  • Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

    Xialei Liu, Joost van de Weijer, Andrew D. Bagdanov Read Full Paper → We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of cropped images , we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same […]

  • On the Duality Between Retinex and Image Dehazing

    Adrian Galdran, Aitor Alvarez-Gila, Alessandro Bria, Javier Vazquez-Corral, Marcelo Bertalmio Read Full Paper → Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different […]

  • Objects as context for detecting their semantic parts

    Abel Gonzalez-Garcia, Davide Modolo, Vittorio Ferrari Read Full Paper → We present a semantic part detection approach that effectively leverages object information.We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a […]