Scene-Domain Active Part Models for Object Representation

Abstract : In this paper, we are interested in enhancing the expressivity and robustness of part-based models for object representation, in the common scenario where the training data are based on 2D images. To this end, we propose scene-domain active part models (SDAPM), which reconstruct and characterize the 3D geometric statistics between object's parts in 3D scene-domain by using 2D training data in the image-domain alone. And on top of this, we explicitly model and handle occlusions in SDAPM. Together with the developed learning and inference algorithms, such a model provides rich object descriptions, including 2D object and parts localization, 3D landmark shape and camera viewpoint, which offers an effective representation to various image understanding tasks, such as object and parts detection, 3D landmark shape and viewpoint estimation from images. Experiments on the above tasks show that SDAPM outperforms previous part-based models, and thus demonstrates the potential of the proposed technique.
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Communication dans un congrès
IEEE International Conference on Computer Vision (ICCV), Dec 2015, Santiago, Chile. pp.2497 - 2505, 2015, Computer Vision (ICCV), 2015 IEEE International Conference on. 〈10.1109/ICCV.2015.287〉
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Soumis le : mardi 29 août 2017 - 10:53:06
Dernière modification le : mercredi 11 avril 2018 - 12:12:03

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Zhou Ren, Chaohui Wang, Alan Yuille. Scene-Domain Active Part Models for Object Representation. IEEE International Conference on Computer Vision (ICCV), Dec 2015, Santiago, Chile. pp.2497 - 2505, 2015, Computer Vision (ICCV), 2015 IEEE International Conference on. 〈10.1109/ICCV.2015.287〉. 〈hal-01578411〉

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