2 Verisk Analytics 3 Google Research https://hhsinping.github.io/3d_scene_stylization Input views Style image Stylized novel views Figure 1. 3D scene stylization. Given a set of images of a 3D scene (left) as well as a reference image of the desired style (middle), our method is able to modify the style of the 3D scene, and synthesize images of arbitrary novel views (right). The novel view synthesis results 1) contain the desired style and 2) are consistent across various novel views, e.g. the texture in the yellow boxes.
Abstract. Short internet video clips like vines present a significantly wild distribution compared to traditional video datasets. In this paper, we focus on the problem of unsupervised action classification in wild vines using traditional labeled datasets. To this end, we use a data augmentation based simple domain adaptation strategy. We utilize semantic word2vec space as a common subspace to embed video features from both, labeled source domain and unlabled target domain. Our method incrementally augments the labeled source with target samples and iteratively modifies the embedding function to bring the source and target distributions together. Additionally, we utilize a multi-modal representation that incorporates noisy semantic information available in form of hash-tags. We show the effectiveness of this simple adaptation technique on a test set of vines and achieve notable improvements in performance.
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