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.

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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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@research.,pjn@}iiit.ac.in http://cvit.iiit.ac.in/research/projects/tag2vec Figure 1. Learning a direct mapping from videos to hash-tags : sample frames from short video clips with user-given hash-tags (left); a sample frame from a query video and hash-tags suggested by our system for this query (right).ABSTRACT User-given tags or labels are valuable resources for semantic understanding of visual media such as images and videos. Recently, a new type of labeling mechanism known as hashtags have become increasingly popular on social media sites. In this paper, we study the problem of generating relevant and useful hash-tags for short video clips. Traditional datadriven approaches for tag enrichment and recommendation use direct visual similarity for label transfer and propagation. We attempt to learn a direct low-cost mapping from video to hash-tags using a two step training process. We first employ a natural language processing (NLP) technique, skip-gram models with neural network training to learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a corpus of ∼ 10 million hash-tags. We then train an embedding function to map video features to the low-dimensional Tag2vec space. We learn this embedding for 29 categories of short video clips with hash-tags. A query video without any tag-information can then be directly mapped to the vector space of tags using the learned embedding and relevant tags can be found by performing a simple nearest-neighbor retrieval in the Tag2Vec space. We validate the relevance of the tags suggested by our system qualitatively and quantitatively with a user study.

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