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Now showing items 1 - 5 of 5

  • Guess where? Actor-supervision for spatiotemporal action localization

    Escorcia, Victor   Dao, Cuong D.   Jain, Mihir   Ghanem, Bernard   Snoek, Cees  

    This paper addresses the problem of spatiotemporal localization of actions in videos. Compared to leading approaches, which all learn to localize based on carefully annotated boxes on training video frames, we adhere to a solution only requiring video class labels. We introduce an actor-supervised architecture that exploits the inherent compositionality of actions in terms of actor transformations, to localize actions. We make two contributions. First, we propose actor proposals derived from a detector for human and non-human actors intended for images, which are linked over time by Siamese similarity matching to account for actor deformations. Second, we propose an actor-based attention mechanism enabling localization from action class labels and actor proposals. It exploits a new actor pooling operation and is end-to-end trainable. Experiments on four action datasets show actor supervision is state-of-the-art for action localization from video class labels and is even competitive to some box-supervised alternatives.
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  • Deep Learning for Visual Understanding: Part 2

    Porikli, Fatih   Shan, Shiguang   Snoek, Cees   Sukthankar, Rahul   Wang, Xiaogang  

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  • Evaluating Color Descriptors for Object and Scene Recognition

    Snoek, Cees   Gevers, Theo   van de Sande, Koen  

    Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been proposed. Because many different descriptors exist, a structured overview is required of color invariant descriptors in the context of image category recognition. Therefore, this paper studies the invariance properties and the distinctiveness of color descriptors (software to compute the color descriptors from this paper is available from http://www.colordescriptors.com) in a structured way. The analytical invariance properties of color descriptors are explored, using a taxonomy based on invariance properties with respect to photometric transformations, and tested experimentally using a data set with known illumination conditions. In addition, the distinctiveness of color descriptors is assessed experimentally using two benchmarks, one from the image domain and one from the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. The results further reveal that, for light intensity shifts, the usefulness of invariance is category-specific. Overall, when choosing a single descriptor and no prior knowledge about the data set and object and scene categories is available, the OpponentSIFT is recommended. Furthermore, a combined set of color descriptors outperforms intensity-based SIFT and improves category recognition by 8 percent on the PASCAL VOC 2007 and by 7 percent on the Mediamill Challenge.
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  • Deep Learning for Visual Understanding

    Porikli, Fatih   Shan, Shiguang   Snoek, Cees   Sukthankar, Rahul   Wang, Xiaogang  

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  • A Medium-Scale Distributed System for Computer Science Research:Infrastructure for the Long Term

    Bal, Henri   Epema, Dick   de laat, Cees   van Nieuwpoort, Rob   Romein, John   Seinstra, Frank   Snoek, Cees   Wijshoff, Harry  

    The Dutch Advanced School for Computing and Imaging has built five generations of a 200-node distributed system over nearly two decades while remaining aligned with the shifting computer science research agenda. The system has supported years of award-winning research, underlining the benefits of investing in a smaller-scale, tailored design.
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