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We consider the problem of joint modeling of videos and their corresponding textual descriptions (e.g. sentences or phrases). Our approach consists of three components: the video representation, the textual representation, and a joint model that links videos and text. Our video representation uses the state-of-the-art deep 3D ConvNet to capture the semantic information in the video. Our textual representation...
In this paper, we address the problem of recognizing unfinished human activity from partially observed videos. Specifically, we propose a novel human activity descriptor, which can represent pairwise relationships among human activities in a compact manner using pre-trained Convolutional Neural Networks (CNNs) by capturing the discriminative sub-volume. The potentially important relationship among...
Inferring the aesthetic quality of images is a challenging computer vision task due to its subjective and conceptual nature. Most image aesthetics evaluation approaches focused on designing handcrafted features, and only a few adopted learning of relevant and imperative characteristics in a data-driven manner. In this paper, we propose to attune Convolutional Neural Networks (CNNs) for image aesthetics...
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