Zhuhao Wang

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In this paper, we study leveraging both weakly labeled images and unlabeled images for multi-label image annotation. Motivated by the recent advance in deep learning, we propose an approach called weakly semi-supervised deep learning for multi-label image annotation (WeSed). In WeSed, a novel weakly weighted pairwise ranking loss is effectively utilized to(More)
Generally speaking, different persons tend to describe images from various aspects due to their individually subjective perception. As a result, generating the appropriate descriptions of images with both diversity and high quality is of great importance. In this paper, we propose a framework called GroupTalk to learn multiple image caption distributions(More)
Due to the describable or human-nameable nature of visual attributes, the appropriate utilization of attributes has been receiving much attention in recent years in many applications. Motivated by the assumption that the long-range interactions between attributes can boost image understanding and classification, path coding is utilized in this paper to(More)
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