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We present a discriminative latent topic model for scene recognition. The capacity of our model is originated from the modeling of two types of visual contexts, i.e., the category specific global spatial layout of different scene elements, and the reinforcement of the visual coherence in uniform local regions. In contrast, most previous methods for scene(More)
To address the non-stationary property of aging patterns, age estimation can be cast as an ordinal regression problem. However, the processes of extracting features and learning a regression model are often separated and optimized independently in previous work. In this paper, we propose an End-to-End learning approach to address ordinal regression problems(More)
In this paper, we address the problem of recognizing images with weakly annotated text tags. Most previous work either cannot be applied to the scenarios where the tags are loosely related to the images, or simply take a pre-fusion at the feature level or a post-fusion at the decision level to combine the visual and textual content. Instead, we first encode(More)
With the popularity of mobile devices and social networks, users can easily build their personalized image sets. Thus, personalized image analysis, indexing, and retrieval have become important topics in social media analysis. Because of users' diverse preferences, their personalized image sets are usually related to specific topics and show large feature(More)
Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models such as Latent Dirichlet Allocation (LDA). However, in those methods no prior knowledge for the(More)