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result(s) for
"high concept"
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Radio silence
A studious girl and a quiet, straight-A boy start a controversial podcast together that challenges their courage and forces them to confront issues in the form of backlash and censorship.
Book
The Studios after the Studios
Modern Hollywood is dominated by a handful of studios: Columbia, Disney, Fox, Paramount, Universal, and Warner Bros. Threatened by independents in the 1970s, they returned to power in the 1980s, ruled unquestioned in the 1990s, and in the new millennium are again beseiged. But in the heyday of this new classical era, the major studios movies — their stories and styles — were astonishingly precise biographies of the studios that made them. Movies became product placements for their studios, advertising them to the industry, to their employees, and to the public at large. If we want to know how studios work—how studios think—we need to watch their films closely. How closely? Maniacally so. In a wide range of examples, The Studios after the Studios explores the gaps between story and backstory in order to excavate the hidden history of Hollywood's second great studio era.
eBook
Bronx masquerade
While studying the Harlem Renaissance, students at a Bronx high school read aloud poems they've written, revealing their innermost thoughts and fears to their formerly clueless classmates.
Book
Dual-Space Transfer Learning Based on an Indirect Mutual Promotion Strategy
Transfer learning is designed to leverage knowledge in the source domain with labels to help build classification models in the target domain where labels are scarce or even unavailable. Previous studies have shown that high-level concepts extracted from original features are more suitable for cross-domain classification tasks, so many transfer learning methods transfer knowledge by modeling high-level concepts on the original feature space. However, there are two limitations to this method: First, learning high-level concepts directly on the original feature space will reduce the proportion of shared information contained in common features in the process of knowledge transfer bridge construction. Second, only learning multiple high-level concepts on the original feature space, the latent shared information contained in the domain-specific features cannot be targeted learned, so the latent shared information in the domain-specific features cannot be effectively used. To overcome these limitations, this paper proposes a novel method named Dual-Space Transfer Learning based on an Indirect Mutual Promotion Strategy (DSTL). The DSTL method is formalized as an optimization problem based on non-negative matrix tri-factorization. DSTL first extracts the common features between domains and constructs the common feature space. Then, the learning of the high-level concepts of the common feature space and the original feature space is integrated through an indirect promotion strategy, which can enhance the learning effect of common features and domain-specific features through the mutual help of the two feature spaces. The system test on benchmark data sets shows the superiority of the DSTL method.
Journal Article
My invented life
During rehearsals for Shakespeare's \"As you like it,\" sixteen-year-old Roz, jealous of her cheerleader sister's acting skills and heartthrob boyfriend, invents a new identity, with unexpected results.
BOOK
Object-aware semantics of attention for image captioning
In image captioning, exploring the advanced semantic concepts is very important for boosting captioning performance. Although much progress has been made in this regard, most existing image captioning models usually neglect the interrelationships between objects in an image, which is a key factor of accurately understanding image content. In this paper, we propose the object-aware semantic attention object-aware semantic attention (OSA) based captioning model to address this issue. Specifically, our attention model allows the explicit associations between the objects by coupling the attention mechanism with three types of semantic concepts, i.e., the category information, relative sizes of the objects, and relative distances between objects. In reality, they are easily built up and seamlessly coupled with the well-known encoder-decoder captioning framework. In our empirical analysis, these semantic concepts favor different aspects of the image content like the number of the objects belonging to each category, the main focus of an image, and the closeness between the objects. Importantly, they are cooperated with visual features to help the attention model effectively highlight the image regions of interest for significant performance gains. By leveraging three types of semantic concepts, we derive four semantic attention models for image captioning. Extensive experiments on MSCOCO dataset show our attention models within the encoder-decoder image captioning framework perform favorably as compared to representative captioning models.
Journal Article
The Problems Of Management And Marketing Of High Technology Services
The article emphasizes the importance of high technologies sector development and complexity level it takes to reach it. Based on scientific literature analysis, including high technologies services features that involve short life cycle, inseparability from science and technologies, as well as existing infrastructure, to solve high technologies development issues, marketing and management methods should be applied. The specific features of the product of high technologies is the reason for high technologies services management and marketing problems that include high risk of business, exceptionally heavy investment and complicated launch of the product. Therefore, there is a need for other solutions, such as the ways to stimulate high technologies development and high technologies products intake and application that should be generated using management and marketing methods. [PUBLICATION ABSTRACT]
Journal Article
Spatial Relations Using High Level Concepts
Existing models of spatial relations do not consider that different concepts exist on different levels in a hierarchy and in turn that the spatial relations in a given scene are a function of the specific concepts considered. One approach to determining the existence of a particular spatial relation is to compute the corresponding high level concepts explicitly using map generalization before inferring the existence of the spatial relation in question. We explore this idea through the development of a model of the spatial relation “enters” that may exist between a road and a housing estate.
Journal Article
