2 resultados para Idols and images
em DRUM (Digital Repository at the University of Maryland)
Resumo:
Music played a prominent role in the United States women’s suffrage movement (1848–1920). Suffragists left behind hundreds of compositions supporting their cause and historical accounts indicate that musical performances were common at suffrage events. With only a few exceptions, scholars have disregarded the music used in this movement, and have underemphasized its significance. This study examines the use of music in the suffrage movement from three perspectives: music with lyrics, titles, and images that espouse women’s enfranchisement; music performed at national suffrage conventions held by the National American Woman Suffrage Association; and music accompanying suffrage parades. Though the music used varies in each case, it is clear that music played an important role in unifying suffragists and underscoring the ideals and goals of the movement.
Resumo:
A computer vision system that has to interact in natural language needs to understand the visual appearance of interactions between objects along with the appearance of objects themselves. Relationships between objects are frequently mentioned in queries of tasks like semantic image retrieval, image captioning, visual question answering and natural language object detection. Hence, it is essential to model context between objects for solving these tasks. In the first part of this thesis, we present a technique for detecting an object mentioned in a natural language query. Specifically, we work with referring expressions which are sentences that identify a particular object instance in an image. In many referring expressions, an object is described in relation to another object using prepositions, comparative adjectives, action verbs etc. Our proposed technique can identify both the referred object and the context object mentioned in such expressions. Context is also useful for incrementally understanding scenes and videos. In the second part of this thesis, we propose techniques for searching for objects in an image and events in a video. Our proposed incremental algorithms use the context from previously explored regions to prioritize the regions to explore next. The advantage of incremental understanding is restricting the amount of computation time and/or resources spent for various detection tasks. Our first proposed technique shows how to learn context in indoor scenes in an implicit manner and use it for searching for objects. The second technique shows how explicitly written context rules of one-on-one basketball can be used to sequentially detect events in a game.