Learning Adaptive Representations for Image Retrieval and Recognition

Content-based image retrieval is a core problem in computer vision. It has a wide range of application such as object and place recognition, digital library search, organizing image collections, and 3D reconstruction. However, robust and accurate image retrieval from a large-scale image collection still remains an open problem. For particular instance retrieval, challenges come not only from photometric and geometric changes between the query and the database images, but also from severe visual overlap with irrelevant images. On the other hand, large intra-class variation and inter-class similarity between semantic categories represents a major obstacle in semantic image retrieval and recognition. This dissertation explores learning image representations that adaptively focus on specific image content to tackle these challenges. For this purpose, three kinds of image contexts for discriminating relevant and irrelevant image content are exploited: (1) local image context, (2) semi-global image context, and (3) global image context. Novel models for learning adaptive image representations based on each context are introduced. Moreover, as a byproduct of training the proposed models, the underlying task-relevant contexts are automatically revealed from the data in a self-supervised manner. These include data-driven notion of good local mid-level features, task-relevant semi-global contexts with rich high-level information, and the hierarchy of images. Experimental evaluation illustrates the superiority of the proposed methods in the applications of place recognition, scene categorization, and particular object retrieval.Doctor of Philosoph

Violence, Environmental Crisis, and Human Sacrifice Among the Moche Culture

Throughout the ancient world, there have been numerous occurrences of human sacrifice performed by ancient cultures that have been proven in the archaeological record. The scope of this research will focus on the Moche culture that dominated the majority of the North Coast of ancient Peru during the Early Intermediate Period (200-850 CE). Understanding the role of human sacrifice in Moche civilization is directly related to understanding human sacrifice as a whole. Human sacrifice was also seen in later, well-documented societies; however, this religious practice did not originate with either of these societies but has a larger cultural significance in the history of Peru. Through review of relevant literature, iconographic interpretation, ethnohistoric analogies, and archaeological and osteological analysis, this research reveals how the Moche elite and priests utilized human sacrifice to affirm and advance their religious and sociopolitical ideologies to maintain order over internal and neighboring rival polities

ProTeCt: Prompt Tuning for Hierarchical Consistency

Large visual-language models, like CLIP, learn generalized representations and have shown promising zero-shot performance. Few-shot adaptation methods, based on prompt tuning, have also been shown to further improve performance on downstream datasets. However, these models are not hierarchically consistent. Frequently, they infer incorrect labels at coarser taxonomic class levels, even when the inference at the leaf level (original class labels) is correct. This is problematic, given their support for open set classification and, in particular, open-grained classification, where practitioners define label sets at various levels of granularity. To address this problem, we propose a prompt tuning technique to calibrate the hierarchical consistency of model predictions. A set of metrics of hierarchical consistency, the Hierarchical Consistent Accuracy (HCA) and the Mean Treecut Accuracy (MTA), are first proposed to benchmark model performance in the open-granularity setting. A prompt tuning technique, denoted as Prompt Tuning for Hierarchical Consistency (ProTeCt), is then proposed to calibrate classification across all possible label set granularities. Results show that ProTeCt can be combined with existing prompt tuning methods to significantly improve open-granularity classification performance without degradation of the original classification performance at the leaf level

Propaganda and Persuasion in Imperial and Contemporary China

Paper by Arthur F. Wrigh

Dressed for Work: The Sartorial Representations of Working Women in Early 21st-Century American Primetime Dramas

The present study is theoretically located in the field of critical feminist studies of the representation of women in the mass media. It discusses the ways in which working women characters construct and express their occupational identity in selected American primetime TV dramas of the early 21st century. The observed strategies, which range from highly restricted self-expression to unbridled sartorial liberty, appear to be heavily correlated with the prestige of the presented occupations and their levels of masculinization/feminization. Moreover, the self-limiting sartorial choices of high-achieving professional women, frequently containing their femininity, result from the competitive nature of prestigious yet traditionally male-gendered occupations. However, it is also pointed out that working women are generally depicted as determined to accentuate the physical aspects of their femininity regardless of the established dress code or traditional gendering of their occupations. Thus, the sartorial choices made by the female characters at the workplace serve in the analyzed TV shows as symbolic manifestations of women’s growing confidence as players on the job market in their own right

Spartan Daily, April 28, 1978

Volume 70, Issue 55https://scholarworks.sjsu.edu/spartandaily/6347/thumbnail.jp