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Towards Learning Compact Visual Embeddings using Deep Neural Networks

Abstract Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this direction, where the similarity in the embedding space is measured in terms of the cosine similarity. Such language embedding models have seen numerous applications in both language and vision community as they capture the information in the modality (English language) efficiently. Inspired by these language models, this work focuses on learning embedding spaces for two visual computing tasks, 1. Image Hashing 2. Zero Shot Learning. The training set was used to learn embedding spaces over which similarity/dissimilarity is measured using ... (more)
Created Date 2019
Contributor Gattupalli, Jaya Vijetha Reddy (Author) / Li, Baoxin (Advisor) / Yang, Yezhou (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Subject Computer science / Image Embeddings / Image Hashing / Weakly Supervised Learning
Type Masters Thesis
Extent 72 pages
Language English
Note Masters Thesis Computer Engineering 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis