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Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning
Zero-shot learning (ZSL) addresses the unseen class recognition problem ...
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Leveraging Semantic Embeddings for Safety-Critical Applications
Semantic Embeddings are a popular way to represent knowledge in the fiel...
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Semantic Softmax Loss for Zero-Shot Learning
A typical pipeline for Zero-Shot Learning (ZSL) is to integrate the visu...
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Zero-Shot Learning with Common Sense Knowledge Graphs
Zero-shot learning relies on semantic class representations such as attr...
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Augmenting Neural Nets with Symbolic Synthesis: Applications to Few-Shot Learning
We propose symbolic learning as extensions to standard inductive learnin...
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Semantic Graph-enhanced Visual Network for Zero-shot Learning
Zero-shot learning uses semantic attributes to connect the search space ...
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Visual-Semantic Embedding Model Informed by Structured Knowledge
We propose a novel approach to improve a visual-semantic embedding model...
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Zero-Shot Learning with Knowledge Enhanced Visual Semantic Embeddings
We improve zero-shot learning (ZSL) by incorporating common-sense knowledge in DNNs. We propose Common-Sense based Neuro-Symbolic Loss (CSNL) that formulates prior knowledge as novel neuro-symbolic loss functions that regularize visual-semantic embedding. CSNL forces visual features in the VSE to obey common-sense rules relating to hypernyms and attributes. We introduce two key novelties for improved learning: (1) enforcement of rules for a group instead of a single concept to take into account class-wise relationships, and (2) confidence margins inside logical operators that enable implicit curriculum learning and prevent premature overfitting. We evaluate the advantages of incorporating each knowledge source and show consistent gains over prior state-of-art methods in both conventional and generalized ZSL e.g. 11.5 and 11.6
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