Adaptive Semantic-visual Tree For Hierarchical Embeddings

Abstract

Merchandise categories inherently form a semantic hierarchy with different levels of concept abstraction, especially for fine-grained categories. This hierarchy encodes rich correlations among various categories across different levels, which can effectively regularize the semantic space and thus make predictions less ambiguous. However, previous studies of fine-grained image retrieval primarily focus on semantic similarities or visual similarities. In a real application, merely using visual similarity may not satisfy the need of consumers to search merchandise with real-life images, e.g., given a red coat as a query image, we might get a red suit in recall results only based on visual similarity since they are visually similar. But the users actually want a coat rather than suit even the coat is with different color or texture attributes. We introduce this new problem based on photoshopping in real practice. That's why semantic information are integrated to regularize the margins to m

Stats

• citations11
• S2 citations—
• github stars0
• HF likes0
• heat score8.09
• arxiv keyyang2020adaptive

Related papers

• Integrating Visual And Semantic Similarity Using Hierarchies For Image Retrieval (2023)4.52
• Learning Visual Hierarchies In Hyperbolic Space For Image Retrieval (2024)0.00
• Hierarchy-based Image Embeddings For Semantic Image Retrieval (2018)13.84
• Semantic Hierarchy Preserving Deep Hashing For Large-scale Image Retrieval (2019)5.24
• Hierarchical Similarity Learning For Language-based Product Image Retrieval (2021)6.93
• Automatic Spatially-aware Fashion Concept Discovery (2017)16.82
• Cross-modal Hierarchical Modelling For Fine-grained Sketch Based Image Retrieval (2020)6.77
• From Pixels To Purchase: Building And Evaluating A Taxonomy-decoupled Visual Search Engine For Home Goods E-commerce (2026)0.00