AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A prototype-driven scale-aware modeling multi-modal hashing framework that jointly models cross-modal interaction and hierarchical prototype learning in an end-to-end manner is proposed, enabling the learned hash codes to better preserve fine-grained and overlapping semantic structures commonly observed in real-world multimodal data.
Multi-modal hashing converts high-dimensional features into compact binary codes while preserving semantic similarity, thereby achieving high performance with lower storage and computational costs. However, existing methods generally overly depend on discrete supervisory information, ignoring the ambiguity of labels an...
Due to its efficient storage and fast retrieval capabilities, hashing methods have garnered significant attention in image retrieval applications. However, most existing methods rely on semantic similarity matrices to indicate whether two images are similar or not, which neglects the guiding role of semantic signals...
Although cross-modal hashing enables efficient large-scale retrieval by encoding multimodal data into compact binary representations, its fixed code length and binary nature impose a fundamental capacity constraint that hinders continual adaptation to growing data streams and emerging semantic concepts. Existing contin...
Due to its computational and storage efficiency, image hashing has emerged as a significant research approach for fine-grained image retrieval (FGIR). Since fine-grained images exhibit minimal inter-class variance and significant intra-class diversity, existing image hashing methods typically employ multiple local bran...
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