Abstract: Recently,supervised cross-modal retrieval techniques have garnered significant attention.Based on sample-level semantic relationships,existing methods primarily focus on assessing the sample-wise similarity while neglecting the potential impact of label distribution on improving retrieval performance.Furthermore,existing approaches still face challenges related to inaccurate feature extraction and sluggish processing rates.To address this problems,we introduce a new method,termed FLIP-based joint similarity preserving hashing(FJSPH),for cross-modal retrieval.Specifically,we leverage the fast language image pre-training model(FLIP) to extract more accurate cross-modal features.To further reduce the cross-modal semantic differences,we attempt to enhance modal interaction and refine modal semantic representation through multimodal comparative learning.In addition,we use sample-wise similarity and cluster-wise similarity to further exploit the semantic correlation between different modalities.This approach ensures that samples sharing similar semantics are positioned closer together in Hamming space,thereby producing more distinctive hash codes.The experimental results on three cross-modal datasets indicate that the FJSPH approach exhibits excellent retrieval performance in cross-modal retrieval.

Key words: Joint similarity preserving, Fast language-image pre-trained model, Cross-modal retrieval, Sample-wise similarity, Cluster-wise similarity