Year: 2025
Type: conference-paper
Abstract: The rapid growth of multimodal data applications has introduced significant challenges in efficient storage and cross-modal retrieval. While existing approaches such as common subspace learning, deep feature extraction, and hashing techniques have made progress, they still face critical limitations in computational efficiency and retrieval performance. Recent advances using Vision-Language Pretrained models (e.g., CLIP with Dual Query Unification for Compositional Image Retrieval, DQU-CIR) have shown promise but remain computationally intensive.
Authors: Li Zheng, Yufan Zhao, Xiao Sun
Institution: Harbin Engineering University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414307404