인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Text-image cross-model matching is a core challenge in multimodal machine learning, aiming to enable efficient retrieval of images and texts across different modalities. The difficulty in this task stems from the inherent gap between text and image representations, which can lead to suboptimal retrieval performance. Traditional approaches attempt to learn a shared representation space where both image and text can be directly compared. However, they often fail to account for the varying levels of semantic information captured in different layers of the encoders, resulting in inadequate alignment between the modalities. To address these limitations, we propose a novel approach called P rogressive M ulti- S ubspace F usion, dubbed PMSF for text-image matching. Our model reduces the model gap by using a progressive learning process, starting with shallow representations and moving to deeper layers. We use a dual-tower structure to encode multi-level features for both image and text, which are then mapped to corresponding auxiliary subspaces. These subspaces are fused through an adaptive GPO pooling strategy, enabling joint learning of a shared representation space. Experimental results on benchmark datasets, including Flickr30K and MSCOCO, show that PMSF significantly improves retrieval performance, achieving a Rsum score of 516.9 and 510.7, outperforming 23 state-of-the-art methods.
#Computer science
#Artificial intelligence
#Linear subspace
#Representation (politics)
#Benchmark (surveying)
#Pooling
#Image (mathematics)
#Pattern recognition (psychology)
#Matching (statistics)
#Subspace topology
#Task (project management)
#Natural language processing
#Machine learning
#Mathematics
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