DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning

Previous multimodal sentence representation learning methods have achieved impressive performance.However, most approaches focus on aligning images and text at a coarse level, facing two critical challenges: cross-modal misalignment bias and intra-modal semantic divergence, which significantly degrade sentence representation quality.To address these challenges, we propose DALR (Dual-level Alignment Learning for Multimodal Sentence Representation).For cross-modal alignment, we propose a consistency learning module that softens negative samples and utilizes semantic similarity from an auxiliary task to achieve finegrained cross-modal alignment.Additionally, we contend that sentence relationships go beyond binary positive-negative labels, exhibiting a more intricate ranking structure.To better capture these relationships and enhance representation quality, we integrate ranking distillation with global intra-modal alignment learning.Comprehensive experiments on semantic textual similarity (STS) and transfer (TR) tasks validate the effectiveness of our approach, consistently demonstrating its superiority over state-of-the-art baselines.