Gentle-CLIP: Exploring Aligned Semantic In Low-Quality Multimodal Data With Soft Alignment

• URL: http://arxiv.org/abs/2406.05766v1
• Date: Sun, 9 Jun 2024 12:41:14 GMT
• Title: Gentle-CLIP: Exploring Aligned Semantic In Low-Quality Multimodal Data With Soft Alignment
• Authors: Zijia Song, Zelin Zang, Yelin Wang, Guozheng Yang, Jiangbin Zheng, Kaicheng yu, Wanyu Chen, Stan Z. Li,
• Abstract summary: Multimodal fusion breaks through the barriers between diverse modalities and has already yielded numerous impressive performances. In various specialized fields, it is struggling to obtain sufficient alignment data for the training process. We transform semi-supervised multimodal alignment into a manifold matching problem and propose a new method based on CLIP, named Gentle-CLIP.
• Score: 38.38565939595014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal fusion breaks through the barriers between diverse modalities and has already yielded numerous impressive performances. However, in various specialized fields, it is struggling to obtain sufficient alignment data for the training process, which seriously limits the use of previously elegant models. Thus, semi-supervised learning attempts to achieve multimodal alignment with fewer matched pairs but traditional methods like pseudo-labeling are difficult to apply in domains with no label information. To address these problems, we transform semi-supervised multimodal alignment into a manifold matching problem and propose a new method based on CLIP, named Gentle-CLIP. Specifically, we design a novel semantic density distribution loss to explore implicit semantic alignment information from unpaired multimodal data by constraining the latent representation distribution with fine granularity, thus eliminating the need for numerous strictly matched pairs. Meanwhile, we introduce multi-kernel maximum mean discrepancy as well as self-supervised contrastive loss to pull separate modality distributions closer and enhance the stability of the representation distribution. In addition, the contrastive loss used in CLIP is employed on the supervised matched data to prevent negative optimization. Extensive experiments conducted on a range of tasks in various fields, including protein, remote sensing, and the general vision-language field, demonstrate the effectiveness of our proposed Gentle-CLIP.

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