Semantic-aware Contrastive Learning via Multi-prompt Alignment

Feb 6, 2025·
Zhuoran Zhao
Hao Qin
Hao Qin
,
Ming Kong
,
Luyuan Chen
,
Di Xie
,
Jiang Zhu
,
Qiang Zhu
· 1 min read
Abstract
This work studies semantic-aware positive sample generation for contrastive learning through multi-source and multi-modal prompt alignment. It uses large multimodal model capabilities to improve semantic consistency and sample diversity.
Type
Publication
Machine Learning
publications

The paper investigates how semantic consistency in generated positive samples affects representation learning.

Contrastive Learning Self-Supervised Learning Multimodal Learning
Hao Qin
Authors
Hao Qin
Ph.D. Student in Artificial Intelligence
I am a Ph.D. student in the College of Computer Science and Technology at Zhejiang University. My research focuses on 3D vision, 3D Gaussian Splatting, 3D-AIGC, and multi-agent systems, with broader interests in self-supervised representation learning and embodied visual content creation.