PriorCLIP: Visual Prior Guided Vision-Language Model for Remote Sensing Image-Text Retrieval

Understand

Remote sensing image-text retrieval plays a crucial role in remote sensing interpretation, yet remains challenging under both closed-domain and open-domain scenarios due to semantic noise and domain shifts.

• To address these issues, we propose a visual prior-guided vision-language model, PriorCLIP, which leverages visual priors for unbiased representation learning and adaptive vision-language alignment.
• In the closed-domain setting, PriorCLIP introduces two Progressive Attention Encoder (PAE) structures: Spatial-PAE constructs a belief matrix with instruction embeddings to filter key features and mitigate semantic bias.
• At the same time, Temporal-PAE exploits cyclic activation across time steps to enhance text representation.