PriorCLIP: Visual Prior Guided Vision-Language Model for Remote Sensing Image-Text Retrieval
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866915487229673472 |
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| author | Pan, Jiancheng Ma, Muyuan Ma, Qing Bai, Cong Chen, Shengyong |
| author_facet | Pan, Jiancheng Ma, Muyuan Ma, Qing Bai, Cong Chen, Shengyong |
| contents | 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. For the open-domain setting, we design a two-stage prior representation learning strategy, consisting of large-scale pre-training on coarse-grained image-text pairs, followed by fine-tuning on fine-grained pairs using vision-instruction, which enables robust retrieval across long-tail concepts and vocabulary shifts. Furthermore, a cluster-based symmetric contrastive Attribution Loss is proposed to constrain inter-class relations and alleviate semantic confusion in the shared embedding space. Extensive experiments on RSICD and RSITMD benchmarks demonstrate that PriorCLIP achieves substantial improvements, outperforming existing methods by 4.9% and 4.0% in closed-domain retrieval, and by 7.3% and 9.4% in open-domain retrieval, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10160 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PriorCLIP: Visual Prior Guided Vision-Language Model for Remote Sensing Image-Text Retrieval Pan, Jiancheng Ma, Muyuan Ma, Qing Bai, Cong Chen, Shengyong Computer Vision and Pattern Recognition Artificial Intelligence 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. For the open-domain setting, we design a two-stage prior representation learning strategy, consisting of large-scale pre-training on coarse-grained image-text pairs, followed by fine-tuning on fine-grained pairs using vision-instruction, which enables robust retrieval across long-tail concepts and vocabulary shifts. Furthermore, a cluster-based symmetric contrastive Attribution Loss is proposed to constrain inter-class relations and alleviate semantic confusion in the shared embedding space. Extensive experiments on RSICD and RSITMD benchmarks demonstrate that PriorCLIP achieves substantial improvements, outperforming existing methods by 4.9% and 4.0% in closed-domain retrieval, and by 7.3% and 9.4% in open-domain retrieval, respectively. |
| title | PriorCLIP: Visual Prior Guided Vision-Language Model for Remote Sensing Image-Text Retrieval |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2405.10160 |