Summary of Pir: Remote Sensing Image-text Retrieval with Prior Instruction Representation Learning, by Jiancheng Pan et al.
PIR: Remote Sensing Image-Text Retrieval with Prior Instruction Representation Learning
by Jiancheng Pan, Muyuan Ma, Qing Ma, Cong Bai, Shengyong Chen
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach is proposed to improve remote sensing image-text retrieval tasks by introducing a prior instruction representation (PIR) learning paradigm. This paradigm draws on prior knowledge to instruct adaptive learning of vision and text representations, addressing semantic noise issues in vision-language understanding tasks. The PIR-ITR framework is designed for domain-adapted remote sensing image-text retrieval, while the open-domain retrieval task is further developed with massive additional data pre-training the vision-language foundation model. To address semantic noise in remote sensing vision-language representations and improve open-domain retrieval performance, a domain-specific CLIP-based framework called PIR-CLIP is proposed. This framework utilizes prior-guided knowledge of remote sensing scene recognition to select key features for reducing the impact of semantic noise, and applies cyclic activation to enhance text representation capability. A cluster-wise Affiliation Loss (AL) is also proposed to constrain inter-classes and reduce semantic confusion zones in the common subspace. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to improve remote sensing image-text retrieval tasks by using prior knowledge to learn better vision and text representations. It proposes a framework called PIR-ITR that helps with this task, especially for images from remote sensing scenes. The authors also suggest a new approach to open-domain retrieval, which involves pre-training the foundation model with massive additional data. To further improve performance, they propose a special version of this approach just for remote sensing, called PIR-CLIP. This framework uses prior knowledge to select important features and make text representations better. It also includes a way to reduce mistakes caused by semantic noise. |
Keywords
» Artificial intelligence » Language understanding
