Triplet Synthesis For Enhancing Composed Image Retrieval via Counterfactual Image Generation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909465108807680 |
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| author | Uesugi, Kenta Saito, Naoki Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki |
| author_facet | Uesugi, Kenta Saito, Naoki Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki |
| contents | Composed Image Retrieval (CIR) provides an effective way to manage and access large-scale visual data. Construction of the CIR model utilizes triplets that consist of a reference image, modification text describing desired changes, and a target image that reflects these changes. For effectively training CIR models, extensive manual annotation to construct high-quality training datasets, which can be time-consuming and labor-intensive, is required. To deal with this problem, this paper proposes a novel triplet synthesis method by leveraging counterfactual image generation. By controlling visual feature modifications via counterfactual image generation, our approach automatically generates diverse training triplets without any manual intervention. This approach facilitates the creation of larger and more expressive datasets, leading to the improvement of CIR model's performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13968 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Triplet Synthesis For Enhancing Composed Image Retrieval via Counterfactual Image Generation Uesugi, Kenta Saito, Naoki Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Composed Image Retrieval (CIR) provides an effective way to manage and access large-scale visual data. Construction of the CIR model utilizes triplets that consist of a reference image, modification text describing desired changes, and a target image that reflects these changes. For effectively training CIR models, extensive manual annotation to construct high-quality training datasets, which can be time-consuming and labor-intensive, is required. To deal with this problem, this paper proposes a novel triplet synthesis method by leveraging counterfactual image generation. By controlling visual feature modifications via counterfactual image generation, our approach automatically generates diverse training triplets without any manual intervention. This approach facilitates the creation of larger and more expressive datasets, leading to the improvement of CIR model's performance. |
| title | Triplet Synthesis For Enhancing Composed Image Retrieval via Counterfactual Image Generation |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2501.13968 |