ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search
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| Format: | Preprint |
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2026
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| _version_ | 1866911739522580480 |
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| author | Xie, Zequn Jia, Xibei Cai, Sihang Wang, Shulei Jin, Tao |
| author_facet | Xie, Zequn Jia, Xibei Cai, Sihang Wang, Shulei Jin, Tao |
| contents | Text-Based Person Search (TBPS) aims to retrieve pedestrian images using natural language queries. However, existing TBPS models, especially those based on CLIP, struggle with fine-grained understanding due to global representational bias and semantic sparsity inherited from training on short captions. This results in weak fine-grained alignment, exacerbated by the scarcity of region-level annotations. To address this, we propose ROGLE (Robust Global-Local Embedding), a unified framework that overcomes reliance on costly manual annotations through an automated Region-to-Sentence Matching (RSM) strategy. RSM automatically mines pseudo region-sentence pairs for scalable fine-grained supervision. Furthermore, ROGLE employs a multi-granular learning strategy that fuses global contrastive learning with region-level local alignment. We also introduce the P-VLG Benchmark, a large-scale dataset constructed by curating and enriching images from established public benchmarks. It features over 100,000 annotated regions and rich long-form captions, making it the first TBPS benchmark to support both global and local assessment protocols. Extensive experiments show that ROGLE significantly outperforms existing approaches, particularly on challenging long-form queries. Code and the P-VLG benchmark will be made publicly available. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2606_01825 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search Xie, Zequn Jia, Xibei Cai, Sihang Wang, Shulei Jin, Tao Computer Vision and Pattern Recognition Multimedia Text-Based Person Search (TBPS) aims to retrieve pedestrian images using natural language queries. However, existing TBPS models, especially those based on CLIP, struggle with fine-grained understanding due to global representational bias and semantic sparsity inherited from training on short captions. This results in weak fine-grained alignment, exacerbated by the scarcity of region-level annotations. To address this, we propose ROGLE (Robust Global-Local Embedding), a unified framework that overcomes reliance on costly manual annotations through an automated Region-to-Sentence Matching (RSM) strategy. RSM automatically mines pseudo region-sentence pairs for scalable fine-grained supervision. Furthermore, ROGLE employs a multi-granular learning strategy that fuses global contrastive learning with region-level local alignment. We also introduce the P-VLG Benchmark, a large-scale dataset constructed by curating and enriching images from established public benchmarks. It features over 100,000 annotated regions and rich long-form captions, making it the first TBPS benchmark to support both global and local assessment protocols. Extensive experiments show that ROGLE significantly outperforms existing approaches, particularly on challenging long-form queries. Code and the P-VLG benchmark will be made publicly available. |
| title | ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2606.01825 |