From Data Deluge to Data Curation: A Filtering-WoRA Paradigm for Efficient Text-based Person Search

• URL: http://arxiv.org/abs/2404.10292v1
• Date: Tue, 16 Apr 2024 05:29:14 GMT
• Title: From Data Deluge to Data Curation: A Filtering-WoRA Paradigm for Efficient Text-based Person Search
• Authors: Jintao Sun, Zhedong Zheng, Gangyi Ding,
• Abstract summary: In text-based person search endeavors, data generation has emerged as a prevailing practice, addressing concerns over privacy preservation and the arduous task of manual annotation. We observe that only a subset of the data in constructed datasets plays a decisive role. We introduce a new Filtering-WoRA paradigm, which contains a filtering algorithm to identify this crucial data subset and WoRA learning strategy for light fine-tuning.
• Score: 19.070305201045954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In text-based person search endeavors, data generation has emerged as a prevailing practice, addressing concerns over privacy preservation and the arduous task of manual annotation. Although the number of synthesized data can be infinite in theory, the scientific conundrum persists that how much generated data optimally fuels subsequent model training. We observe that only a subset of the data in these constructed datasets plays a decisive role. Therefore, we introduce a new Filtering-WoRA paradigm, which contains a filtering algorithm to identify this crucial data subset and WoRA (Weighted Low-Rank Adaptation) learning strategy for light fine-tuning. The filtering algorithm is based on the cross-modality relevance to remove the lots of coarse matching synthesis pairs. As the number of data decreases, we do not need to fine-tune the entire model. Therefore, we propose a WoRA learning strategy to efficiently update a minimal portion of model parameters. WoRA streamlines the learning process, enabling heightened efficiency in extracting knowledge from fewer, yet potent, data instances. Extensive experimentation validates the efficacy of pretraining, where our model achieves advanced and efficient retrieval performance on challenging real-world benchmarks. Notably, on the CUHK-PEDES dataset, we have achieved a competitive mAP of 67.02% while reducing model training time by 19.82%.

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