Text-based Person Search (TBPS) aims to retrieve the person images using natural language descriptions. Recently, Contrastive Language Image Pretraining (CLIP), a universal large cross-modal vision-language pre-training model, has remarkably performed over various cross-modal downstream tasks due to its powerful cross-modal semantic learning capacity. TPBS, as a fine-grained cross-modal retrieval task, is also facing the rise of research on the CLIP-based TBPS. In order to explore the potential of the visual-language pre-training model for downstream TBPS tasks, this paper makes the first attempt to conduct a comprehensive empirical study of CLIP for TBPS and thus contribute a straightforward, incremental, yet strong TBPS-CLIP baseline to the TBPS community. We revisit critical design considerations under CLIP, including data augmentation and loss function. The model, with the aforementioned designs and practical training tricks, can attain satisfactory performance without any sophisticated modules. Also, we conduct the probing experiments of TBPS-CLIP in model generalization and model compression, demonstrating the effectiveness of TBPS-CLIP from various aspects. This work is expected to provide empirical insights and highlight future CLIP-based TBPS research.

翻译：文本行人检索旨在通过自然语言描述检索行人图像。近年来，对比语言-图像预训练（CLIP）作为一种通用的跨模态视觉-语言大模型，凭借其强大的跨模态语义学习能力，在各类跨模态下游任务中表现卓越。作为细粒度跨模态检索任务，文本行人检索也正迎来基于CLIP的研究热潮。为探究视觉-语言预训练模型在文本行人检索下游任务中的潜力，本文首次对CLIP在文本行人检索中的应用展开全面的实证研究，为文本行人检索领域提供了一个简洁、渐进但性能强劲的TBPS-CLIP基线模型。我们重新审视了CLIP框架下的关键设计考量，包括数据增强与损失函数。该模型在采用上述设计及实用训练技巧后，无需复杂模块即可取得令人满意的性能。此外，我们还通过模型泛化与模型压缩的探测实验，从多维度验证了TBPS-CLIP的有效性。本工作旨在为后续基于CLIP的文本行人检索研究提供实证见解与启发。