Human-centric visual analysis plays a pivotal role in diverse applications, including surveillance, healthcare, and human-computer interaction. With the emergence of large-scale unlabeled human image datasets, there is an increasing need for a general unsupervised pre-training model capable of supporting diverse human-centric downstream tasks. To achieve this goal, we propose CLASP (CLIP-guided Adaptable Self-suPervised learning), a novel framework designed for unsupervised pre-training in human-centric visual tasks. CLASP leverages the powerful vision-language model CLIP to generate both low-level (e.g., body parts) and high-level (e.g., attributes) semantic pseudo-labels. These multi-level semantic cues are then integrated into the learned visual representations, enriching their expressiveness and generalizability. Recognizing that different downstream tasks demand varying levels of semantic granularity, CLASP incorporates a Prompt-Controlled Mixture-of-Experts (MoE) module. MoE dynamically adapts feature extraction based on task-specific prompts, mitigating potential feature conflicts and enhancing transferability. Furthermore, CLASP employs a multi-task pre-training strategy, where part- and attribute-level pseudo-labels derived from CLIP guide the representation learning process. Extensive experiments across multiple benchmarks demonstrate that CLASP consistently outperforms existing unsupervised pre-training methods, advancing the field of human-centric visual analysis.

翻译：以人为中心的视觉分析在监控、医疗保健和人机交互等多种应用中发挥着关键作用。随着大规模无标签人体图像数据集的涌现，亟需一种能够支持多样化以人为中心下游任务的通用无监督预训练模型。为实现这一目标，我们提出了CLASP（CLIP引导的可适应自监督学习），这是一个专为以人为中心的视觉任务设计的无监督预训练新框架。CLASP利用强大的视觉语言模型CLIP生成低级（如身体部位）和高级（如属性）语义伪标签。这些多层级语义线索随后被整合到学习到的视觉表征中，从而增强其表达能力和泛化性。考虑到不同下游任务需要不同粒度的语义信息，CLASP引入了一个提示控制专家混合模块。该模块能够根据任务特定提示动态调整特征提取过程，缓解潜在的特征冲突并提升可迁移性。此外，CLASP采用多任务预训练策略，其中源自CLIP的部位级和属性级伪标签共同指导表征学习过程。在多个基准测试上的大量实验表明，CLASP持续优于现有的无监督预训练方法，推动了以人为中心的视觉分析领域的发展。