CLIP-Guided Adaptable Self-Supervised Learning for Human-Centric Visual Tasks

• URL: http://arxiv.org/abs/2601.13133v1
• Date: Mon, 19 Jan 2026 15:19:28 GMT
• Title: CLIP-Guided Adaptable Self-Supervised Learning for Human-Centric Visual Tasks
• Authors: Mingshuang Luo, Ruibing Hou, Bo Chao, Hong Chang, Zimo Liu, Yaowei Wang, Shiguang Shan,
• Abstract summary: We propose CLASP (CLIP-guided Adaptable Self-suPervised learning), a novel framework for unsupervised pre-training in human-centric visual tasks.<n> 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.<n>MoE dynamically adapts feature extraction based on task-specific prompts, mitigating potential feature conflicts and enhancing transferability.
• Score: 76.00315860962885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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