CFPL-FAS: Class Free Prompt Learning for Generalizable Face
  Anti-spoofing
Ajian Liu Shuai Xue Jianwen Gan Jun Wan Yanyan Liang Jiankang Deng Sergio Escalera Zhen Lei
Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the
model's performance on unseen domains. Existing methods either rely on domain
labels to align domain-invariant feature spaces, or disentangle generalizable
features from the whole sample, which inevitably lead to the distortion of
semantic feature structures and achieve limited generalization. In this work,
we make use of large-scale VLMs like CLIP and leverage the textual feature to
dynamically adjust the classifier's weights for exploring generalizable visual
features. Specifically, we propose a novel Class Free Prompt Learning (CFPL)
paradigm for DG FAS, which utilizes two lightweight transformers, namely
Content Q-Former (CQF) and Style Q-Former (SQF), to learn the different
semantic prompts conditioned on content and style features by using a set of
learnable query vectors, respectively. Thus, the generalizable prompt can be
learned by two improvements: (1) A Prompt-Text Matched (PTM) supervision is
introduced to ensure CQF learns visual representation that is most informative
of the content description. (2) A Diversified Style Prompt (DSP) technology is
proposed to diversify the learning of style prompts by mixing feature
statistics between instance-specific styles. Finally, the learned text features
modulate visual features to generalization through the designed Prompt
Modulation (PM). Extensive experiments show that the CFPL is effective and
outperforms the state-of-the-art methods on several cross-domain datasets.