对比通用模型与人脸专用模型，发现后者更优但融合可进一步提升性能。
Foundation versus Domain-specific Models: Performance Comparison, Fusion, and Explainability in Face Recognition
- 用通用模型和专用模型在多个数据集上对比测试
- 专用模型整体表现更好，但通用模型在模糊人脸上表现更佳
- 简单融合能提升低误报率下的准确率，且可解释识别结果
本文探讨了通用基础模型（如CLIP、BLIP、GPT-4o、Grok-4）与领域专用的人脸识别模型（如AdaFace或ArcFace）在人脸识别任务中的表现差异。通过多组实验和多个基准数据集验证，得出以下结论：（a）在所有考虑的人脸基准数据集中，专用模型均优于零样本通用模型；（b）通用模型在过分割人脸图像上的表现优于紧密裁剪的人脸图像，表明上下文线索的重要性；（c）将通用模型与专用模型在分数层面进行简单融合，可在低误匹配率下提升准确率；（d）GPT-4o和Grok-4等基础模型能够为识别过程提供可解释性，在某些情况下甚至可纠正AdaFace的低置信度判断。这再次强调了合理结合专用模型与通用模型的价值。
原文摘要 · Abstract (English)
In this paper, we address the following question: How do generic foundation models (e.g., CLIP, BLIP, GPT-4o, Grok-4) compare against a domain-specific face recognition model (viz., AdaFace or ArcFace) on the face recognition task? Through a series of experiments involving several foundation models and benchmark datasets, we report the following findings: (a) In all face benchmark datasets considered, domain-specific models outperformed zero-shot foundation models. (b) The performance of zero-shot generic foundation models improved on over-segmented face images compared to tightly cropped faces, thereby suggesting the importance of contextual clues. (c) A simple score-level fusion of a foundation model with a domain-specific face recognition model improved the accuracy at low false match rates. (d) Foundation models, such as GPT-4o and Grok-4, are able to provide explainability to the face recognition pipeline. In some instances, foundation models are even able to resolve low-confidence decisions made by AdaFace, thereby reiterating the importance of combining domain-specific face recognition models with generic foundation models in a judicious manner.
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