15M Multimodal Facial Image-Text Dataset

• URL: http://arxiv.org/abs/2407.08515v2
• Date: Fri, 12 Jul 2024 01:19:33 GMT
• Title: 15M Multimodal Facial Image-Text Dataset
• Authors: Dawei Dai, YuTang Li, YingGe Liu, Mingming Jia, Zhang YuanHui, Guoyin Wang,
• Abstract summary: FaceCaption-15M comprises over 15 million pairs of facial images and their corresponding natural language descriptions of facial features. We conducted a comprehensive analysis of image quality, text naturalness, text complexity, and text-image relevance to demonstrate the superiority of FaceCaption-15M.
• Score: 5.552727861734425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently, image-text-driven multi-modal deep learning models have demonstrated their outstanding potential in many fields. In practice, tasks centered around facial images have broad application prospects. This paper presents \textbf{FaceCaption-15M}, a large-scale, diverse, and high-quality dataset of facial images accompanied by their natural language descriptions (facial image-to-text). This dataset aims to facilitate a study on face-centered tasks. FaceCaption-15M comprises over 15 million pairs of facial images and their corresponding natural language descriptions of facial features, making it the largest facial image-caption dataset to date. We conducted a comprehensive analysis of image quality, text naturalness, text complexity, and text-image relevance to demonstrate the superiority of FaceCaption-15M. To validate the effectiveness of FaceCaption-15M, we first trained a facial language-image pre-training model (FLIP, similar to CLIP) to align facial image with its corresponding captions in feature space. Subsequently, using both image and text encoders and fine-tuning only the linear layer, our FLIP-based models achieved state-of-the-art results on two challenging face-centered tasks. The purpose is to promote research in the field of face-related tasks through the availability of the proposed FaceCaption-15M dataset. All data, codes, and models are publicly available. https://huggingface.co/datasets/OpenFace-CQUPT/FaceCaption-15M

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