仅用文本数据就能检测图像中的不良内容，无需人工标注。
Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection
- 用合成文本+预训练视觉语言模型，无需真实图像即可训练检测器。
- 在异常图像和仇恨图像检测任务中表现优于传统方法。
- 适合需要快速部署内容安全检测系统的团队使用。
本研究探索了视觉数据集中潜在不良内容的检测方法。理论分析表明，仅通过文本数据即可获得有效的视觉数据分割模型。基于此，我们提出无痛文本训练（HFTT），一种仅需合成文本数据与预训练视觉语言模型即可构建不良视觉内容检测器的简化方法。HFTT采用创新的目标函数，大幅减少人工标注需求；同时设计巧妙的文本数据生成策略，低成本模拟未知视觉分布的融合。其独特性使HFTT不仅适用于传统分布外检测，还可扩展至抽象概念任务。我们在分布外检测与仇恨图像检测任务上进行了实验验证，结果表明该方法有效。代码已开源：https://github.com/Saehyung-Lee/HFTT。
原文摘要 · Abstract (English)
In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://github.com/Saehyung-Lee/HFTT
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