轻量级多模态模型，精准识别含隐喻或文化梗的有害表情包。
MemeBLIP2: A novel lightweight multimodal system to detect harmful memes
- 融合图像与文本特征，通过共享空间对齐提升检测能力
- 在PrideMM数据集上实现更优有害内容识别效果
- 适合社交媒体安全、内容审核等实际应用
表情包常结合图像与简短文字传递幽默或观点，但部分包含仇恨言论等有害信息。本文提出MemeBLIP2，一种轻量级多模态系统，通过将图像与文本表示对齐至共享空间并融合，有效检测有害表情包。基于BLIP-2构建核心模型，在PrideMM数据集上验证，该系统能捕捉模态中的细微线索，即使面对讽刺或文化特定内容也表现良好，显著提升有害内容检测性能。
原文摘要 · Abstract (English)
Memes often merge visuals with brief text to share humor or opinions, yet some memes contain harmful messages such as hate speech. In this paper, we introduces MemeBLIP2, a light weight multimodal system that detects harmful memes by combining image and text features effectively. We build on previous studies by adding modules that align image and text representations into a shared space and fuse them for better classification. Using BLIP-2 as the core vision-language model, our system is evaluated on the PrideMM datasets. The results show that MemeBLIP2 can capture subtle cues in both modalities, even in cases with ironic or culturally specific content, thereby improving the detection of harmful material.
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