Memes are a dominant medium for online communication and manipulation because meaning emerges from interactions between embedded text, imagery, and cultural context. Existing meme research is distributed across tasks (hate, misogyny, propaganda, sentiment, humour) and languages, which limits cross-domain generalization. To address this gap we propose MemeLens, a unified multilingual and multitask explanation-enhanced Vision Language Model (VLM) for meme understanding. We consolidate 38 public meme datasets, filter and map dataset-specific labels into a shared taxonomy of $20$ tasks spanning harm, targets, figurative/pragmatic intent, and affect. We present a comprehensive empirical analysis across modeling paradigms, task categories, and datasets. Our findings suggest that robust meme understanding requires multimodal training, exhibits substantial variation across semantic categories, and remains sensitive to over-specialization when models are fine-tuned on individual datasets rather than trained in a unified setting. We will make the experimental resources and datasets publicly available for the community.

翻译：网络迷因作为一种主导性的在线交流与操纵媒介，其意义产生于嵌入式文本、图像与文化语境的交互作用。现有迷因研究分散于不同任务（仇恨言论、厌女症、宣传、情感、幽默）与语言之间，限制了跨领域泛化能力。为填补这一空白，我们提出MemeLens——一个统一的多语言多任务增强解释型视觉语言模型（VLM），用于迷因理解。我们整合了38个公开迷因数据集，将数据集特定标签筛选并映射至包含危害性、目标对象、比喻/语用意图及情感维度等20项任务的共享分类体系。通过对建模范式、任务类别与数据集的全面实证分析，我们发现：鲁棒的迷因理解需要多模态训练，在语义类别间存在显著差异，且当模型在单个数据集上微调而非统一训练时，仍易受过度专业化影响。我们将公开实验资源与数据集以供学界使用。