@inproceedings{garg-etal-2025-just,
    title = "Just {KIDDIN}' : Knowledge Infusion and Distillation for Detection of {IN}decent Memes",
    author = "Garg, Rahul  and
      Padhi, Trilok  and
      Jain, Hemang  and
      Kursuncu, Ugur  and
      Kumaraguru, Ponnurangam",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.findings-acl.1184/",
    doi = "10.18653/v1/2025.findings-acl.1184",
    pages = "23067--23086",
    ISBN = "979-8-89176-256-5",
    abstract = "Detecting toxicity in online multimodal environments, such as memes, remains a challenging task due to the complex contextual connections across modalities (e.g., text and visual), which demand both common-sense reasoning and contextual awareness. To bridge this gap, we propose a hybrid neurosymbolic framework that unifies (1) distillation of implicit contextual knowledge (e.g., sarcasm, cultural references) from Large Vision-Language Models (LVLMs) and (2) infusion of explicit relational semantics through sub-graphs from Knowledge Graphs (KGs). Experimental results on two benchmark datasets show the superior performance of our approach, \textit{Knowledge-Infused Distilled Vision-Language Model (KID-VLM)}, over the state-of-the-art baselines across AUC and F1, with improvements of 0.5{\%}, and 10.6{\%}, respectively, in HatefulMemes Benchmark across variants. Further, KID-VLM demonstrates better generalizability and achieves the best performance across all baselines in the HarMeme Dataset with a 6.3{\%} and 3.2{\%} in F1 and AUC.Given the contextual complexity of the toxicity detection, KID-VLM showcases the significance of learning compact models ({\textasciitilde}500M parameters) from both explicit (i.e., KG) and implicit (i.e., LVLMs) contextual cues incorporated through a hybrid neurosymbolic approach. Our codes and pretrained models are publicly available."
}