Just KIDDIN: Knowledge Infusion and Distillation for Detection of INdecent Memes

2024-11-19

arXiv.org

AI Summary

This paper addresses the challenge of toxicity detection in multimodal hate memes—composite images with overlaid text. To this end, we propose a neuro-symbolic framework integrating knowledge distillation and explicit commonsense injection. Methodologically: (i) cross-modal knowledge distillation from a large vision-language model (LVLM) captures implicit toxic semantics; (ii) a ConceptNet subgraph is constructed and embedded into a multimodal alignment space to explicitly model commonsense-driven toxic associations between image and text; (iii) a relation-aware reasoning module orchestrates synergistic interaction between the two components. To our knowledge, this is the first work to jointly leverage LVLM-based distillation and structured knowledge graph infusion for hate speech detection. Evaluated on two benchmark datasets for hateful meme detection, our framework achieves absolute improvements of 1.1%, 7.0%, and 35.0% in AU-ROC, F1-score, and Recall, respectively—substantially outperforming existing state-of-the-art methods.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Toxicity identification in online multimodal environments remains a challenging task due to the complexity of contextual connections across modalities (e.g., textual and visual). In this paper, we propose a novel framework that integrates Knowledge Distillation (KD) from Large Visual Language Models (LVLMs) and knowledge infusion to enhance the performance of toxicity detection in hateful memes. Our approach extracts sub-knowledge graphs from ConceptNet, a large-scale commonsense Knowledge Graph (KG) to be infused within a compact VLM framework. The relational context between toxic phrases in captions and memes, as well as visual concepts in memes enhance the model's reasoning capabilities. Experimental results from our study on two hate speech benchmark datasets demonstrate superior performance over the state-of-the-art baselines across AU-ROC, F1, and Recall with improvements of 1.1%, 7%, and 35%, respectively. Given the contextual complexity of the toxicity detection task, our approach showcases the significance of learning from both explicit (i.e. KG) as well as implicit (i.e. LVLMs) contextual cues incorporated through a hybrid neurosymbolic approach. This is crucial for real-world applications where accurate and scalable recognition of toxic content is critical for creating safer online environments.

Problem

• Toxicity detection in multimodal online content
• Integration of Knowledge Distillation and infusion
• Enhancing meme toxicity recognition accuracy

Innovation

• Integrates Knowledge Distillation from LVLMs
• Extracts sub-knowledge graphs from ConceptNet
• Employs hybrid neurosymbolic approach