🤖 AI Summary
This work addresses the challenge of detecting implicit misogynistic content in internet memes, which often relies on semantic incongruity between images and text as well as culturally specific contexts. To this end, the authors propose GeoMVC, a novel approach featuring a geometric interaction layer that dynamically models alignment between image and text embeddings using Hadamard product and cosine similarity, replacing conventional static concatenation. Additionally, a multi-view consensus mechanism is introduced to aggregate predictions from original, length-filtered, and English-translated text views, thereby mitigating distributional shifts caused by OCR noise and transcription errors. Evaluated on Task A of the ICMI 2026 CC-MMD Challenge, GeoMVC achieved second place for Malayalam (Macro F1: 0.892), third for Chinese (0.895), and fifth for Tamil (0.521), demonstrating its effectiveness and robustness in cross-lingual multimodal misogyny detection.
📝 Abstract
The proliferation of internet memes has introduced new complexities to automated content moderation, particularly in detecting misogyny. Memes often rely on a semantic clash between visual and textual modalities, where hateful intent is implicit and culturally grounded. This paper presents GeoMVC (Geometric Interaction and Multi-View Consensus), developed for the CC-MMD Grand Challenge at ICMI 2026. To address the limitations of static feature concatenation, a Geometric Interaction Layer is proposed that models cross-modal alignment via Hadamard products and cosine similarity between frozen visual and textual embeddings. We further mitigate distribution shifts caused by noisy OCR and code-mixed transliteration through a Multi-View Consensus strategy, aggregating predictions across raw, length-filtered, and English-translated text views. The system achieved Rank 2 in the Malayalam partition (Macro F1: 0.892) and Rank 3 in the Chinese partition (Macro F1: 0.895) on Task A, while securing Rank 5 in the Tamil partition (Macro F1: 0.521). A detailed error analysis on the development partition highlights open challenges in modeling localized transliteration and code-mixed sarcasm across Dravidian and Chinese cultural contexts.