LADLE-MM: Limited Annotation based Detector with Learned Ensembles for Multimodal Misinformation

• URL: http://arxiv.org/abs/2512.20257v1
• Date: Tue, 23 Dec 2025 11:14:58 GMT
• Title: LADLE-MM: Limited Annotation based Detector with Learned Ensembles for Multimodal Misinformation
• Authors: Daniele Cardullo, Simone Teglia, Irene Amerini,
• Abstract summary: LADLE-MM is a model-soup multimodal misinformation detector with Learned Ensembles for Multimodal Misinformation.<n>It is composed of two unimodal branches and a third multimodal one that enhances image and text representations.<n>It achieves competitive performance on both binary and multi-label classification tasks.
• Score: 8.769506450302154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rise of easily accessible tools for generating and manipulating multimedia content, realistic synthetic alterations to digital media have become a widespread threat, often involving manipulations across multiple modalities simultaneously. Recently, such techniques have been increasingly employed to distort narratives of important events and to spread misinformation on social media, prompting the development of misinformation detectors. In the context of misinformation conveyed through image-text pairs, several detection methods have been proposed. However, these approaches typically rely on computationally intensive architectures or require large amounts of annotated data. In this work we introduce LADLE-MM: Limited Annotation based Detector with Learned Ensembles for Multimodal Misinformation, a model-soup initialized multimodal misinformation detector designed to operate under a limited annotation setup and constrained training resources. LADLE-MM is composed of two unimodal branches and a third multimodal one that enhances image and text representations with additional multimodal embeddings extracted from BLIP, serving as fixed reference space. Despite using 60.3% fewer trainable parameters than previous state-of-the-art models, LADLE-MM achieves competitive performance on both binary and multi-label classification tasks on the DGM4 benchmark, outperforming existing methods when trained without grounding annotations. Moreover, when evaluated on the VERITE dataset, LADLE-MM outperforms current state-of-the-art approaches that utilize more complex architectures involving Large Vision-Language-Models, demonstrating the effective generalization ability in an open-set setting and strong robustness to unimodal bias.

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