2026/03/20 by Ali AbuSaleh, Ali Abusaleh, Leon Hammerla +1
Computer Science · #Topic Modeling #Natural Language Processing Techniques #Speech and dialogue systems
paper · pdf · doi:10.1109/icnlp69856.2026.11527861
Detecting high-level semantic concepts like negation across modalities remains a challenge for current multimodal systems. We analyze this as a fundamental representation learning problem, providing the first evidence that negation does not form a linearly or non-linearly separable class in the latent spaces of standard vision-language models (VLMs). We demonstrate that pretrained embeddings primarily encode modality-specific features, lacking a generalizable negation signal. To overcome this, we propose a novel cross-modal attention architecture that explicitly models inter-modal dependencies, achieving performance gains of up to + 7. 0 3 % F 1 over unimodal baselines. Our analysis reveals a key asymmetry: while textual negation often appears independently, visual negation is semantically dependent on linguistic context, a finding validated through our statistical analysis of 3, 2 2 2 political video-text pairs automatically annotated via Qwen2.5-VL. By combining this analysis with self-supervised video representations (JEPA2), we advance the modeling of temporal negation. This work provides new methods and insights for learning robust, semantically-aligned representations in multimodal systems.