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This research addresses a fundamental limitation in vision-language models by proving that negation signals are not inherently separable within standard latent spaces. The authors introduce an attention-based architecture that successfully aligns visual and textual modalities to detect cross-modal negations, yielding significant performance improvements on complex video-text datasets. These findings offer critical insights for improving the semantic robustness of multimodal AI systems in real-world applications involving unimodal baselines.
Learning to Detect Cross-Modal Negation: An Analysis of Latent Representations and an Attention-Based Solution
arXiv:2607.17712v1 Announce Type: new Abstract: 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 +7.03% F1 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 3,222 political video-text pairs automatically annotated via \textsc{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.
Abstract: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 +7.03% F1 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 3,222 political video-text pairs automatically annotated via \textsc{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.
This manuscript is an accepted version of the article (published at ICNLP2026). Published in IEEE Xplore, DOI:https://doi.org/10.1109/ICNLP69856.2026.11527861 document: this https URL
Artificial Intelligence (cs.AI)
arXiv:2607.17712 [cs.AI]
(or arXiv:2607.17712v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.17712 arXiv-issued DOI via DataCite (pending registration)
2026 8th International Conference on Natural Language Processing (ICNLP), Xi'an, China, 2026, pp. 613-622
https://doi.org/10.1109/ICNLP69856.2026.11527861 DOI(s) linking to related resources
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