DiffImaginE: Imagine to Verify Entity Types with Diffusion

• URL: http://arxiv.org/abs/2608.03025v3
• Date: Thu, 13 Aug 2026 02:36:54 GMT
• Title: DiffImaginE: Imagine to Verify Entity Types with Diffusion
• Abstract summary: We introduce DiffImaginE, which formulates MNER type verification as conditional latent diffusion inference.<n>Given span-localised visual evidence, a type-conditioned denoiser predicts noise injected into its standardised latent.<n>The resulting denoising error provides an ELBO-consistent surrogate for type-conditional negative log-likelihood.
• Score: 17.922112345279643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal named entity recognition (MNER) determines whether each candidate span and entity-type hypothesis is supported by joint textual and visual evidence. Existing imagine-and-compare verifiers map each (span, type) pair to one predicted visual feature, compressing diverse visual realisations into a single prototype and providing a compatibility score without explicit probabilistic semantics. We introduce DiffImaginE, which formulates MNER type verification as conditional latent diffusion inference. Given span-localised visual evidence, a type-conditioned denoiser predicts noise injected into its standardised latent. The resulting denoising error provides an ELBO-consistent surrogate for type-conditional negative log-likelihood, allowing competing type hypotheses to be ranked by how well they explain the observation. DiffImaginE retains a standard multimodal encoder stack and replaces the deterministic verifier with a classifier-free-guided diffusion scorer trained using Min-SNR weighting. We directly supervise per-type diffusion scores as classification logits, learn aggregation across noise levels, and use antithetic sampling to reduce Monte Carlo comparison variance. Our analysis shows that classifier-free guidance sharpens the induced type posterior and characterises when antithetic pairing reduces variance at equal denoiser cost. Experiments on Twitter-2015 and Twitter-2017 show consistent gains over a matched deterministic ImaginE control under the same encoder, auxiliary objectives, and evaluation protocol, supported by ablations and paired significance tests.

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