FM-CGM is a framework that uses a large reasoning model and text-to-image diffusion model for zero-shot visual causal reasoning via concept extractor, manipulator, counterfactual generator, and Causal Semantic Guidance mechanism.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
Pixel-level protective perturbations for portrait privacy are ineffective against common image transformations, and a low-cost purification framework can strip them out.
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Leveraging Foundation Models for Causal Generative Modeling
FM-CGM is a framework that uses a large reasoning model and text-to-image diffusion model for zero-shot visual causal reasoning via concept extractor, manipulator, counterfactual generator, and Causal Semantic Guidance mechanism.
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Do Protective Perturbations Really Protect Portrait Privacy under Real-world Image Transformations?
Pixel-level protective perturbations for portrait privacy are ineffective against common image transformations, and a low-cost purification framework can strip them out.