Meta-CoT uses two-level decomposition of editing operations into meta-tasks and a CoT consistency reward to improve granularity and generalization, reporting 15.8% gains across 21 tasks.
Introducing 4o image generation, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
IncreFA uses hierarchical constraints with learnable orthogonal priors and a latent memory bank to enable continual adaptation for attributing images to new generative models, reporting SOTA accuracy and 98.93% unseen detection on a 28-model benchmark.
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Meta-CoT: Enhancing Granularity and Generalization in Image Editing
Meta-CoT uses two-level decomposition of editing operations into meta-tasks and a CoT consistency reward to improve granularity and generalization, reporting 15.8% gains across 21 tasks.
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IncreFA: Breaking the Static Wall of Generative Model Attribution
IncreFA uses hierarchical constraints with learnable orthogonal priors and a latent memory bank to enable continual adaptation for attributing images to new generative models, reporting SOTA accuracy and 98.93% unseen detection on a 28-model benchmark.