Generative Semantic Segmentation via an Observable Semantic-Image Interface and Hierarchical Generator Evidence Alignment
Generative semantic segmentation exposes structured predictions as images, but direct color decoding is susceptible to color drift and boundary mixing, whereas latent-feature decoders that predict a separate output distribution may relegate the rendered image to an intermediate visualization. We present Semantic Prism, a conditional semantic-image generation-and-refinement framework with deterministic inference. A diffusion-distilled one-step generator renders a semantic RGB image; per-pixel dis
Record details
Published: 12 August 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 13 August 2026
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ethics.ai (12 August 2026), “Generative Semantic Segmentation via an Observable Semantic-Image Interface and Hierarchical Generator Evidence Alignment,” evidence record 18802, https://ethics.ai/record/18802 (originally published by arXiv).
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