Rethinking Diffusion Segmentation: When Does It Rely on Its Noisy State, and Does Diffusion Matter?

arXiv:2609.23967 · cs.CV, cs.LG · Submitted 2026-09-21 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-21

Updated: 2026-09-21

Code: https://github.com/Hengzhuo-Yang/Rethinking-Diffusion-Segmentation

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

State Reliance — 25× in this paper
Core — 24× in this paper · explained in 2 episodes
Diffusion Model — 19× in this paper · explained in 8 episodes
Diffusion Models — 9× in this paper · explained in 6 episodes
Ablation — 4× in this paper · explained in 1 episode
Abstraction — 4× in this paper · explained in 1 episode
Alignment — 3× in this paper · explained in 1 episode
Distillation — 2× in this paper · explained in 2 episodes
Generative Model — 2× in this paper · explained in 2 episodes
Generative Models — 2× in this paper · explained in 2 episodes
Segmentation Masks — 2× in this paper · explained in 2 episodes
Trace — 2× in this paper · explained in 2 episodes
Transformer — 2× in this paper · explained in 2 episodes
Accuracy — 1× in this paper · explained in 1 episode
Generative AI — 1× in this paper · explained in 2 episodes
Latent Diffusion Model — 1× in this paper · explained in 2 episodes
Latent Space — 1× in this paper · explained in 7 episodes
Reconstruction Error — 1× in this paper · explained in 2 episodes
U-net architecture — 1× in this paper · explained in 3 episodes
nnU-Net — 1× in this paper · explained in 2 episodes

Sources

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