On the Relaxation of Conditional Independence Assumption for Image Segmentation

arXiv:2609.38930 · cs.CV, cs.AI, cs.LG, stat.ML · Submitted 2026-09-30 · Read on arXiv

cs.CV, cs.AI, cs.LG, stat.ML

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/ZixunWang/RankSEG-DEP

Terminology

• Reciprocal Moment Approximation — 6× in this paper
• Conditional Random Field — 5× in this paper
• Spatially Localized Dependence — 5× in this paper
• Robustness — 4× in this paper · explained in 5 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Conditional Independence Assumption — 3× in this paper
• Segmentation Masks — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Maximum A Posteriori — 1× in this paper
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• Rethinking Atrous Convolution for Semantic Image Segmentation
• RankSEG-RMA: An Efficient Segmentation Algorithm via Reciprocal Moment Approximation
• Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

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