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DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation Without

Tianchen Zeng, Sai Wang, Yu Wu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 2, 2026

Summary

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA. Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...

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DiffRES, a new framework using Stable Diffusion, generates high-quality masks for Referring Expression Segmentation (RES). It overcomes information leakage issues, producing precise object masks with sharp boundaries.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Referring Expression Segmentation (RES) demands accurate object localization and precise mask prediction.
Current RES methods often compromise mask quality, leading to incomplete foregrounds and imprecise boundaries due to a focus on multimodal alignment.

Purpose of the Study:

To introduce DiffRES, a novel framework leveraging Stable Diffusion (SD) for high-quality mask generation in RES.
To address and mitigate the information leakage issue in generative dense prediction diffusion models for RES.

Main Methods:

DiffRES directly guides Stable Diffusion with visual and linguistic information to generate binary masks, bypassing training information leakage.
The framework ensures efficient knowledge transfer from SD to the RES task, focusing on mask quality.

Related Experiment Videos

Main Results:

DiffRES achieves superior boundary precision (APb) compared to state-of-the-art traditional methods.
The proposed model significantly outperforms existing SD-based RES models across all evaluation metrics.

Conclusions:

DiffRES effectively generates precise binary masks with sharp boundaries for RES.
The framework demonstrates a significant advancement in RES, particularly in mask quality and precision.