# Text-Guided Refinement for Referring Image Segmentation
## 5 Conclusions  
In this paper, we address the challenge of coarse target edge segmentation caused by scale discrepancies in referring image segmentation tasks. To enhance the network's ability to perceive targets across varying scales, we propose a multi-modal feature fusion framework integrated into the decoder stage. This framework enables the dynamic integration of multi-scale and multi-modal features during decoding, leveraging the complementary strengths of linguistic and visual information to refine segmentation accuracy. Unlike existing methods that treat all features equally during fusion, our framework incorporates a novel text-guided gating mechanism within a cascaded pyramid structure. This mechanism prioritizes relevant features while suppressing noise, ensuring the effective alignment of visual and linguistic cues. By integrating these features selectively and dynamically, the proposed method significantly enhances the model's precision and robustness in multi-scale  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**UNC** & **Methods** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **IoU** \\ \hline \multirow{2}{*}{val} & CMSA & 66.44 & 59.70 & 50.77 & 35.52 & 10.96 & 58.32 \\  & CMSA + ALL & 72.26 & 66.11 & 57.38 & 42.51 & 14.79 & 61.69 \\ \multirow{2}{*}{testA} & CMSA & 70.28 & 63.64 & 54.07 & 38.38 & 11.21 & 60.61 \\  & CMSA + ALL & 77.18 & 71.42 & 62.03 & 45.36 & 13.42 & 65.13 \\ \multirow{2}{*}{testB} & CMSA & 61.51 & 53.60 & 45.40 & 32.40 & 12.90 & 55.09 \\  & CMSA + ALL & 68.42 & 61.12 & 51.14 & 37.31 & 14.90 & 59.16 \\ \hline \end{tabular}
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Table 4: Prec@X and Overall IoU results on UNC dataset using CSMA as multi-modal encoder.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **IoU** & **Training Time (h)** & **Testing Time (ms)** & **Memory Costs (kb)** \\ \hline \multirow{2}{*}{CMPC} & G4.53 & 18.46 & 85 & 1,057,705 \\  & 66.76 & 20.30 & 103 & 1,080,341 \\ \hline \hline \end{tabular}
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Table 5: Comparison of computational time and memory costs.  
target segmentation. To validate the effectiveness of our method, extensive experiments were conducted on three benchmark datasets, demonstrating consistent performance improvements over existing state-of-the-art approaches. These results highlight the potential of the TGRN to advance the field of referring image segmentation by offering a precise and scalable solution that bridges the gap between multi-modal feature alignment and multi-scale target recognition.  
Conceptualization, S.Q.; methodology, S.Q.; investigation, S.Q. and S.Z.; resources, S.Q. and T.R.; writing--original draft preparation, S.Q.; writing--review and editing, S.Q., S.Z., and T.R.; visualization, S.Q.; funding acquisition, S.Q. and S.Z. All authors have read and agreed to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fundamental Research Program of Shanxi Province (No. 202203021212236), National Natural Science Foundation of China (Grant Nos. 62403199).  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository.  
The authors declare no conflicts of interest.