# Text-Guided Refinement for Referring Image Segmentation
## 4 Experiment
### Comparison with State-of-the-Arts  
To verify the effectiveness of the proposed model, we compared it with fourteen methods, namely, RMI [3], ASGN [37], RRN [2], MAttNet [45], CMSA [12], CAC [36], STEP [34], BCAM [10], CMPC [35], LSCM [11], CMPC+ [46], EFN [47], TGMI [48], and RBVL [49]. To ensure a fair comparison, we selected methods that utilize backbone architectures similar to DeepLab and related structures, such as ResNet101, for extracting image features. These models are pre-trained on the PASCAL-VOC dataset, which contains approximately 10K images. In contrast, some existing methods, such as LTS [50], depend on Darknet [51], pre-trained on the much larger MS-COCO dataset containing around 110K images. This discrepancy in pre-training data size may introduce biases, making direct performance comparisons less equitable. The results, presented in Table 1 demonstrate the advantages of our approach across the test subsets of the three benchmark datasets. Our proposed approach outperforms all state-of-the-art methods with similar backbones in terms of overall IoU on most datasets, except for G-Ref, where it achieves comparable performance to EFN. Notably, compared to the TGMI method, the TGRN achieves a performance gain of approximately 1% across all three datasets. In particular, on the test subsets of the UNC+ dataset, our method demonstrates an improvement of around 2%, highlighting its effectiveness in accurately segmenting and recognizing referred objects. These results validate the robustness and precision of the proposed TGRN model, further indicating its potential in enhancing the performance of referring image segmentation tasks. Visual examples in Figure 4 illustrate the capability of our method to accurately segment specific regions based on the provided query expressions. In addition, since the UNC, UNC+, and G-Ref were all collected from MS-COCO, we followed the work [47] by using the MS-COCO dataset for pre-training to facilitate comparisons with more state-of-the-art methods. The results of the model trained in this setting are denoted as \(\text{TGRN}_{occo}\) as shown in Table 2, which demonstrates that sufficient training data can yield better results. Our method still achieves superior performance compared to existing methods, such as a 1% improvement in G-Ref over the M\({}^{3}\)Dec method. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Vis.Encoder**} & \multirow{2}{*}{**Lang.Encoder**} & \multicolumn{3}{c}{**UNC**} & \multicolumn{3}{c}{**UNC+**} & \multicolumn{3}{c}{**G-Ref**} \\ \cline{3-10}  & & & **val** & **testA** & **testB** & **val** & **testA** & **testB** & **val** \\ \hline MCN [52] & DN-53 & GRU & 62.44 & 64.20 & 59.71 & 50.62 & 54.99 & 44.69 & 49.22 \\ ISFP [53] & DN-53 & GRU & 65.19 & 68.45 & 62.73 & 52.70 & 56.77 & 46.39 & 52.67 \\ LTS [50] & DN-53 & GRU & 65.43 & 67.76 & 63.08 & 54.21 & 58.32 & 48.02 & 54.40 \\ VLT [14] & DN-53 & GRU & 65.65 & 68.29 & 62.73 & 55.50 & 59.20 & 49.36 & 52.99 \\ DCMFNet [54] & DN-53 & LSTM & 65.84 & 69.34 & 63.09 & 54.78 & 60.03 & 49.30 & 51.99 \\ M\({}^{3}\)Dec [55] & DN-53 & GRU & 67.88 & 70.82 & 65.02 & 56.98 & 61.26 & 50.11 & 54.79 \\ \hline \(\text{TGRN}_{occo}\) & DL-101 & LSTM & 68.59 & 70.39 & 66.10 & 57.28 & 61.44 & 50.66 & 55.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with state-of-the-art methods pre-trained on MS-COCO dataset on three benchmark datasets using Overall IoU as a metric.