# Text-Guided Refinement for Referring Image Segmentation
## 4 Experiment
### Ablation Study  
To evaluate the contribution of each module in the proposed method, we conducted ablation experiments using three test sets (val, testA, and testB) of the UNC dataset. The following models were tested in the ablation study:  
* Baseline: Since this method primarily focuses on designing a multi-scale feature fusion decoder, the encoder from the widely used CMPC method [35] was chosen for multi-modal feature encoding. To evaluate the necessity of multi-scale information fusion, segmentation predictions were made using only the multi-modal feature. * Baseline + RRN: This model incorporates the RRN method [2] following the encoder of the CMPC model. The multi-modal features from three different scales are sequentially fed into a convLSTM network for multi-scale feature fusion. * Baseline + CPN: This model denotes the addition of a cascaded pyramid structure for multi-scale information fusion based on the baseline model. In this model, features of different scales are not fused by a gate function but are achieved by simple feature addition. * Baseline + CPN + TG: This model extends the previous one by adding the text-guided gating mechanism (TG) for enhanced fusion within the cascaded pyramid structure. * Baseline + ALL: This model incorporates language-embedded visual encoder for subsequent processing, building upon the previous model. Also, the model includes the ASPP module to enhance the network's ability to capture fine-grained image details, thus forming the complete network structure proposed in this paper. The quantitative and qualitative results of the ablation experiments are presented in Table 3 and Figure 5, respectively. The table includes the results on the three test subsets--val, testA, and testB--of the UNC dataset. reflecting an average improvement of nearly 10% over the baseline model. These results underscore the effectiveness of the cascaded pyramid structure in achieving robust multi-scale feature fusion. Compared to the existing RRN method, the CPN model shows a more than 1% improvement in Overall IoU on the testA set. This indicates that the proposed cascaded pyramid structure better captures the directional flow of information across different scales, enhancing its ability to recognize targets with higher accuracy. Further enhancement is observed with the inclusion of the TG module. On the testB test set, the model incorporating the text-guided gating mechanism achieves a [EMAIL_ADDRESS] score of 71.19%, representing an improvement of over 4% compared to the model without this module (67.11%). Similarly, on the Overall IoU, the TG module contributes a more than 1% improvement, reaching 60.96%. These results demonstrate that the text-guided gating method effectively selects relevant information for referring segmentation from multi-scale features, thereby facilitating the flow of information at various scales. Figure 5: Visual examples of the proposed modules. For the language-embedded visual encoder, the results on the testA set show that the Baseline + ALL method achieves a 1% improvement in Overall IoU compared to the model without this module. This suggests that the visual feature representation method effectively filters out irrelevant areas in the image, reducing noise and enabling more precise segmentation. Additionally, the ASPP module, with its dilated convolution and downsampling capabilities, enhances the recognition ability of smaller-scale targets, further strengthening the model's overall performance. The baseline single-scale model achieves Overall IoU values of 56.00%, 59.77%, and 54.12% on the val, testA, and testB sets, respectively. In comparison, the proposed method achieves values of 63.82%, 66.76%, and 61.93%, leading to an average improvement of 7%. These results confirm the superiority and robustness of the proposed method in addressing the challenges of referring segmentation. Figures 4 and 5 present the segmentation results obtained from the aforementioned models.