# Text-Guided Refinement for Referring Image Segmentation
## 4 Experiment
### Ablation Study
The examples demonstrate that the proposed models achieve superior performance compared to the baseline model, particularly in scenarios requiring complex multi-modal reasoning. Specifically, the model excels in scenes involving multiple similar objects and weak or missing spatial cues. In such cases, the ability of the TGRN to maintain continuous language guidance throughout the decoding process proves essential. The text-guided gating selectively activates spatial features that correspond to linguistically relevant regions, thereby preserving detail at the object boundaries during the multi-scale fusion process. For instance, in the fifth row of Figure 5, only the full model (\"Baseline + ALL\") correctly recovers the entire arm of the woman in white--a region that is small and low in contrast, requiring both accurate semantic understanding and fine-grained feature preservation. Similarly, in the second row, the model precisely segments the human at the top of the image, demonstrating improved boundary recovery. Moreover, in Figure 4, for the referring expression \"the real cat not the reflection\" which relies on visual attributes rather than spatial location, the proposed model successfully distinguishes the correct target from similar distractors. To further illustrate the impact of the multi-scale feature fusion, Figure 6 presents a comparison of features before and after applying the cascaded pyramid structure. The second, third, and fourth columns depict the multi-modal features extracted from the convolutional layers Res3, Res4, and Res5, respectively. It is evident that the cascaded pyramid structure enables the model to retain and integrate information related to the referring target across different scales, leading to accurate segmentation results. For instance, in the first row, the features at the three scales identify different regions of the \"banana\" but fail to capture the complete object. After applying the cascaded pyramid structure, the fused feature effectively recognizes the entire \"banana\". Similarly, in the third row, the scale 5 feature initially misidentifies the target. However, after the fusion process, this erroneous information is mitigated, allowing the network to correct the target's location and boundaries. Figure 7 provides a visual comparison of segmentation heatmaps before and after applying the text-guided gating structure. The input features at different scales (columns 2 to 4) show general activation in relevant regions, but the activations tend to be diffuse or misaligned around the object edges. After applying gated fusion (fifth column), the features become more concentrated around object contours, suggesting that the text-guided gating mechanism helps to refine spatial focus and reduce noise from irrelevant areas. For example, in the third row, initial features highlight a broad region including distractors. After text-guided fusion, the region of interest is narrowed to the person's body and legs with greater boundary precision, showing that the reinforced multi-modal interaction directly enhances edge-aware segmentation.