diff --git a/captum/attr/_utils/interpretable_input.py b/captum/attr/_utils/interpretable_input.py
index 6882df6c04..d7a057f197 100644
--- a/captum/attr/_utils/interpretable_input.py
+++ b/captum/attr/_utils/interpretable_input.py
@@ -1,6 +1,16 @@
 # pyre-strict
 from abc import ABC, abstractmethod
-from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
+from typing import (
+    Any,
+    Callable,
+    cast,
+    Dict,
+    List,
+    Optional,
+    Tuple,
+    TYPE_CHECKING,
+    Union,
+)
 
 import numpy as np
 import PIL.Image
@@ -9,6 +19,9 @@
 from captum._utils.typing import TokenizerLike
 from torch import Tensor
 
+if TYPE_CHECKING:
+    from matplotlib.pyplot import Axes, Figure
+
 
 def _scatter_itp_attr_by_mask(
     itp_attr: Tensor,
@@ -666,6 +679,19 @@ def to_model_input(self, perturbed_tensor: Optional[Tensor] = None) -> Any:
 
         return self.processor_fn(perturbed_image)
 
+    def get_mask_list(self) -> List[Tensor]:
+        """
+        Get the list of binary masks for each interpretable feature.
+        If mask_list is provided, return it directly. Otherwise, create
+        a list of binary masks from the mask tensor.
+
+        Returns:
+            List[Tensor]: list of binary masks, one for each interpretable feature
+        """
+        return self.mask_list or [
+            self.mask == mask_id for mask_id in self.mask_id_to_idx.keys()
+        ]
+
     def format_attr(self, itp_attr: Tensor) -> Tensor:
         """
         Attribution for interpretable image segments
@@ -711,3 +737,78 @@ def format_pixel_attr(self, itp_attr: Tensor) -> Tensor:
                 formatted_mask.unsqueeze(0),
             )
             return formatted_attr
+
+    def plot_mask_overlay(
+        self, show: bool = False, border_width: int = 0, show_legends: bool = True
+    ) -> Union[None, Tuple["Figure", "Axes"]]:
+        """
+        util to help visualize the mask segementation
+
+        Args:
+            show: If True, display the plot immediately using plt.show().
+                  If False, return the figure and axes objects.
+            border_width: Width of the border around each segment in pixels.
+                          Set to 0 to disable borders. Default is 0.
+            show_legends: If True, display the mask id for each segment at its
+                          centroid. Default is True.
+        """
+
+        import matplotlib.pyplot as plt
+
+        # scipy is not within install_requires of captum
+        from scipy.ndimage import binary_erosion
+
+        fig, ax = plt.subplots()
+
+        ax.imshow(self.image)
+        fig.set_facecolor("white")
+
+        # random colors for all interpretable features
+        opacity = 0.6
+        colors = [
+            np.array([*np.random.random(3), opacity])
+            for _ in range(self.n_itp_features)
+        ]
+
+        mask_list = self.get_mask_list()
+
+        for mask, color, mid in zip(mask_list, colors, self.mask_id_to_idx.keys()):
+            mask_np = mask.numpy().astype(bool)
+            h, w = mask_np.shape
+            mask_image = mask_np.reshape(h, w, 1) * color.reshape(1, 1, -1)
+            ax.imshow(mask_image)
+
+            # Add border inside each segment using erosion
+            if border_width > 0:
+                eroded = binary_erosion(mask_np, iterations=border_width)
+                border = mask_np & ~eroded
+                border_color = np.array([*color[:3], 0.8])
+                border_image = border.reshape(h, w, 1) * border_color.reshape(1, 1, -1)
+                ax.imshow(border_image)
+
+            # Calculate centroid and display mask id
+            if show_legends and mask_np.any():
+                rows, cols = np.where(mask_np)
+                centroid_y, centroid_x = rows.mean(), cols.mean()
+                ax.text(
+                    centroid_x,
+                    centroid_y,
+                    str(mid),
+                    color="white",
+                    fontsize=10,
+                    ha="center",
+                    va="center",
+                    bbox={
+                        "boxstyle": "round,pad=0.2",
+                        "facecolor": "black",
+                        "alpha": 0.6,
+                    },
+                )
+
+        ax.axis("off")
+
+        if show:
+            plt.show()
+            return None
+        else:
+            return fig, ax