diff --git a/torchvision/transforms/v2/functional/_geometry.py b/torchvision/transforms/v2/functional/_geometry.py
index a35de4ac95d..0e40791deb4 100644
--- a/torchvision/transforms/v2/functional/_geometry.py
+++ b/torchvision/transforms/v2/functional/_geometry.py
@@ -401,14 +401,24 @@ def __resize_image_pil_dispatch(
     return _resize_image_pil(image, size=size, interpolation=interpolation, max_size=max_size)
 
 
-def resize_mask(mask: torch.Tensor, size: Optional[list[int]], max_size: Optional[int] = None) -> torch.Tensor:
+def resize_mask(
+    mask: torch.Tensor,
+    size: Optional[list[int]],
+    max_size: Optional[int] = None,
+    interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
+) -> torch.Tensor:
     if mask.ndim < 3:
         mask = mask.unsqueeze(0)
         needs_squeeze = True
     else:
         needs_squeeze = False
 
-    output = resize_image(mask, size=size, interpolation=InterpolationMode.NEAREST, max_size=max_size)
+    if isinstance(interpolation, int):
+        interpolation = InterpolationMode(interpolation)
+    if interpolation not in {InterpolationMode.NEAREST, InterpolationMode.NEAREST_EXACT}:
+        interpolation = InterpolationMode.NEAREST
+
+    output = resize_image(mask, size=size, interpolation=interpolation, max_size=max_size)
 
     if needs_squeeze:
         output = output.squeeze(0)
@@ -420,7 +430,8 @@ def resize_mask(mask: torch.Tensor, size: Optional[list[int]], max_size: Optiona
 def _resize_mask_dispatch(
     inpt: tv_tensors.Mask, size: list[int], max_size: Optional[int] = None, **kwargs: Any
 ) -> tv_tensors.Mask:
-    output = resize_mask(inpt.as_subclass(torch.Tensor), size, max_size=max_size)
+    interpolation = kwargs.pop("interpolation", InterpolationMode.NEAREST)
+    output = resize_mask(inpt.as_subclass(torch.Tensor), size, max_size=max_size, interpolation=interpolation)
     return tv_tensors.wrap(output, like=inpt)