diff --git a/torchvision/prototype/transforms/functional/_geometry.py b/torchvision/prototype/transforms/functional/_geometry.py
index d3e1d94c96c..95f620f68b7 100644
--- a/torchvision/prototype/transforms/functional/_geometry.py
+++ b/torchvision/prototype/transforms/functional/_geometry.py
@@ -4,6 +4,7 @@
 
 import PIL.Image
 import torch
+from torch.nn.functional import interpolate
 from torchvision.prototype import features
 from torchvision.transforms import functional_pil as _FP, functional_tensor as _FT
 from torchvision.transforms.functional import (
@@ -115,6 +116,12 @@ def resize_image_tensor(
     max_size: Optional[int] = None,
     antialias: bool = False,
 ) -> torch.Tensor:
+    align_corners: Optional[bool] = None
+    if interpolation == InterpolationMode.BILINEAR or interpolation == InterpolationMode.BICUBIC:
+        align_corners = False
+    elif antialias:
+        raise ValueError("Antialias option is supported for bilinear and bicubic interpolation modes only")
+
     shape = image.shape
     num_channels, old_height, old_width = shape[-3:]
     new_height, new_width = _compute_resized_output_size((old_height, old_width), size=size, max_size=max_size)
@@ -122,13 +129,24 @@ def resize_image_tensor(
     if image.numel() > 0:
         image = image.reshape(-1, num_channels, old_height, old_width)
 
-        image = _FT.resize(
+        dtype = image.dtype
+        need_cast = dtype not in (torch.float32, torch.float64)
+        if need_cast:
+            image = image.to(dtype=torch.float32)
+
+        image = interpolate(
             image,
             size=[new_height, new_width],
-            interpolation=interpolation.value,
+            mode=interpolation.value,
+            align_corners=align_corners,
             antialias=antialias,
         )
 
+        if need_cast:
+            if interpolation == InterpolationMode.BICUBIC and dtype == torch.uint8:
+                image = image.clamp_(min=0, max=255)
+            image = image.round_().to(dtype=dtype)
+
     return image.reshape(shape[:-3] + (num_channels, new_height, new_width))
 
 
@@ -1312,9 +1330,11 @@ def resized_crop(
 
 def _parse_five_crop_size(size: List[int]) -> List[int]:
     if isinstance(size, numbers.Number):
-        size = [int(size), int(size)]
+        s = int(size)
+        size = [s, s]
     elif isinstance(size, (tuple, list)) and len(size) == 1:
-        size = [size[0], size[0]]
+        s = size[0]
+        size = [s, s]
 
     if len(size) != 2:
         raise ValueError("Please provide only two dimensions (h, w) for size.")