diff --git a/torchvision/prototype/transforms/functional/_geometry.py b/torchvision/prototype/transforms/functional/_geometry.py
index ce97ce0575d..98bd7a52712 100644
--- a/torchvision/prototype/transforms/functional/_geometry.py
+++ b/torchvision/prototype/transforms/functional/_geometry.py
@@ -727,39 +727,38 @@ def _pad_with_scalar_fill(
     shape = image.shape
     num_channels, height, width = shape[-3:]
 
-    if image.numel() > 0:
-        image = image.reshape(-1, num_channels, height, width)
-
-        if padding_mode == "edge":
-            # Similar to the padding order, `torch_pad`'s PIL's padding modes don't have the same names. Thus, we map
-            # the PIL name for the padding mode, which we are also using for our API, to the corresponding `torch_pad`
-            # name.
-            padding_mode = "replicate"
-
-        if padding_mode == "constant":
-            image = torch_pad(image, torch_padding, mode=padding_mode, value=float(fill))
-        elif padding_mode in ("reflect", "replicate"):
-            # `torch_pad` only supports `"reflect"` or `"replicate"` padding for floating point inputs.
-            # TODO: See https://github.com/pytorch/pytorch/issues/40763
-            dtype = image.dtype
-            if not image.is_floating_point():
-                needs_cast = True
-                image = image.to(torch.float32)
-            else:
-                needs_cast = False
-
-            image = torch_pad(image, torch_padding, mode=padding_mode)
-
-            if needs_cast:
-                image = image.to(dtype)
-        else:  # padding_mode == "symmetric"
-            image = _FT._pad_symmetric(image, torch_padding)
+    batch_size = 1
+    for s in shape[:-3]:
+        batch_size *= s
+
+    image = image.reshape(batch_size, num_channels, height, width)
+
+    if padding_mode == "edge":
+        # Similar to the padding order, `torch_pad`'s PIL's padding modes don't have the same names. Thus, we map
+        # the PIL name for the padding mode, which we are also using for our API, to the corresponding `torch_pad`
+        # name.
+        padding_mode = "replicate"
+
+    if padding_mode == "constant":
+        image = torch_pad(image, torch_padding, mode=padding_mode, value=float(fill))
+    elif padding_mode in ("reflect", "replicate"):
+        # `torch_pad` only supports `"reflect"` or `"replicate"` padding for floating point inputs.
+        # TODO: See https://github.com/pytorch/pytorch/issues/40763
+        dtype = image.dtype
+        if not image.is_floating_point():
+            needs_cast = True
+            image = image.to(torch.float32)
+        else:
+            needs_cast = False
 
-        new_height, new_width = image.shape[-2:]
-    else:
-        left, right, top, bottom = torch_padding
-        new_height = height + top + bottom
-        new_width = width + left + right
+        image = torch_pad(image, torch_padding, mode=padding_mode)
+
+        if needs_cast:
+            image = image.to(dtype)
+    else:  # padding_mode == "symmetric"
+        image = _FT._pad_symmetric(image, torch_padding)
+
+    new_height, new_width = image.shape[-2:]
 
     return image.reshape(shape[:-3] + (num_channels, new_height, new_width))
 
@@ -868,7 +867,24 @@ def pad(
         return pad_image_pil(inpt, padding, fill=fill, padding_mode=padding_mode)
 
 
-crop_image_tensor = _FT.crop
+def crop_image_tensor(image: torch.Tensor, top: int, left: int, height: int, width: int) -> torch.Tensor:
+    h, w = image.shape[-2:]
+
+    right = left + width
+    bottom = top + height
+
+    if left < 0 or top < 0 or right > w or bottom > h:
+        image = image[..., max(top, 0) : bottom, max(left, 0) : right]
+        torch_padding = [
+            max(min(right, 0) - left, 0),
+            max(right - max(w, left), 0),
+            max(min(bottom, 0) - top, 0),
+            max(bottom - max(h, top), 0),
+        ]
+        return _pad_with_scalar_fill(image, torch_padding, fill=0, padding_mode="constant")
+    return image[..., top:bottom, left:right]
+
+
 crop_image_pil = _FP.crop
 
 
@@ -893,7 +909,18 @@ def crop_bounding_box(
 
 
 def crop_mask(mask: torch.Tensor, top: int, left: int, height: int, width: int) -> torch.Tensor:
-    return crop_image_tensor(mask, top, left, height, width)
+    if mask.ndim < 3:
+        mask = mask.unsqueeze(0)
+        needs_squeeze = True
+    else:
+        needs_squeeze = False
+
+    output = crop_image_tensor(mask, top, left, height, width)
+
+    if needs_squeeze:
+        output = output.squeeze(0)
+
+    return output
 
 
 def crop_video(video: torch.Tensor, top: int, left: int, height: int, width: int) -> torch.Tensor: