diff --git a/monai/networks/utils.py b/monai/networks/utils.py
index d85175ef7e..9d20d2a83b 100644
--- a/monai/networks/utils.py
+++ b/monai/networks/utils.py
@@ -48,7 +48,7 @@ def one_hot(labels: torch.Tensor, num_classes: int, dtype: torch.dtype = torch.f
         num_classes: number of output channels, the corresponding length of `labels[dim]` will be converted to
             `num_classes` from `1`.
         dtype: the data type of the output one_hot label.
-        dim: the dimension to be converted to `num_classes` channels from `1` channel.
+        dim: the dimension to be converted to `num_classes` channels from `1` channel, should be non-negative number.
 
     Example:
 
@@ -69,9 +69,6 @@ def one_hot(labels: torch.Tensor, num_classes: int, dtype: torch.dtype = torch.f
         print(out.shape)  # torch.Size([2, 2, 2, 2, 2])
 
     """
-    if labels.dim() == 0:
-        # if no channel dim, add it
-        labels = labels.unsqueeze(0)
 
     # if `dim` is bigger, add singleton dim at the end
     if labels.ndim < dim + 1:
diff --git a/monai/transforms/post/array.py b/monai/transforms/post/array.py
index 8913a1a041..397b14e2e2 100644
--- a/monai/transforms/post/array.py
+++ b/monai/transforms/post/array.py
@@ -150,6 +150,8 @@ def __call__(
     ) -> torch.Tensor:
         """
         Args:
+            img: the input tensor data to convert, if no channel dimension when converting to `One-Hot`,
+                will automatically add it.
             argmax: whether to execute argmax function on input data before transform.
                 Defaults to ``self.argmax``.
             to_onehot: whether to convert input data into the one-hot format.
diff --git a/tests/test_as_discrete.py b/tests/test_as_discrete.py
index 658a21efd6..ea806be139 100644
--- a/tests/test_as_discrete.py
+++ b/tests/test_as_discrete.py
@@ -37,9 +37,16 @@
     (1, 2, 2),
 ]
 
+TEST_CASE_4 = [
+    {"argmax": False, "to_onehot": True, "n_classes": 3},
+    torch.tensor(1),
+    torch.tensor([0.0, 1.0, 0.0]),
+    (3,),
+]
+
 
 class TestAsDiscrete(unittest.TestCase):
-    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
+    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])
     def test_value_shape(self, input_param, img, out, expected_shape):
         result = AsDiscrete(**input_param)(img)
         torch.testing.assert_allclose(result, out)