diff --git a/backends/arm/quantizer/quantization_annotator.py b/backends/arm/quantizer/quantization_annotator.py
index d7c85447dd5..ebc91c22bbb 100644
--- a/backends/arm/quantizer/quantization_annotator.py
+++ b/backends/arm/quantizer/quantization_annotator.py
@@ -6,7 +6,7 @@
 import logging
 import operator
 from dataclasses import dataclass
-from typing import Callable, List, Optional, Sequence
+from typing import Callable, cast, List, Optional, Sequence
 
 import torch
 import torch.fx
@@ -137,11 +137,18 @@ def _is_large_scalar(node: Node, gm: torch.fx.GraphModule):
     node since histc op (in HistogramObserver) only works for values up to certain upper
     bound.
     """
+    HISTC_UPPER_BOUND = 3.4028235e15
     if node.op == "get_attr" and isinstance(node.target, str):
         tensor = _get_node_target(gm, node.target)
         # torch.histc works until this upper bound
-        HISTC_UPPER_BOUND = 3.4028235e15
         return tensor.numel() == 1 and abs(tensor.item()) > HISTC_UPPER_BOUND
+    if node.op == "call_function" and node.target in (
+        torch.ops.aten.full.default,
+        torch.ops.aten.full,
+        torch.ops.aten.fill_.Scalar,
+    ):
+        fill_value = cast(float, node.args[1])
+        return abs(fill_value) > HISTC_UPPER_BOUND
     return False
 
 
@@ -358,9 +365,6 @@ def _match_pattern(
     torch.ops.aten.permute_copy.default,
     torch.ops.aten.avg_pool2d.default,
     torch.ops.aten.max_pool2d.default,
-    torch.ops.aten.full.default,
-    torch.ops.aten.full,
-    torch.ops.aten.fill_.Scalar,
     torch.ops.aten.flatten.using_ints,
     torch.ops.aten.dropout.default,
     torch.ops.aten.dropout_.default,
@@ -518,9 +522,6 @@ def any_or_hardtanh_min_zero(n: Node):
         ]
         quant_properties.quant_output = _QuantProperty(0, shared_qspec)  # type: ignore[arg-type]
     elif node.target in _one_to_one_shared_input_or_input_act_qspec:
-        if not isinstance(node.args[0], Node):
-            return None
-
         input_qspec = (
             SharedQuantizationSpec(node.args[0])  # type: ignore[arg-type]
             if is_output_annotated(node.args[0])  # type: ignore
@@ -578,7 +579,12 @@ def any_or_hardtanh_min_zero(n: Node):
             ),
         ]
         quant_properties.quant_output = None
-    elif node.target in [torch.ops.aten.scalar_tensor.default]:
+    elif node.target in [
+        torch.ops.aten.scalar_tensor.default,
+        torch.ops.aten.full.default,
+        torch.ops.aten.full,
+        torch.ops.aten.fill_.Scalar,
+    ]:
         quant_properties.quant_inputs = []
         quant_properties.quant_output = _QuantProperty(0, output_act_qspec)
     elif node.target in [operator.getitem]: