fix(onnx): treat empty Resize axes as no-op

src/frontends/onnx/frontend/src/op/resize.cpp

@@ -125,6 +125,9 @@ ov::OutputVector resize(const ov::frontend::onnx::Node& node) {
     }
     auto axes_attr = node.get_attribute_value<std::vector<int64_t>>("axes", {});
     if (axes_attr.empty()) {
+        if (node.has_attribute("axes")) {
+            return {data};
+        }
         return {std::make_shared<v11::Interpolate>(data, scale_or_sizes, attrs)};
     }
 

src/frontends/onnx/tests/tests_python/test_ops_resize.py

@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+# Copyright (C) 2026 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import onnx
+from onnx import helper
+
+from tests.tests_python.utils import run_model
+
+
+def test_resize_with_empty_axes_is_noop():
+    input_data = np.arange(4, dtype=np.float32).reshape(1, 1, 2, 2)
+    scales = np.array([], dtype=np.float32)
+
+    node = helper.make_node("Resize", ["x", "", "scales"], ["y"], axes=[])
+    graph = helper.make_graph(
+        [node],
+        "resize_empty_axes",
+        [
+            helper.make_tensor_value_info("x", onnx.TensorProto.FLOAT, input_data.shape),
+            helper.make_tensor_value_info("scales", onnx.TensorProto.FLOAT, scales.shape),
+        ],
+        [helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, input_data.shape)],
+    )
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 18)])
+
+    result = run_model(model, [input_data, scales])[0]
+    np.testing.assert_array_equal(result, input_data)