diff --git a/modules/dnn/src/layers/concat_layer.cpp b/modules/dnn/src/layers/concat_layer.cpp
index 3a6466bd80..6cb083e453 100644
--- a/modules/dnn/src/layers/concat_layer.cpp
+++ b/modules/dnn/src/layers/concat_layer.cpp
@@ -302,6 +302,8 @@ public:
             ranges[cAxis].start = 0;
             for (size_t i = 0; i < inputs.size(); i++)
             {
+                if (inputs[i].empty())
+                    continue;
                 ranges[cAxis].end = ranges[cAxis].start + inputs[i].size[cAxis];
                 for (int j = 0; j < outMat.dims; ++j)
                 {
diff --git a/modules/dnn/src/layers/slice_layer.cpp b/modules/dnn/src/layers/slice_layer.cpp
index de302ec291..195ed7cb24 100644
--- a/modules/dnn/src/layers/slice_layer.cpp
+++ b/modules/dnn/src/layers/slice_layer.cpp
@@ -69,10 +69,12 @@ Range normalizeRange(const Range& input_range, int n)
 {
     Range range = input_range;
 
-    range.start = std::min(std::max(range.start, -n), n - 1);
-    if (range.start < 0)
-    {
-        range.start += n;
+    if (range.start != n){
+        range.start = std::min(std::max(range.start, -n), n - 1);
+        if (range.start < 0)
+        {
+            range.start += n;
+        }
     }
 
     range.end = std::min(std::max(range.end, -n), n);
@@ -610,7 +612,9 @@ public:
         {
             for (size_t i = 0; i < outputs.size(); i++)
             {
-                inpMat(finalSliceRanges[i]).copyTo(outputs[i]);
+                if (finalSliceRanges[i][0].start != finalSliceRanges[i][0].end){
+                    inpMat(finalSliceRanges[i]).copyTo(outputs[i]);
+                }
             }
         }
         else
diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
index 8855eb6439..e560ff2dbe 100644
--- a/modules/dnn/test/test_onnx_importer.cpp
+++ b/modules/dnn/test/test_onnx_importer.cpp
@@ -3110,6 +3110,13 @@ TEST_P(Test_ONNX_layers, Attention) {
 TEST_P(Test_ONNX_layers, AttentionSingleHead) {
     testONNXModels("attention_single_head");
 }
+TEST_P(Test_ONNX_layers, PyTorchAttentionSingleHead){
+    testONNXModels("pytorch_attention_single_head");
+}
+
+TEST_P(Test_ONNX_layers, PyTorchUnflatten){
+    testONNXModels("unflatten");
+}
 
 TEST_P(Test_ONNX_nets, ViT_B_32) {
     applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_LONG);