diff --git a/CHANGELOG.md b/CHANGELOG.md
index 09ef42515a3..314f85d1bab 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -10,6 +10,8 @@ All notable changes to this project will be documented in this file.
 - Fix Learning Rate and Loss Handling in Tile Classifier MaskRCNN EfficientNet (<https://github.com/openvinotoolkit/training_extensions/pull/3873>)
 - Pad tile to image size (512 x 512) (<https://github.com/openvinotoolkit/training_extensions/pull/3887>)
 - Don't apply labels normalization on training (<https://github.com/openvinotoolkit/training_extensions/pull/3886>)
+- Disable Tile Classifier in Rotated Detection (<https://github.com/openvinotoolkit/training_extensions/pull/3894>)
+- Enhance Memeory Cache Handler with Complex Unique Keys (<https://github.com/openvinotoolkit/training_extensions/pull/3897>)
 
 ## \[v1.6.4\]
 
diff --git a/README.md b/README.md
index 4378d901a32..83d1612666e 100644
--- a/README.md
+++ b/README.md
@@ -104,6 +104,7 @@ You can find more details with examples in the [CLI command intro](https://openv
 - Fix Learning Rate and Loss Handling in Tile Classifier MaskRCNN EfficientNet
 - Pad tile to image size (512 x 512)
 - Don't apply labels normalization on training
+- Enhance Memeory Cache Handler with Complex Unique Keys
 
 ### Release History
 
diff --git a/docs/source/guide/release_notes/index.rst b/docs/source/guide/release_notes/index.rst
index 85edf4473ff..1826f532280 100644
--- a/docs/source/guide/release_notes/index.rst
+++ b/docs/source/guide/release_notes/index.rst
@@ -11,6 +11,7 @@ v1.6.5 (3Q24)
 - Fix Learning Rate and Loss Handling in Tile Classifier MaskRCNN EfficientNet
 - Pad tile to image size (512 x 512)
 - Don't apply labels normalization on training
+- Enhance Memeory Cache Handler with Complex Unique Keys
 
 v1.6.4 (3Q24)
 -------------
diff --git a/src/otx/algorithms/common/adapters/mmcv/pipelines/load_image_from_otx_dataset.py b/src/otx/algorithms/common/adapters/mmcv/pipelines/load_image_from_otx_dataset.py
index fa0c94518d9..7fe2e5df31f 100644
--- a/src/otx/algorithms/common/adapters/mmcv/pipelines/load_image_from_otx_dataset.py
+++ b/src/otx/algorithms/common/adapters/mmcv/pipelines/load_image_from_otx_dataset.py
@@ -42,7 +42,12 @@ def _get_unique_key(results: Dict[str, Any]) -> Tuple:
         if "cache_key" in results:
             return results["cache_key"]
         d_item = results["dataset_item"]
-        results["cache_key"] = d_item.media.path, d_item.roi.id
+        if d_item.media.path:  # when video extracted frames come, media.path is None
+            results["cache_key"] = d_item.media.path, d_item.roi.id
+        elif len(d_item.annotation_scene.annotations) > 0:
+            results["cache_key"] = d_item.roi.id, d_item.annotation_scene.annotations[0].id
+        else:
+            results["cache_key"] = d_item.roi.id
         return results["cache_key"]
 
     def _get_memcache_handler(self):
diff --git a/src/otx/algorithms/detection/configs/rotated_detection/configuration.yaml b/src/otx/algorithms/detection/configs/rotated_detection/configuration.yaml
index fb9b25325e0..05db55c767c 100644
--- a/src/otx/algorithms/detection/configs/rotated_detection/configuration.yaml
+++ b/src/otx/algorithms/detection/configs/rotated_detection/configuration.yaml
@@ -565,7 +565,7 @@ tiling_parameters:
     header: Enable tile classifier
     description: Enabling tile classifier enhances the speed of tiling inference by incorporating a tile classifier into the instance segmentation model. This feature prevents the detector from making predictions on tiles that do not contain any objects, thus optimizing its speed performance.
     default_value: false
-    editable: true
+    editable: false
     affects_outcome_of: TRAINING
     type: BOOLEAN
     ui_rules:
@@ -573,8 +573,8 @@ tiling_parameters:
       operator: AND
       rules: []
       type: UI_RULES
-    value: true
-    visible_in_ui: true
+    value: false
+    visible_in_ui: false
     warning: The tile classifier prioritizes inference speed over training speed, it requires more training in order to achieve its optimized performance.
 
   enable_adaptive_params: