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README.md

@@ -40,9 +40,20 @@ If you identify weaknesses in the data, please contact me.
 
 I had some trouble with the Hugging Face file upload. This is why you can find the data here: [Google Drive](https://drive.google.com/drive/folders/1K1lK6nSoaQ7PLta-bcfol3XSGZA1b9nt?usp=drive_link).
 
+# Research
+
+Synthetic datasets have limitations for achieving great segmentation results. This is because artificial lighting, occlusion, scale or backgrounds create a gap between synthetic and real images. A "model trained solely on synthetic data generated with naïve domain randomization struggles to generalize on the real domain", see [PEOPLESANSPEOPLE: A Synthetic Data Generator for Human-Centric Computer Vision (2022)](https://arxiv.org/pdf/2112.09290). However, hybrid training approaches seem to be promising and can even improve segmentation results.
+
+Currently I am doing research how to close this gap with the resources I have. There are approaches like considering the pose of humans for improving segmentation results, see [Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation (2019)](https://arxiv.org/pdf/1907.05193).
+
 # Changelog
 
-26.05.2024
+### 28.05.2024
+
+- Reduced blur, because it leads to blurred edges in results
+
+### 26.05.2024
 
 - Added more diverse backgrounds (natural landscapes, streets, houses)
 - Added more close-up images
+- Added shadow augmentation