Update README.md

README.md

@@ -60,7 +60,7 @@ in orange. In the reference branch, the common representation of a specified obj
 features with the foreground map generated by a SOD network. In the segmentation branch, the visual features from the last three layers of the
 encoder are employed to represent the given image. Then, these two kinds of feature representations are fused and compared in the well-designed
 RMG module to generate a mask prior, which is used to enrich the visual feature among different scales to highlight the camouflaged targets in our
-RFE module. Finally, the enriched features are fed into the decoder to generate the final segmentation map. DSF:Dual-source Information Fusion, MSF: Multi-scale Feature Fusion, TM: Target Matching.
+RFE module. Finally, the enriched features are fed into the decoder to generate the final segmentation map. DSF: Dual-source Information Fusion, MSF: Multi-scale Feature Fusion, TM: Target Matching.
     </em>
 </p>
 
@@ -92,7 +92,7 @@ python train.py --model_name r2cnet --gpu_id 0
 </p>
 
 ## Contact
-For technical questions, feel free to contact `[email protected]` and `[email protected]`.
+For technical questions, feel free to contact [[email protected]]() and [[email protected]]().
 
 ## Citation
 If our work is helpful to you or gives some inspiration to you, please star this project and cite our paper. Thank you!