Update README.md

README.md

@@ -21,8 +21,7 @@ The model is built using PyTorch on an NVIDIA RTX A6000 GPU with a total memory
 9. [Acknowledgement](#acknowledgment)
 
 ## Introduction
-Camouflaged object detection is an emerging and challenging computer vision task that requires identifying and segmenting objects that blend seamlessly into their environments due to high similarity in color, texture, and size. This task is further complicated by low-light conditions, partial occlusion, small object size, intricate background patterns, and the presence of multiple objects. While many sophisticated methods have been proposed for this task, current methods still struggle to precisely detect camouflaged objects in complex scenarios, especially with small and multiple objects, indicating room for improvement. We propose a Multi-Scale Recursive Network that extracts multi-scale features using a Pyramid Vision Transformer backbone and combines these features through specialized Attention-Based Scale Integration Units, allowing for selective feature merging. For more precise object detection, our decoder recursively refines features by incorporating Multi-Granularity Fusion Units. A novel recursive-feedback decoding strategy is developed to enhance global context understanding, helping the model overcome the challenges in this task. By jointly leveraging multi-scale learning and recursive feature optimization, our proposed method achieves performance gains, successfully detecting small and multiple camouflaged objects. Our model achieves state-of-the-art results on two benchmark camouflaged object detection datasets and ranks second on the remaining two.
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+We introduce a Multi-Scale Recursive Network that utilizes a Pyramid Vision Transformer backbone to extract multi-scale features. This network employs Attention-Based Scale Integration Units for selective feature merging, and a recursive decoding strategy incorporating Multi-Granularity Fusion Units to refine features and enhance global context. Our approach leverages multi-scale learning and recursive optimization, achieving state-of-the-art performance on benchmark datasets for detecting small and multiple camouflaged objects.
 ## Network
 This diagram illustrates the overall architecture of MSRNet. 
 ![Methodology](Images/MethodologyDiagram.png)