Enhance model card: Add pipeline tag, paper & GitHub links, summarize abstract, update image paths

README.md

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+pipeline_tag: image-segmentation
+---
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 # MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection.
-This repository is for MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection, introduced in this [paper](http://arxiv.org/abs/2511.12810).
+This repository is for MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection, introduced in the paper [MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection](https://huggingface.co/papers/2511.12810).
+Code: https://github.com/linaagh98/MSRNet
 
 The model is built using PyTorch on an NVIDIA RTX A6000 GPU with a total memory size of 48 GB.
 
@@ -14,11 +19,11 @@ 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.
+Camouflaged object detection (COD) is a challenging computer vision task that aims to identify and segment objects blending into their environment. MSRNet proposes a Multi-Scale Recursive Network to extract and combine multi-scale features, using a Pyramid Vision Transformer backbone and Attention-Based Scale Integration Units. Its decoder refines features recursively with Multi-Granularity Fusion Units and a novel recursive-feedback decoding strategy to enhance global context. This approach improves detection of small and multiple camouflaged objects, achieving state-of-the-art results on several benchmark datasets.
 
 ## Network
 This diagram illustrates the overall architecture of MSRNet. 
-![Methodology](Images/MethodologyDiagram.png)
+![Methodology](https://raw.githubusercontent.com/linaagh98/MSRNet/main/Images/MethodologyDiagram.png)
 
 ## Data Preparation
 In this research, we utilized four benchmark datasets for camouflaged object detection (CAMO, CHAMELEON, COD10K, and NC4K).
@@ -115,14 +120,9 @@ The prediction results of our highest-performing model (PvtV2B4_MSRNet) on [CAMO
 ### MSRNet Visual Results
 
 A visual Comparison showing the superiority of MSRNet in detecting multiple (rows 1-3), small (rows 4 and 5), and tiny (rows 6 and 7) camouflaged objects.
-![visual-Comparison](Images/VisualComparison.png)
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+![visual-Comparison](https://raw.githubusercontent.com/linaagh98/MSRNet/main/Images/VisualComparison.png)
 
  
 
 ## Acknowledgment
-This project builds upon the work of [Lart Pang](https://github.com/lartpang), incorporating key modifications to the decoding strategy and input scales to enhance its ability to detect small and multiple camouflaged objects. 
+This project builds upon the work of [Lart Pang](https://github.com/lartpang), incorporating key modifications to the decoding strategy and input scales to enhance its ability to detect small and multiple camouflaged objects.