MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection
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 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 via a Pyramid Vision Transformer backbone and combines them via specialized Attention-Based Scale Integration Units, enabling 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 datasets for camouflaged object detection and ranks second on the remaining two. Our codes, model weights, and results are available at https://github.com/linaagh98/MSRNet{https://github.com/linaagh98/MSRNet}.
69250b1a16eb3a9f131038db
Leena Alghamdi
claimed_verified
2025-11-25T09:05:47.384Z
65becdfd443909bf23265819
/avatars/b2f493c1315822a184901440b26ff6e8.svg
Leena Alghamdi
linaa98
user
linaa98
69250b1a16eb3a9f131038dc
Muhammad Usman
69250b1a16eb3a9f131038dd
Hafeez Anwar
69250b1a16eb3a9f131038de
Abdul Bais
69250b1a16eb3a9f131038df
Saeed Anwar
MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection
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 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 via a Pyramid Vision Transformer backbone and combines them via specialized Attention-Based Scale Integration Units, enabling 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 datasets for camouflaged object detection and ranks second on the remaining two. Our codes, model weights, and results are available at https://github.com/linaagh98/MSRNet{https://github.com/linaagh98/MSRNet}.
A Multi-Scale Recursive Network using a Pyramid Vision Transformer and specialized units improves camouflaged object detection by enhancing feature extraction and recursive feature refinement.
Pyramid Vision Transformer Attention-Based Scale Integration Units Multi-Granularity Fusion Units recursive-feedback decoding strategy multi-scale learning recursive feature optimization