MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection

TL;DR

MSRNet introduces a multi-scale recursive network with attention and feedback mechanisms, significantly improving camouflaged object detection in complex scenarios by leveraging multi-scale features and recursive refinement.

Contribution

The paper presents a novel Multi-Scale Recursive Network with attention-based scale integration and recursive feedback decoding for enhanced camouflaged object detection.

Findings

Achieves state-of-the-art results on benchmark datasets.

Effectively detects small and multiple camouflaged objects.

Outperforms existing methods in complex scenarios.

Abstract

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 \href{https://github.com/linaagh98/MSRNet}{https://github.com/linaagh98/MSRNet}.