GreenCOD: A Green Camouflaged Object Detection Method
Hong-Shuo Chen, Yao Zhu, Suya You, Azad M. Madni, C.-C. Jay Kuo

TL;DR
GreenCOD introduces a novel, efficient camouflaged object detection method that avoids backpropagation, leveraging gradient boosting and deep features to achieve high performance with fewer computational resources.
Contribution
It presents the first backpropagation-free camouflaged object detection approach using gradient boosting and deep features, reducing complexity and computational demands.
Findings
Achieves state-of-the-art performance without backpropagation.
Requires fewer than 20G MACs for training.
Offers a more efficient paradigm for camouflaged object detection.
Abstract
We introduce GreenCOD, a green method for detecting camouflaged objects, distinct in its avoidance of backpropagation techniques. GreenCOD leverages gradient boosting and deep features extracted from pre-trained Deep Neural Networks (DNNs). Traditional camouflaged object detection (COD) approaches often rely on complex deep neural network architectures, seeking performance improvements through backpropagation-based fine-tuning. However, such methods are typically computationally demanding and exhibit only marginal performance variations across different models. This raises the question of whether effective training can be achieved without backpropagation. Addressing this, our work proposes a new paradigm that utilizes gradient boosting for COD. This approach significantly simplifies the model design, resulting in a system that requires fewer parameters and operations and maintains high…
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Taxonomy
TopicsImage Enhancement Techniques · Visual Attention and Saliency Detection · Remote-Sensing Image Classification
