  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 performance
compared to state-of-the-art deep learning models. Remarkably, our models are
trained without backpropagation and achieve the best performance with fewer
than 20G Multiply-Accumulate Operations (MACs). This new, more efficient
paradigm opens avenues for further exploration in green, backpropagation-free
model training.
