Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects
Chunming He, Kai Li, Yachao Zhang, Yulun Zhang, Zhenhua Guo, Xiu Li,, Martin Danelljan, Fisher Yu

TL;DR
This paper introduces a game-inspired adversarial framework and a new detection method to improve camouflaged object detection by generating harder-to-detect objects and enhancing segmentation accuracy.
Contribution
It proposes Camouflageator, an adversarial training framework for generating challenging camouflaged objects, and ICEG, a novel COD method with coherence and edge guidance modules, advancing detection performance.
Findings
ICEG outperforms existing COD detectors.
Camouflageator enhances various COD detectors.
State-of-the-art performance achieved on benchmarks.
Abstract
Camouflaged object detection (COD) is the challenging task of identifying camouflaged objects visually blended into surroundings. Albeit achieving remarkable success, existing COD detectors still struggle to obtain precise results in some challenging cases. To handle this problem, we draw inspiration from the prey-vs-predator game that leads preys to develop better camouflage and predators to acquire more acute vision systems and develop algorithms from both the prey side and the predator side. On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect. Camouflageator trains the generator and detector in an adversarial way such that the enhanced auxiliary generator helps produce a stronger detector. On the predator side, we introduce a novel COD…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Image Enhancement Techniques
