Simultaneously Localize, Segment and Rank the Camouflaged Objects
Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, and Nick Barnes, Deng-Ping Fan

TL;DR
This paper introduces a novel ranking-based camouflaged object detection network that localizes, segments, and ranks camouflaged objects by their conspicuousness, providing new insights into camouflage and animal evolution.
Contribution
It presents the first ranking-based COD network (Rank-Net) that simultaneously localizes, segments, and ranks camouflaged objects, along with a large testing dataset for evaluation.
Findings
Achieves state-of-the-art performance on COD tasks.
Provides a more interpretable model for camouflaged object detection.
Introduces a new dataset for evaluating COD models.
Abstract
Camouflage is a key defence mechanism across species that is critical to survival. Common strategies for camouflage include background matching, imitating the color and pattern of the environment, and disruptive coloration, disguising body outlines [35]. Camouflaged object detection (COD) aims to segment camouflaged objects hiding in their surroundings. Existing COD models are built upon binary ground truth to segment the camouflaged objects without illustrating the level of camouflage. In this paper, we revisit this task and argue that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage and evolution of animals, but also provide guidance to design more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of the camouflaged objects that make…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Olfactory and Sensory Function Studies · Image Enhancement Techniques
