Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and Visual Analysis Strategy
Hong Zhang, Yixuan Lyu, Qian Yu, Hanyang Liu, Huimin Ma, Yuan Ding, Yifan Yang*
Abstract
"In the domain of Camouflaged Object Segmentation (COS), despite continuous improvements in segmentation performance, the underlying mechanisms of effective camouflage remain poorly understood, akin to a black box. To address this gap, we present the first comprehensive study to examine the impact of camouflage attributes on the effectiveness of camouflage patterns, offering a quantitative framework for the evaluation of camouflage designs. To support this analysis, we have compiled the first dataset comprising descriptions of camouflaged objects and their attribute contributions, termed COD-Text And X-attributions (COD-TAX). Moreover, drawing inspiration from the hierarchical process by which humans process information: from high-level textual descriptions of overarching scenarios, through mid-level summaries of local areas, to low-level pixel data for detailed analysis. We have developed a robust framework that combines textual and visual information for the task of COS, named Attribution CUe Modeling with Eye-fixation Network (ACUMEN). ACUMEN demonstrates superior performance, outperforming nine leading methods across three widely-used datasets. We conclude by highlighting key insights derived from the attributes identified in our study. Code: https://github.com/lyu-yx/ACUMEN."
BibTeX
@inproceedings{eccv2024_unlockingattribu,
title = {Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and Visual Analysis Strategy},
author = {Hong Zhang and Yixuan Lyu and Qian Yu and Hanyang Liu and Huimin Ma and Yuan Ding and Yifan Yang*},
booktitle = {ECCV 2024},
year = {2024}
}