Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and VisualAnalysis Strategy
<|reference_start|>Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and VisualAnalysis Strategy: 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.<|reference_end|>
@article{zhang2024unlocking,
    title={Unlocking Attributes' Contribution to Successful Camouflage: A Combined
  Textual and VisualAnalysis Strategy},
    author={Hong Zhang, Yixuan Lyu, Qian Yu, Hanyang Liu, Huimin Ma, Ding Yuan,
  Yifan Yang},
    journal={arXiv preprint arXiv:2408.12086},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12086},
    primaryClass={cs.CV cs.AI}
}