Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and VisualAnalysis Strategy

Hong Zhang, Yixuan Lyu, Qian Yu, Hanyang Liu, Huimin Ma, Ding Yuan, Yifan Yang

TL;DR

This paper addresses the opaque mechanisms of camouflage effectiveness in Camouflaged Object Segmentation (COS) by introducing a quantitative, attribute-centric framework. It proposes ACUMEN, a dual-branch model that leverages textual scene descriptions during training and visual cues at inference, combined with the new COD-TAX dataset that associates camouflage attributes with their contributions. Key contributions include (i) COD-TAX for cross-modal, attribute-based camouflage analysis, (ii) the ACUMEN architecture with Fixation Prediction, Attributes Contribution Prediction, and an Attributes-Fixation Embedding module, and (iii) state-of-the-art performance on CAMO, COD10K, and NC4K with comprehensive ablations and visualizations. The work demonstrates that explicit attribute analysis enhances COS and provides insights for camouflage design and counter-detection, with code available for reproducibility.

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.

Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and VisualAnalysis Strategy

TL;DR

This paper addresses the opaque mechanisms of camouflage effectiveness in Camouflaged Object Segmentation (COS) by introducing a quantitative, attribute-centric framework. It proposes ACUMEN, a dual-branch model that leverages textual scene descriptions during training and visual cues at inference, combined with the new COD-TAX dataset that associates camouflage attributes with their contributions. Key contributions include (i) COD-TAX for cross-modal, attribute-based camouflage analysis, (ii) the ACUMEN architecture with Fixation Prediction, Attributes Contribution Prediction, and an Attributes-Fixation Embedding module, and (iii) state-of-the-art performance on CAMO, COD10K, and NC4K with comprehensive ablations and visualizations. The work demonstrates that explicit attribute analysis enhances COS and provides insights for camouflage design and counter-detection, with code available for reproducibility.

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.
Paper Structure (22 sections, 9 equations, 7 figures, 3 tables)

This paper contains 22 sections, 9 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Camouflaged Object Segmentation
  4. Large Vision-Language Models
  5. The COD-TAX Dataset
  6. Text and X-Attributes (TAX) Collecting
  7. Annotation and Refinement Process
  8. Dataset Features and Statistics
  9. Methods
  10. Network Overall
  11. Fixation Prediction
  12. Attributes' Contribution Prediction
  13. Attributes-Fixation Embedding
  14. Mask Predicting
  15. Total Loss Function
  16. ...and 7 more sections

Figures (7)

  • Figure 1: Overview of the COD-TAX dataset distribution: (a) 17 attribute classes in three categories, with proportions showing average contributions and Max indicating highest occurrences. (b) Textual description lengths, (c) word cloud of word frequency, and (d) two COD-TAX examples.
  • Figure 2: Overall structure of the proposed ACUMEN. The model utilizes both a textual branch and a visual branch, with the textual branch active only during training for practical usage.
  • Figure 3: Fixation prediction decoder.
  • Figure 4: Attributes-Fixation Embedding structure.
  • Figure 5: Qualitative comparison of ACUMEN with SOTA methods.
  • ...and 2 more figures