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Simultaneously Localize, Segment and Rank the Camouflaged Objects
Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, Deng-Ping Fan
TL;DR
Camouflaged object detection traditionally segments objects without representing camouflage level or the regions that make them detectable. The paper introduces Rank-Net, which jointly localizes, segments, and ranks camouflaged objects, and reports state-of-the-art performance with a more interpretable network and a new testing set.
Problem
Existing COD models use binary ground truth and therefore do not represent camouflage level, while detectable object regions are not explicitly modeled.
Method
Rank-Net jointly learns discriminative-region localization, full-object segmentation, and camouflage ranking using fixation-derived annotations and ranking labels.
Results
The joint learning framework achieves state-of-the-art performance, and NC4K provides 4,121 testing images for evaluating generalization.
Takeaways & Limitations
The produced discriminative-region and rank maps provide insights into camouflage, while the testing set supports evaluation of COD model generalization.
Takeaways & Limitations
The dataset construction assumes that longer observer detection delay indicates a higher camouflage level, with multiple observers used for robust camouflage-level estimates.
Abstract
from arXiv · showhide
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 them detectable by predators. With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects. The localization model is proposed to find the discriminative regions that make the camouflaged object obvious. The segmentation model segments the full scope of the camouflaged objects. And, the ranking model infers the detectability of different camouflaged objects. Moreover, we contribute a large COD testing set to evaluate the generalization ability of COD models. Experimental results show that our model achieves new state-of-the-art, leading to a more interpretable COD network.
1. Introduction
The paper extends camouflaged object detection beyond binary segmentation by modeling camouflage level and discriminative regions, introducing a joint framework and a new testing dataset.
- Camouflaged object detection has potential applications in insect control, medical image segmentation, and detection against similar backgrounds.
- Existing COD models use binary ground truth, revealing object existence but not its camouflage level.
- Camouflaged object ranking estimates detectability, while discriminative region localization identifies parts that make objects apparent to predators.Relevant patterns may include faces, eyes, antennae, or contrasting body edges.
- Observers’ detection delay supplies ranking labels: longer discovery time indicates a higher camouflage level.The relabeled fixation dataset also estimates discriminative regions.
- NC4K contributes 4,121 Internet images as a testing set for evaluating existing models’ generalization ability.
- Rank-Net jointly localizes discriminative regions, segments camouflaged objects, and ranks their camouflage levels.The framework is trained end-to-end and combines the ranking task with joint localization and segmentation.
2. Related Work
Related work covers camouflaged-object datasets, deep COD models, ranking-based dense prediction, and discriminative-region localization, while identifying omitted camouflage-level information as a gap.
- Camouflaged object detection dataset: Existing camouflaged-object datasets mainly provide binary ground truth, whereas this work adds ranking and discriminative-region annotations.The paper also contributes a 4,121-image testing dataset.
- Camouflaged object detection: Earlier COD methods use deep networks to model complex camouflage but do not account for observers’ varying search times and camouflage degrees.
- Ranking based dense prediction models: Saliency-ranking methods predict masks or attention orderings from relative annotations collected across observers.
- Ranking based dense prediction models: Unlike within-image saliency ranking, camouflage ranking is progressive across the dataset and is generated from multiple observers’ median fixation times.
- Discriminative region localization technique: Discriminative-region methods estimate image regions important for classification, providing a basis for locating camouflage patterns that aid detection.
3. Our Method
The method introduces datasets and a unified framework for localizing discriminative regions, segmenting camouflaged objects, and ranking their camouflage difficulty. Eye-tracking detection delays provide ranking labels, while joint decoders and task-specific losses support localization, segmentation, and instance ranking.
- 3.1. The new dataset: CAM-FR reprocesses CAMO and COD10K images with fixation and ranking annotations derived from observers’ detection delays.Six observers viewed each image; the final delay for each instance is the median across observers.
- 3.1. The new dataset: CAM-FR contains 2,000 training images and 280 testing images, with rank 1 hardest, rank 2 median, and rank 3 easiest.Rank 0 denotes background.
- 3.2. Joint localization and segmentation: The joint localization and segmentation network defines discriminative regions as object regions that make camouflaged objects apparent and have higher contrast with their surroundings.A fixation decoder predicts discriminative regions, while reverse attention guides the camouflage decoder toward segmentation.
- 3.2. Joint localization and segmentation: Fixation and camouflage decoders share a decoder structure, with dual residual attention combining position and channel attention and denseASPP providing multi-scale receptive fields.The framework uses ResNet50 feature representations from multiple backbone stages.
- 3.3. Inferring the ranks of camouflaged objects: The ranking framework uses ResNet50, feature pyramid integration, and an RPN to propose camouflaged-object regions before ranking and instance segmentation.The RPN classifies candidate boxes and regresses their coordinates; the rank model infers camouflage rank and object location.
- 3.3. Inferring the ranks of camouflaged objects: A 4 × 4 label-similarity prior weights ranking loss according to the penalty for predicting rank n as rank m, reflecting progressive camouflage difficulty.The prior penalizes larger rank errors more strongly, such as predicting rank 3 instead of rank 2 for a rank-1 instance.
4. Experimental Results
Experiments evaluate the proposed framework for discriminative-region localization, camouflaged-object detection, and camouflage ranking, including joint-learning ablations and context modeling. Results indicate effective localization, detection, and ranking, with joint learning benefiting fixation and camouflaged prediction more than ranking.
- Experimental setup: The framework is trained and evaluated across camouflaged-object detection, discriminative-region localization, and camouflage ranking tasks.CAM-FR provides the training basis for simultaneous prediction, while separate task experiments evaluate individual performance.
- Evaluation metrics: rMAE evaluates ranking by comparing predicted and ground-truth pixel ranks, assigning larger penalties to larger rank errors.Ranks 0, 1, 2, and 3 represent background, hardest, median, and easiest camouflage, respectively.
- Performance comparison: The model identifies discriminative regions such as animal heads and salient patterns, while also producing camouflaged-object detection and ranking maps.Figure 5 presents fixation, detection, and ranking predictions from top to bottom.
- Performance comparison: The proposed ranking model outperforms MS-RCNN on MAE and rMAE, while SOLOv2 achieves comparable performance.The ranking prediction uses camouflage levels represented in the testing results.
- Ablation Study: Joint training consistently improves fixation and camouflaged prediction over their corresponding single-task baselines, whereas ranking performance remains similar to ranking-only training.This indicates that ranking benefits less from the other two tasks in the framework.
- Ablation Study: Removing dual residual attention modules slightly decreases performance, supporting their role in providing global context and discriminative feature representation.The paper attributes this benefit to the context-based nature of camouflaged-object detection.
5. Conclusion
The paper introduces discriminative-region localization and camouflage ranking alongside segmentation, combining them in a joint framework to analyze and predict camouflage. It reports state-of-the-art performance and presents NC4K for evaluating model generalization.
- 5. Conclusion: The paper introduces camouflaged-object discriminative-region localization and ranking as new tasks with corresponding relabeled datasets.Localization targets regions that make camouflaged objects detectable, while ranking represents their camouflage level.
- 5. Conclusion: A joint-learning network simultaneously localizes, segments, and ranks camouflaged objects.The framework combines the three predictions within one model.
- 5. Conclusion: The proposed joint-learning framework achieves state-of-the-art performance and produces discriminative-region and rank maps for interpreting camouflage.These outputs are described as providing insights into the nature of camouflage.
- 5. Conclusion: NC4K is introduced as a testing dataset intended to better evaluate the generalization ability of camouflaged-object detection models.The conclusion identifies dataset-based evaluation as a further contribution.