camouflaged object detection (COD) is a challenging task that identifies camouflagedobjects from highly similar backgrounds. Existing methods typically treat the whole object equally while neglecting the indistinguis...
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ISBN:
(纸本)9798350390155;9798350390162
camouflaged object detection (COD) is a challenging task that identifies camouflagedobjects from highly similar backgrounds. Existing methods typically treat the whole object equally while neglecting the indistinguishable regions that require more attention than other regions. In this paper, we propose a Fuzzy Boundary-Guided Network (FBG-Net) for camouflaged object detection, which mimics the human behavior that pays more attention to these low-confidence regions when observing objects. Specifically, we devise two main building blocks: (1) Mixed Semantics Aggregation Module (MSAM) to integrate boundary and texture features cumulatively in the high-to-low scales, and (2) Fuzzy Boundary-Guided Module (FBGM) to locate and enhance the low-confidence regions under the guidance of fuzzy boundary. Extensive experiments demonstrate the effectiveness of FBG-Net with superior performance to existing state-of-the-art methods. Code is available at https://***/YAOSL98/FBG-Net.
camouflagedobjects, exhibiting high similarity with their surroundings, pose a substantial challenge for both humans and machines to detect when concealed within the environment. Existing methods for camouflage objec...
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ISBN:
(纸本)9798350359329;9798350359312
camouflagedobjects, exhibiting high similarity with their surroundings, pose a substantial challenge for both humans and machines to detect when concealed within the environment. Existing methods for camouflage objectdetection (COD) struggle in accurately segmenting the overall structure of camouflagedobjects. To address this issue, we propose a novel boundaryguided fusion of multi-level features network (BGFM-Net) for COD. In contrast to existing boundary-guided methods, we pay more attention to addressing the significant imbalance in the pixel quantities between boundary and background features, allowing for a more comprehensive representation of boundary features. BGFM-Net primarily consists of a multi-scale aggregation module (MSAM), a boundary-guided feature module (BFM), and a cross-Level fusion module (CLFM). MSAM effectively integrates contextual semantics at different scales, achieving a powerful and efficient feature representation. BFM adeptly combines edge features while constraining interference from background features, guiding the learning of camouflagedobject boundary representation. CLFM integrates multi-level features for predicting camouflagedobjects while adaptively adjusting channel weights to emphasize important channels and diminish the impact of less relevant channels for the task. Extensive experiments on three benchmark camouflage datasets demonstrate that our BGFM-Net outperforms other state-of-the-art COD models.
camouflaged object detection (COD) in computer vision is a complex task, focusing on identifying and delineating subjects that closely resemble their surroundings. The method finds extensive use across various sectors...
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ISBN:
(纸本)9798350354638;9798350354621
camouflaged object detection (COD) in computer vision is a complex task, focusing on identifying and delineating subjects that closely resemble their surroundings. The method finds extensive use across various sectors, including defense, healthcare, farming, and emergency services. This study provides an overview of deep learning approaches for detecting camouflaged targets, encompassing diverse strategies like bio-inspired techniques, feature integration, multi-task training, multi-modal data synthesis, progressive refinement, probabilistic modeling, and novel loss function designs. Then this paper discusses in detail the various deep-learning models and introduces the challenges of camouflage target detection. Several commonly used camouflaged target detection datasets are also analyzed and key metrics for evaluating model performance are discussed. Through organization and analysis, this paper aims to provide a broad perspective to help researchers advance and implement camouflaged target detection techniques.
Detecting camouflagedobjects is expected to be a challenging task due to the hard-distinguihsed boundaries of targets. Although existing learning-based methods have concentrated on utilizing boundary information to e...
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ISBN:
(纸本)9798350344868;9798350344851
Detecting camouflagedobjects is expected to be a challenging task due to the hard-distinguihsed boundaries of targets. Although existing learning-based methods have concentrated on utilizing boundary information to enhance camouflaged object detection, the absence of boundary difficulty estimation causes them to treat all boundary regions as equal, thereby making it more challenging to distinguish high intrinsic similarity boundary regions. To address this issue, by filtering redundant information on easy boundaries, we have proposed Edge Attention Network (EANet) to extract informative boundary knowledge. Specifically, we propose an Edgeattention Guidance module to prevent misleading segmentation by extracting critical boundary features. Then, Progressive Recognition module is proposed to progressively generate boundary-informative. The experimental results on three real-world datasets have demonstrated that our EANet outperforms existing methods across all three mertrics, while maintaining low computation.
camouflaged object detection is a challenging task due to the high visual similarity between the object of interest and its surroundings. While deep learning models have shown promising performance, the size and power...
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ISBN:
(纸本)9798350376975;9798350376968
camouflaged object detection is a challenging task due to the high visual similarity between the object of interest and its surroundings. While deep learning models have shown promising performance, the size and power requirements of most existing models make them unsuitable for deployment in resource-constrained devices. To alleviate this problem, we modified BGNet, a camouflaged object detection network, by replacing its backbone network Res2Net50 with a lighter neural network model such as EfficientNet and MobileNet. Replacing the backbone network with EfficientNetV2-Medium decreased the model size by 1.53x and GPU power consumption by 1.41x. To further reduce the memory footprint, we benchmarked different pruning and quantization algorithms on the resulting network. Our experiments show that applying l2-norm pruning followed by DoReFa quantization reduced the number of multiply-accumulate operations by 3.71x. Our proposed lightweight camouflaged object detection model performs better than the state-of-the-art BGNet, registering weighted F-measure scores of 0.777, 0.739, and 0.808 on CAMO, COD10K, and NC4K, respectively, compared to BGNet with scores of 0.749, 0.722, and 0.788, while also being lighter and requiring lower power.
camouflaged object detection (COD) targets the segmentation of objects hidden in intricate environments, a task complicated by the pronounced similarities between objects and their surroundings. The diverse appearance...
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ISBN:
(纸本)9798350390155;9798350390162
camouflaged object detection (COD) targets the segmentation of objects hidden in intricate environments, a task complicated by the pronounced similarities between objects and their surroundings. The diverse appearances of camouflagedobjects, such as different view angles, partial visibilities, and ambiguous forms, further exacerbate this challenge. To address these issues, we introduce the Hierarchically Aggregated Identification Transformer Network (HAITNet). HAITNet harnesses local and global features to refine object localization by employing multi-scale transformer features unified through the Feature Cascaded Fusion Module (FCFM). To tackle ambiguity from indistinct textures, we present the Graph-based Low-level Feature Enhancement Module (GLFEM) and Graph-based Feature Aggregation Module (GFAM). GLFEM enhances texture representation in ambiguous areas, while GFAM reduces false positives and refines prediction maps by discerning contextual relationships. Experimental results on three widely used datasets demonstrate that the proposed HAITNet outperforms the state-of-the-art approaches. Our code is available at https://***/underlmao/HAITNet.
camouflaged object detection (COD) aims to identify camouflagedobjects hiding in their surroundings, which is a valuable yet challenging task. The main challenge is that there are ambiguous semantic biases in the cam...
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camouflaged object detection (COD) aims to identify camouflagedobjects hiding in their surroundings, which is a valuable yet challenging task. The main challenge is that there are ambiguous semantic biases in the camouflagedobject datasets, which affect the results of COD. To address this challenge, we design a counter-factual intervention network (CINet) to mitigate the influences of ambiguous semantic biases and obtain accurate COD. Specifically, our CINet consists of three key modules, i.e., texture-aware interaction module (TIM), context-aware fusion module (CFM), and counterfactual intervention module (CIM). The TIM is designed to extract the refined textures for accurate localization, the CFM is proposed to fuse the multi-scale contextual features to enhance the detection performance, and the CIM is presented to learn more effective textures and make unbiased predictions. Unlike most existing COD methods that directly capture contextual features through the final loss function, we develop a counterfactual intervention strategy to learn more effective contextual textures. Extensive experiments on four challenging benchmark datasets demonstrate that our CINet significantly outperforms 31 state-of-the-art methods.
camouflaged object detection (COD) aims to accurately recognize targets in intricate environments that blend into the background. Although numerous camouflage object identification techniques have demonstrated effecti...
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ISBN:
(纸本)9798350359329;9798350359312
camouflaged object detection (COD) aims to accurately recognize targets in intricate environments that blend into the background. Although numerous camouflage object identification techniques have demonstrated effectiveness, they often possess a substantial number of parameters. Therefore, we propose a new lightweight edge-guided multilevel feature fusion camouflaged object detection network, codenamed as LEMFNet. Initially, we adopt a lightweight CNN network model for feature extraction to reduce model complexity. Subsequently, we introduced a neighborhood feature association module (NFAM) to integrate feature information from different stages to obtain complementary feature representations and enhance the overall model performance. Furthermore, to obtain a more complete object structure, we introduce a boundary aggregation module (BAM) to delve into the edge semantics associated with the target and integrate the edge features into the proposed edge-guided aggregation module (EGAM). Experiments on three challenging benchmarks demonstrate our approach, with fewer parameters, achieves comparable or superior performance, effectively balancing resource utilization and accuracy.
Recently, CNN-based camouflaged object detection methods are dedicated to improving detection performance, thereby ignoring the huge amount of parameters and computations it brings. And, current methods ignore the imp...
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ISBN:
(纸本)9798350390155;9798350390162
Recently, CNN-based camouflaged object detection methods are dedicated to improving detection performance, thereby ignoring the huge amount of parameters and computations it brings. And, current methods ignore the importance of the internal consistency of deep features and shallow features for generating discriminative features. To solve the above problems, we propose a novel lightweight network (FCENet) based on feature complementation and enhancement. Firstly, we design the Deep Feature Complementation (DFC) module and Shallow Feature Enhancement (SFE) module to process the deep features and shallow features, respectively. We utilize the DFC module to locate the object and the SFE module to provide more detailed information. Secondly, we design the boundary area enhancement (BAE) module and the feature fusion refinement (FFR) module to strengthen the learning of object boundaries, fuse and refine the enhanced deep and shallow features. Extensive experiments show that compared with existing cutting-edge baselines, our method achieves excellent detection performance
camouflaged object detection (COD) aims to segment objects that blend into their surrounding environment. However, low-level features in the shallow layers of neural networks, although rich in edge information, often ...
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ISBN:
(纸本)9798350390155;9798350390162
camouflaged object detection (COD) aims to segment objects that blend into their surrounding environment. However, low-level features in the shallow layers of neural networks, although rich in edge information, often contain a significant amount of redundant information, making it difficult to represent boundary details accurately. On the other hand, deep high-level features retain semantic information for object localization, but the gradual decrease in resolution can introduce biases in representing localization information. To address this issue, we propose a novel boundary and localization representation network (BLR-Net) that guides high-level features to focus on representing localization information while directing low-level features to emphasize boundary details. Firstly, we propose a multi-scale enhanced feature module (MEFM) to capture multi-scale information from backbone features and obtain aggregated feature representations. Next, we propose an extraction boundary module (EBM) that models object boundary features, providing essential boundary information. Subsequently, we introduce a guided learning module (GLM) that utilizes localization features to guide high-level features toward localization representation learning and boundary features to guide low-level features toward boundary representation learning. Finally, we propose a cross-level feature fusion module (CFFM) that aggregates contextual semantic information and gradually fuses multi-level fusion features from the bottom to the top to predict camouflagedobjects. Extensive experiments on four benchmark COD datasets demonstrate that BLR-Net outperforms other state-of-the-art COD models.
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