# DPSNet: A Detail Perception Synergistic Network for Camouflaged Object Detection

Xiaofei Li, Sheng Long, Jiaxin Yang, Jun Lei, Shuohao Li, Jun Zhang,

Laurent D. Cohen

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## To cite this version:

Xiaofei Li, Sheng Long, Jiaxin Yang, Jun Lei, Shuohao Li, et al.. DPSNet: A Detail Perception Synergistic Network for Camouflaged Object Detection. IEEE Transactions on Instrumentation and Measurement, 2024, 74 (5002114), pp.1-14. 10.1109/TIM.2024.3497181. hal-05339332

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# DPSNet: A Detail Perception Synergistic Network for Camouflaged Object Detection

Xiaofei Li, Sheng Long, Jiaxin Yang, Jun Lei, Shuohao Li, Jun Zhang, and Laurent D. Cohen
*Fellow, IEEE*

**Abstract**—Camouflaged object detection (COD) is a challenging task that aims to identify objects that blend into their backgrounds. This has various applications in computer vision, including military surveillance, animal behavior analysis, and image editing. However, existing COD methods often face issues of ambiguous boundaries and inaccurate localization due to ineffective feature fusion and boundary guidance. To address these challenges, we propose a new hierarchical synergistic prediction framework, named Detail Perception Synergistic Network (DPSNet), which leverages an incremental strategy and a novel detail perception loss for accurate COD. Specifically, our DPSNet consists of a baseline network and an enhanced network, generating preliminary and fine-grained predictions, respectively. To adapt to the morphological variations of camouflaged objects, we design a Morphological Awareness Adaptation Module (MAAM) that adaptively fuses inconsistent morphological and texture features. To enhance the contextual semantic feature representations and obtain clear predictions, we propose a Semantic Aggregation Enhancement Module (SAEM) that aggregates contextual semantic features with a deep aggregate group with different receptive fields. Furthermore, to guide the network in learning boundary information for more accurate predictions, we present a Detail Perception Loss (DP Loss) that balances global information and hard samples. Without any additional tricks, extensive experiments demonstrate that our proposed DPSNet achieves real-time performance and significantly surpasses 37 state-of-the-art methods across four standard metrics on four widely used benchmark datasets. The code will be available at https://github.com/fxle/DPSNet.

**Index Terms**—Camouflaged object detection, computer vision, perceptual learning, deep learning

## I. INTRODUCTION

CAMOUFLAGE is a way of hiding or disguising animals or objects by blending them with their surroundings or making them look like something else, which helps both predators and prey to avoid being seen by each other [1], [2]. Camouflaged object detection (COD) is derived with the goal of accurately detecting and segmenting the objects that concealed themselves in surroundings. The concealed objects have the capability to make their body colors, textures or

Xiaofei Li, Sheng Long, Jiaxin Yang, Jun Lei, Shuohao Li, and Jun Zhang are with the Laboratory for Big Data and Decision, National University of Defense Technology, Hunan 410003, China (email: xf@nudt.edu.cn, longsheng@nudt.edu.cn, yangjiaxin0313@163.com, leijun1987@nudt.edu.cn, lishuohao@nudt.edu.cn, zhangjun1975@nudt.edu.cn)

Laurent D. Cohen is with University Paris Dauphine, PSL Research University, CNRS, UMR 7534, CEREMADE, 75016 Paris, France. (e-mail: cohen@ceremade.dauphine.fr)

Xiaofei Li and Sheng Long contributed equally to this work, *Corresponding author: Jun Zhang*.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Visual examples of camouflaged object detection. From top to bottom, three challenges of COD are shown: (i) the ambiguous object boundary, (ii) the high similar textures between foreground and background, (iii) the different scale sizes and shapes of objects. Our method outperforms FSPNet and get better performance.</figcaption></figure>

patterns similar with the environment, thus the camouflaged objects usually are difficult to find [3], [4]. The results of COD can be used as initial steps of many visual tasks, such as image segmentation/editing and retrieval [5], [6], visual tracking [7]–[9], and photo composition [10], [11]. Moreover, the research of COD has immediate applications, such as, art [3], polyp segmentation [12], COVID-19 infection identification from lung X-ray [13], unqualified products detection [14].

However, unlike generic object detection/segmentation tasks [5], [15]–[17], which mostly rely on distinctive features (i.e. salient features), boundaries (i.e., the contours of an object’s shape) or active contour models (i.e., techniques that iteratively adjust a contour to fit an object’s shape) to locate and segment objects, COD has to deal with the difficulty of finding objects that are deliberately hidden by camouflage strategy. Camouflage strategy is a biological phenomenon that works by deceiving and misleading the visual perceptual system of the observer [18], making the target object hard to see or disguising it as something else. Figure 1 illustrates some examples of camouflaged objects and the challenges they pose for COD, such as: (i) the ambiguous object boundary, which makes it hard to distinguish the object from the background; (ii) the high similarity of textures between foreground and background, which reduces the contrast and saliency of the object; (iii) the different scale sizes and shapes of objects, which increase the diversity and complexity of the object morphology.
To address these challenges, early methods of COD are mainly based on handcrafted low-level features (e.g., texture, color and motion), which have limited capability to distinguish the camouflaged or non-camouflaged objects, so these methods can only work in a few simple scenes. Recently, many significant efforts [19]–[21] have been proposed to detect/segment the camouflaged objects from complex natural scenes. In addition, a number of COD datasets [22]–[25] were constructed and shared for training the deep learning models. Although these deep learning-based efforts have achieved remarkable achievements so far, seldom of them completely consider the above challenges, so there are still inaccurate and incomplete boundary in the predicted maps, which inspires us to explore an effective way of accurate COD.

In this paper, we introduce a new model called the Detail Perception Synergistic Network (DPSNet), which effectively detects camouflaged objects and provides high-quality boundary delineation. DPSNet significantly enhances the performance of existing camouflaged object detection methods. The architecture of DPSNet is hierarchical and synergistic, designed to generate detailed representations of camouflaged objects. It consists of two main components: a baseline prediction sub-network and an enhanced prediction sub-network. In the baseline prediction sub-network, we utilize an encoder-decoder structure that incorporates a specially designed Morphological Awareness Adaptation Module (MAAM). This module helps the network adjust to the variations in scale, shape, and orientation of camouflaged objects, allowing it to make an initial coarse prediction. The enhanced prediction sub-network features a novel Semantic Aggregation Enhancement Module (SAEM). This module improves the contextual understanding of the features, leading to clearer predictions. It employs two MAAMs to refine the predictions further, creating a synergistic effect within the network. Additionally, we propose a detail perception loss function. This function guides the network to make more accurate predictions by simultaneously considering the gradients of the predicted output, the ground truth, and the edge map. This method encourages the network to focus on different pixel locations, thus enhancing the overall performance of camouflaged object detection. In summary, the main contributions can be summarized as follows:

* We propose a novel Detail Perception Synergistic Network (DPSNet), which, from the perspective of detail perception, uniquely designs a synergistic enhancement framework and introduces a detail perception loss function to focus on the detailed features of camouflaged objects, greatly improving the performance of camouflaged object detection.

* We design a Morphological Awareness Adaptation Module (MAAM) to adaptively fuses inconsistent morphological and texture features, and a Semantic Aggregation Enhancement Module (SAEM) to further enhance the context semantic feature representations. Both of them are benefit for improving the COD performance.

* We present a detail perception loss that can pay more attention to boundary pixels by taking the gradient of the prediction, the ground truth and the edge map simultane-

ously, which can induce the network to focus on pixels at different locations thus improving the performance of camouflaged object detection.

* Extensive experiments demonstrate that our DPSNet achieves state-of-the-art camouflaged object detection performance on four benchmark datasets in the terms of four evaluation metrics. Qualitative and quantitative results demonstrate the effectiveness of our method.

## II. RELATED WORK

### A. Camouflaged Object Detection

Camouflaged object detection (COD) is a novel and challenging task in computer vision, which aims to identify objects that are visually blended with their surroundings. Although early studies adopt various handcrafted low-level features such as texture [26], motion [27], and 3D convexity [28] for COD, they only work well in some simple and non-uniform backgrounds. Therefore, in order to overcome this challenge, more and more efficient deep learning methods have been developed for the COD task. For instance, Le et al. [23] construct the CAMO dataset of camouflaged objects and propose an end-to-end network for camouflaged object detection by integrating classification information into segmentation. Li et al. [24] conduct a comprehensive study on COD and propose SINet, which includes a search module to gradually locate the candidate regions and an identification module to search for the camouflaged object. Recently, Zhang et al. [29] delve into the intrinsic mechanisms underlying predation behavior, providing a more profound understanding of the challenges inherent in camouflaged object detection. Cong et al. [30] propose a novel learnable and separable frequency-aware mechanism driven by a frequency-domain semantic hierarchy for COD.

### B. Contextual Feature Learning

Several previous works on visual tasks [20], [31], [32] prove that multi-scale feature learning can provide rich contextual features and play a vital role in the success of COD. Consequently, numerous studies focus on researching more efficient and effective methods to fuse low-level features with detailed textural information and high-level features with high semantic clues. For instance, Zhang et al. [33] design a multi-scale context-aware feature extraction module to capture rich contextual information for multi-level features to localize salient objects of various scales. Pang et al. [34] present a multi-scale interactive network (MINet) for salient object detection, embedding self-interaction modules in each decoder unit to obtain more efficient multi-scale features from the integrated features. Gupta et al. [35] propose an adjacent-layer attention block to calibrate and integrate feature maps at the current resolution. Huang et al. [36] propose a multi-feature aggregated network (MFAN) that combines edge features from the shallow network with multi-level salient features from the encoder layer, refining the fused features through a residual refinement module. Unlike existing contextual feature learning methods that use concatenation or local attention mechanisms, we design a new Morphological Awareness Adaptation Module (MAAM) to perceive morphological features of different
<figure><img src="image_4.png" /><figcaption>Fig. 2. An overview of our DPSNet and its two key modules: Morphological Awareness Adaptation Module (MAAM) and Semantic Aggregation Enhancement Module (SAEM).</figcaption></figure>

scales by modeling long-range dependencies in various orientations and positions and focusing on both global and local texture features.

### C. Boundary-aware Learning

In recent years, it has been found that making full use of edge information can improve the performance of salient boundary prediction [37]–[39]. Zhao et al. [40] propose EG-Net, which explicitly models complementary salient object information and salient edge information within the network to preserve the salient object boundary. Zhou et al. [41] design a multi-stage and dual-path network structure to estimate the salient boundary and regions from the low-level and high-level feature maps, respectively. Li et al. [42] propose a novel boundary information-guided hierarchical feature fusion network (HFFNet), which utilizes low-level boundary information to guide saliency map generation, with boundary guidance fusion effectively identifying salient regions. Sun et al. [43] develop a U-shape edge network (UEN) block to fuse boundary information with features extracted by the backbone. Qin et al. [44] propose a predict-refine architecture, BASNet, and a new hybrid loss for boundary-aware salient object detection, which can effectively segment salient object regions and accurately predict fine structures with clear boundaries. Wan et al. [39] propose an edge-aware refinement module to extract boundary cues and aggregate the semantic/spatial/edge features. Unlike existing methods that naively use edge maps

and basic loss functions for boundary feature learning, we design a new detail perception loss to specifically supervise the model in learning edge information, thereby making clear boundary predictions.

## III. PROPOSED METHOD

### A. Motivation

When humans try to identify camouflaged objects in cluttered environments, they often begin by noticing a part of the object within the surrounding scene. They then carefully examine and search for additional details until they can clearly see the entire boundary of the camouflaged object. Inspired by this natural process, we developed a hierarchical synergistic perception framework aimed at enhancing camouflaged object detection (COD) by providing more precise identification of these objects. In our framework, we introduce the Morphological Awareness Adaptation Module (MAAM), which helps the model adjust to the various shapes and forms of camouflaged objects. To improve the understanding of contextual features and achieve clearer predictions, we also designed the Semantic Aggregation Enhancement Module (SAEM). This module uses a deep aggregation method with different receptive fields to learn more comprehensive contextual information. Furthermore, to enhance prediction accuracy, we created a detail perception loss function that effectively balances the use of global information with challenging sample cases. This
combination aims to refine the detection process, leading to better performance in identifying camouflaged objects.

## B. Overall Architecture

The overall architecture of the proposed DPSNet is shown in Figure 2, which consists of a baseline prediction sub-network and an enhanced prediction sub-network. Specifically, in the baseline prediction sub-network, we utilize ResNet-50 [45] as the backbone to extract multi-level features (i.e., $f_1, f_2, f_3, f_4$), the last three-level features are fed into three 3 × 3 convolutional blocks with a dilation rate of 3 to effectively increase the receptive field and capture more contextual feature representations, then the proposed MAAM is added to each convolution layer to fuse these multi-scale features to obtain the preliminary prediction. After that, in the enhanced prediction sub-network, we merge the preliminary prediction map and the feature map $f_2$ by using a holistic attention (HA) module [46] to obtain the refined features to feed two new convolutional blocks following the baseline prediction sub-network. Next, three 3 × 3 convolutional blocks with a dilation rate of 3 and three MAAMs are applied to enhance the contextual features. Finally, the proposed SAEM is adopted to further aggregate the multi-scale contextual features to obtain the fine-grained prediction.

## C. Morphological Awareness Adaptation Module

Camouflaged objects often display a wide range of shape variations, including differences in size, form, and orientation, which can significantly reduce the effectiveness of existing camouflaged object detection methods. To address this challenge, we developed the Morphological Awareness Adaptation Module (MAAM), which helps the model adapt to the varying shapes and textures of camouflaged objects by modeling long-range dependencies in different orientations and positions within the image, while focusing on both global and local texture features. This module adaptively combines inconsistent morphological and texture information, thereby reducing the impact of size variations on the camouflaged objects and leading to improved performance in their detection.

Specifically, as shown in Figure 3, we first up-sample the high-level morphological features $F_{high}$ twice and concatenate them with the low-level morphological features $F_{low}$ to obtain the fused morphological features $F$. Then, we employ two spatial extents of global average pooling kernels, $(1, W)$ and $(H, 1)$, to encode each channel along the horizontal and vertical coordinates, respectively.

Next, we fuse the features from these two spatial extents through a concatenation operation. We then apply a 1 × 1 convolutional layer, Batch Normalization, and a ReLU activation. Finally, we split the intermediate features along the spatial dimension into separate tensors and apply a 1 × 1 convolutional layer and a sigmoid function to each of them, respectively. In this manner, the long-range dependencies and precise positional information $A_p$ from two directions can be obtained as follows:

$$A_p = F \odot \sigma(f_1(g_1(G^h(F)))) \odot \sigma(f_2(g_2(G^v(F)))) \quad (1)$$

<figure><img src="image_5.png" /><figcaption>Fig. 3. Detailed structure of the Morphological Awareness Adaptation Module (MAAM).</figcaption></figure>

where $G^h(\cdot)$ and $G^v(\cdot)$ respectively denote global average pooling operations. $g_1(\cdot)$ and $g_2(\cdot)$ are two 1 × 1 convolutional layers, respectively. $f_1(\cdot)$ and $f_2(\cdot)$ are combinations of Batch Normalization and ReLU activation functions. $\sigma(\cdot)$ is the Sigmoid function. $\odot$ represents element-wise multiplication.

At the same time, we divide $X'$ into two additional branches to handle contextual texture features by modeling both global and local texture information. As shown in Figure 3, the features in both branches are processed using point-wise convolution, ReLU activation, and another point-wise convolution. The difference lies in the global branch, which includes an additional global average pooling layer to emphasize contextual information. The obtained global and local attention weights can be described as follows:

$$A_{gl} = F \odot \sigma(f_g(g(GAP(F))) + f_l(g(F)))) \quad (2)$$

$GAP(\cdot)$ denotes the global average pooling operation. $g(\cdot)$ represents the convolutional operation. $f_g(\cdot)$ and $f_l(\cdot)$ respectively denote the operations $ReLU(Conv(\cdot))$.

Finally, we can get the morphological awareness adaptation features as follows:

$$F_{fusion} = g(h(\sigma(A_p + A_{gl}) \odot (A_p + A_{gl}))) \quad (3)$$

where $h(\cdot)$ denotes the global average pooling operation $GAP(\cdot)$. $g(\cdot)$ represents the 3 × 3 convolutional operation.

## D. Semantic Aggregation Enhancement Module

In the baseline prediction sub-network, we introduce the Morphological Awareness Adaptation Module to adapt to the morphological features of different scales and generate a preliminary prediction. However, this preliminary prediction often contains ambiguous semantic regions due to the high similarity in texture features between the foreground and background. To tackle this issue, we developed the Semantic Aggregation Enhancement Module (SAEM) to further improve the contextual semantic features and achieve clearer predictions. While different morphological features carry rich contextual information, treating these features equally across channels can limit the representational capability of convolutional neural networks (CNNs). To overcome this limitation, the SAEM is designed with convolutional layers that utilize various kernel sizes, enabling the model to effectively capture objects
<figure><img src="image_6.png" /><figcaption>Fig. 4. Detailed structure of the Semantic Aggregation Enhancement Module (SAEM).</figcaption></figure>

with diverse morphological characteristics and to emphasize contextual semantic features through a deep residual structure.

Specifically, we integrate convolutional layers with different convolutional kernels into Residual Blocks (RBs). As shown in Figure 4, for the i-th RB in j-th aggregation groups (AG), which can be formulated as:

$$F_{i,j} = F_{i,j-1} + AG_k(AG_{k-1}(...(AG_0(F_{i,j-1})))) \cdot X_{i,j-1} \quad (4)$$

where $F_{i,j}$ and $F_{i,j-1}$ are the input and output of SAEM, $F_{i,j}$ is the feature that we want to learn from the input. $AG_{i,j}$ is the aggregation group.

In the aggregation group, we first split $F_{Ag}^k$ into two branches, then we use a set of $conv_{3 \times 3}$, $conv_{5 \times 5}$ recep- tive fields convolution layers, concatenation operation, and element-wise summation to highlight the foreground, and obtain the aggregation contextual features $F_{Ag}^{k-1}$ as follows:

$$F_{Ag}^{k-1} = f_5(f_1(C(f_2(F_{Ag_3}^k), f_3(C(F_{Ag_3}^k, F_{Ag_5}^k))))) + f_4(C(f_3(C(F_{Ag_3}^k, F_{Ag_5}^k)), f_2(F_{Ag_5}^k)))) \quad (5)$$

where $f_1$ to $f_5$ denote convolution layers, and C represents concatenation operation.

## E. Detail Perception Loss

The binary cross-entropy loss (BCE loss) is commonly used in segmentation tasks, such as medical image segmen- tation [47], [48] and salient object detection [44], [49]. It is defined as:

$$L_{BCE} = - \sum_{i}^{N} [g_i \log p_i + (1 - g_i) \log(1 - p_i)] \quad (6)$$

where $g_i \in \{0, 1\}$ is the ground truth and $p_i \in [0, 1]$ is the predicted result. $i \in N$ represents the pixels.

However, this loss function imposes a global constraint on each pixel independently, without focusing on the pixels near object boundaries. As a result, it fails to highlight the detailed regions of camouflaged objects and obtain clear boundaries. To address this limitation, we propose the Detail Perception Loss (DP Loss). As illustrated in Figure 5, the DP Loss incorporates

<figure><img src="image_7.png" /><figcaption>Fig. 5. Comparison between IoU Loss and our DP Loss Graph: Our Detail Perception Loss demonstrates the ability to concentrate on extracting intricate details from challenging samples, resulting in clearer and more comprehensive boundaries.</figcaption></figure>

edge information to specifically target challenging samples, extract fine boundary details, and mitigate background noise, leading to more distinct boundary segmentation compared to the IoU loss [44]. The new detail perception loss function ($L_{DP}$) is defined as follows:

$$L_{DP} = 2 \left[ \frac{\sum_{i}^{N} (p_i - p_i g_i e_i) + \sum_{i}^{N} (g_i - p_i g_i e_i)}{\sum_{i}^{N} p_i^2 + \sum_{i}^{N} g_i^2 + \sum_{i}^{N} e_i^2} + \frac{\sum_{i}^{N} (e_i - p_i g_i e_i)}{\sum_{i}^{N} p_i^2 + \sum_{i}^{N} g_i^2 + \sum_{i}^{N} e_i^2} \right] \quad (7)$$

where $p_i \in [0, 1]$ is the prediction, $g_i \in \{0, 1\}$ is the ground truth and $e_i \in \{0, 1\}$ is the edge map.

We take the derivatives of $L_{BCE}$ and $L_{DP}$ respectively to analyze their gradients, and their derivative formulas are as follows:

$$\frac{\partial L_{BCE}}{\partial p} = -\frac{g_i}{p_i} - \frac{1 - g_i}{1 - p_i} \quad (8)$$

$$\frac{\partial L_{DP}}{\partial p} = 2 \left[ \frac{1 - 3g_i e_i}{\sum_{i}^{N} p_i^2 + \sum_{i}^{N} g_i^2 + \sum_{i}^{N} e_i^2} - \frac{2p_i \sum_{i}^{N} (p_i + g_i + e_i - 3p_i g_i e_i)}{(\sum_{i}^{N} p_i^2 + \sum_{i}^{N} g_i^2 + \sum_{i}^{N} e_i^2)^2} \right] \quad (9)$$

From equation (8) and (9), we can see that $\partial L_{BCE}/\partial p$ only concentrates on the pixel of the single location. While $\partial L_{DP}/\partial p$ is connected to all the pixels of the prediction $p$, the ground truth $g$ and the edge map $e$. The term $1-3g_i e_i$ of equation (9) is relevant to the ground truth and the edge map. Thus, DP Loss has more gradients to ensure that the model focuses on more detailed information, especially boundary features. In this way, it facilitates the network to have enough capacity to distinguish between different categories, segmenting a clearer and more complete foreground area.

For a fair comparison, we adopt the widely used weighted binary cross-entropy loss ($L_{BCE}^w$) and weighted IoU loss ($L_{IoU}^w$) [50], as well as our proposed DP Loss, to optimize the two output predictions of our DPSNet, namely, the preliminary
<caption>TABLE I</caption>

QUANTITATIVE COMPARISON OF OUR PROPOSED DPSNET WITH 37 STATE-OF-THE-ART METHODS FOR COD ON FOUR BENCHMARK DATASETS USING FOUR POPULAR EVALUATION METRICS (I.E., S<sub>α</sub>, E<sub>φ</sub>, F<sub>β</sub><sup>ω</sup>, M), ↑ (OR ↓) INDICATES THAT THE HIGHER (OR THE LOWER) THE BETTER. SEGMAR-1 IS THE 1-ST ITERATIVE STAGE FOR FAIR COMPARISON. "-R": RESNET-50 [45]. "-R2": RES2NET-50 [51]. "-P": PVTv2 [52]. "-": NOT AVAILABLE. THE BEST RESULTS ARE MARKED IN **Bold**.

<table><thead><tr><th rowspan="2">Methods</th><th rowspan="2">Year</th><th colspan="4">CAMO-Test</th><th colspan="4">CHAMELEON</th><th colspan="4">COD10K</th><th colspan="4">NC4K</th></tr><tr><th>S<sub>α</sub>↑</th><th>F<sub>β</sub><sup>ω</sup>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th><th>S<sub>α</sub>↑</th><th>F<sub>β</sub><sup>ω</sup>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th><th>S<sub>α</sub>↑</th><th>F<sub>β</sub><sup>ω</sup>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th><th>S<sub>α</sub>↑</th><th>F<sub>β</sub><sup>ω</sup>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th></tr></thead><tbody><tr><td colspan="20" style="text-align:center; font-weight:bold;">CNN-Based Methods</td></tr><tr><td>SINet [24]</td><td>CVPR'20</td><td>0.751</td><td>0.606</td><td>0.100</td><td>0.771</td><td>0.869</td><td>0.740</td><td>0.044</td><td>0.891</td><td>0.771</td><td>0.551</td><td>0.051</td><td>0.806</td><td>0.808</td><td>0.723</td><td>0.058</td><td>0.872</td></tr><tr><td>R-MGL [53]</td><td>CVPR'21</td><td>0.775</td><td>0.673</td><td>0.088</td><td>0.847</td><td>0.893</td><td>0.813</td><td>0.030</td><td>0.923</td><td>0.814</td><td>0.666</td><td>0.035</td><td>0.865</td><td>0.833</td><td>0.739</td><td>0.053</td><td>0.893</td></tr><tr><td>PFNet [21]</td><td>CVPR'21</td><td>0.782</td><td>0.695</td><td>0.085</td><td>0.852</td><td>0.882</td><td>0.810</td><td>0.033</td><td>0.942</td><td>0.800</td><td>0.660</td><td>0.036</td><td>0.868</td><td>0.829</td><td>0.745</td><td>0.053</td><td>0.898</td></tr><tr><td>SLSR [25]</td><td>CVPR'21</td><td>0.787</td><td>0.696</td><td>0.080</td><td>0.854</td><td>0.890</td><td>0.822</td><td>0.030</td><td>0.948</td><td>0.804</td><td>0.673</td><td>0.037</td><td>0.892</td><td>0.840</td><td>0.766</td><td>0.048</td><td>0.907</td></tr><tr><td>UJSC [54]</td><td>CVPR'21</td><td>0.800</td><td>0.728</td><td>0.073</td><td>0.873</td><td>0.891</td><td>0.833</td><td>0.030</td><td>0.955</td><td>0.809</td><td>0.684</td><td>0.035</td><td>0.891</td><td>0.842</td><td>0.771</td><td>0.047</td><td>0.907</td></tr><tr><td>UGTR [55]</td><td>ICCV'21</td><td>0.784</td><td>0.684</td><td>0.086</td><td>0.851</td><td>0.888</td><td>0.794</td><td>0.031</td><td>0.940</td><td>0.817</td><td>0.666</td><td>0.036</td><td>0.890</td><td>0.839</td><td>0.746</td><td>0.052</td><td>0.899</td></tr><tr><td>TINet [56]</td><td>AAAI'21</td><td>0.781</td><td>0.678</td><td>0.087</td><td>0.847</td><td>0.874</td><td>0.783</td><td>0.038</td><td>0.916</td><td>0.793</td><td>0.635</td><td>0.043</td><td>0.848</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>C<sup>2</sup>F-Net [20]</td><td>IJCAI'21</td><td>0.796</td><td>0.719</td><td>0.080</td><td>0.854</td><td>0.888</td><td>0.828</td><td>0.032</td><td>0.935</td><td>0.813</td><td>0.686</td><td>0.036</td><td>0.890</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>BgNet [57]</td><td>IVC'21</td><td>0.822</td><td><strong>0.793</strong></td><td>0.073</td><td>0.873</td><td>0.884</td><td>0.835</td><td>0.031</td><td>0.934</td><td>0.836</td><td><strong>0.764</strong></td><td>0.031</td><td>0.898</td><td>0.852</td><td>0.814</td><td>0.047</td><td>0.897</td></tr><tr><td>SINet-V2 [19]</td><td>TPAMI'21</td><td>0.820</td><td>0.743</td><td>0.070</td><td>0.882</td><td>0.888</td><td>0.816</td><td>0.030</td><td>0.942</td><td>0.815</td><td>0.680</td><td>0.037</td><td>0.887</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>UR-SINet-V2 [58]</td><td>ACMMM'21</td><td>0.814</td><td>0.758</td><td>0.067</td><td>0.891</td><td>0.901</td><td>0.862</td><td>0.023</td><td>0.960</td><td>0.816</td><td>0.708</td><td>0.033</td><td>0.903</td><td>0.844</td><td>0.787</td><td>0.045</td><td>0.910</td></tr><tr><td>PreyNet [29]</td><td>ACMMM'22</td><td>0.790</td><td>0.763</td><td>0.077</td><td>0.854</td><td>0.895</td><td>0.859</td><td>0.028</td><td>0.951</td><td>0.813</td><td>0.731</td><td>0.034</td><td>0.894</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>C<sup>2</sup>F-F-Net-V2 [59]</td><td>TCSVT'22</td><td>0.765</td><td>0.680</td><td>0.087</td><td>0.838</td><td>0.878</td><td>0.822</td><td>0.031</td><td>0.938</td><td>0.766</td><td>0.640</td><td>0.042</td><td>0.872</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SegMaR-1 [60]</td><td>CVPR'22</td><td>0.805</td><td>0.724</td><td>0.072</td><td>0.864</td><td>0.892</td><td>0.823</td><td>0.028</td><td>0.937</td><td>0.813</td><td>0.682</td><td>0.035</td><td>0.880</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>ZoomNet [61]</td><td>CVPR'22</td><td>0.820</td><td>0.752</td><td>0.066</td><td>0.877</td><td>0.902</td><td>0.845</td><td>0.023</td><td>0.943</td><td><strong>0.838</strong></td><td>0.729</td><td>0.029</td><td>0.888</td><td>0.853</td><td>0.784</td><td>0.043</td><td>0.896</td></tr><tr><td>BgNet-V2 [62]</td><td>KBS'22</td><td>0.804</td><td>0.719</td><td>0.075</td><td>0.859</td><td>0.885</td><td>0.815</td><td>0.032</td><td>0.942</td><td>0.804</td><td>0.663</td><td>0.039</td><td>0.881</td><td>0.843</td><td>0.764</td><td>0.048</td><td>0.901</td></tr><tr><td>FAP-Net [63]</td><td>TIP'22</td><td>0.815</td><td>0.776</td><td>0.076</td><td>0.865</td><td>0.893</td><td>0.842</td><td>0.028</td><td>0.940</td><td>0.822</td><td>0.731</td><td>0.036</td><td>0.888</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>FindNet [64]</td><td>TIP'22</td><td>0.800</td><td>0.725</td><td>0.077</td><td>0.862</td><td>0.895</td><td>0.839</td><td>0.027</td><td>0.944</td><td>0.811</td><td>0.688</td><td>0.036</td><td>0.883</td><td>0.841</td><td>0.769</td><td>0.048</td><td>0.895</td></tr><tr><td>BSA-Net [65]</td><td>AAAI'22</td><td>0.796</td><td>0.717</td><td>0.079</td><td>0.851</td><td>0.895</td><td>0.841</td><td>0.027</td><td>0.946</td><td>0.818</td><td>0.699</td><td>0.034</td><td>0.891</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>BGNet [66]</td><td>IJCAI'22</td><td>0.812</td><td>0.749</td><td>0.073</td><td>0.870</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.831</td><td>0.722</td><td>0.033</td><td>0.901</td><td>0.851</td><td>0.788</td><td>0.044</td><td>0.907</td></tr><tr><td>FCFANet [67]</td><td>ICME'23</td><td>0.815</td><td>0.761</td><td>0.073</td><td>0.876</td><td>0.898</td><td>0.846</td><td>0.025</td><td>0.944</td><td>0.834</td><td>0.730</td><td>0.031</td><td>0.905</td><td>0.848</td><td>0.791</td><td>0.046</td><td>0.906</td></tr><tr><td>PENet [68]</td><td>IJCAI'23</td><td>0.828</td><td>0.771</td><td>0.063</td><td>0.890</td><td>0.902</td><td>0.851</td><td>0.024</td><td>0.960</td><td>0.831</td><td>0.723</td><td>0.031</td><td>0.908</td><td>0.855</td><td>0.795</td><td>0.042</td><td>0.912</td></tr><tr><td>SINet-V2* [69]</td><td>TIP'23</td><td>0.796</td><td>0.712</td><td>0.079</td><td>0.870</td><td>0.878</td><td>0.809</td><td>0.034</td><td>0.925</td><td>0.791</td><td>0.649</td><td>0.042</td><td>0.844</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>PUENet [70]</td><td>TIP'23</td><td>0.794</td><td>0.762</td><td>0.080</td><td>0.857</td><td>0.888</td><td>0.844</td><td>0.030</td><td>0.945</td><td>0.813</td><td>0.727</td><td>0.035</td><td>0.887</td><td>0.836</td><td>0.798</td><td>0.050</td><td>0.892</td></tr><tr><td>R-MGL-V2 [71]</td><td>TIP'23</td><td>0.774</td><td>0.684</td><td>0.085</td><td>0.848</td><td>0.892</td><td>0.825</td><td>0.029</td><td>0.935</td><td>0.813</td><td>0.682</td><td>0.034</td><td>0.882</td><td>0.831</td><td>0.751</td><td>0.051</td><td>0.892</td></tr><tr><td>FEDER [72]</td><td>CVPR'23</td><td>0.807</td><td>0.785</td><td>0.069</td><td>0.873</td><td>0.894</td><td>0.855</td><td>0.028</td><td>0.947</td><td>0.823</td><td>0.740</td><td>0.032</td><td>0.900</td><td>0.823</td><td><strong>0.817</strong></td><td>0.045</td><td>0.905</td></tr><tr><td>DINet [73]</td><td>TMM'24</td><td>0.790</td><td>0.747</td><td>0.082</td><td>0.851</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.804</td><td>0.696</td><td>0.039</td><td>0.879</td><td>0.837</td><td>0.787</td><td>0.051</td><td>0.891</td></tr><tr><td>ICEG [74]</td><td>ICLR'24</td><td>0.807</td><td>0.789</td><td>0.068</td><td>0.879</td><td>0.899</td><td>0.858</td><td>0.027</td><td>0.950</td><td>0.826</td><td>0.747</td><td>0.030</td><td>0.906</td><td>0.849</td><td>0.814</td><td>0.044</td><td>0.908</td></tr><tr><td>DPSNet-R (Ours)</td><td>2024</td><td>0.812</td><td>0.748</td><td>0.068</td><td>0.878</td><td>0.886</td><td>0.827</td><td>0.028</td><td>0.946</td><td>0.818</td><td>0.698</td><td>0.034</td><td>0.897</td><td>0.844</td><td>0.777</td><td>0.045</td><td>0.907</td></tr><tr><td>DPSNet-R2 (Ours)</td><td>2024</td><td><strong>0.828</strong></td><td>0.764</td><td><strong>0.064</strong></td><td><strong>0.893</strong></td><td><strong>0.905</strong></td><td><strong>0.867</strong></td><td><strong>0.023</strong></td><td><strong>0.961</strong></td><td>0.821</td><td>0.754</td><td><strong>0.030</strong></td><td><strong>0.908</strong></td><td><strong>0.859</strong></td><td>0.783</td><td><strong>0.042</strong></td><td><strong>0.909</strong></td></tr><tr><td colspan="20" style="text-align:center; font-weight:bold;">Transformer-Based Methods</td></tr><tr><td>FSPNet [75]</td><td>CVPR'23</td><td>0.856</td><td>0.830</td><td>0.050</td><td>0.899</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.851</td><td>0.769</td><td>0.026</td><td>0.895</td><td>0.879</td><td>0.843</td><td>0.035</td><td>0.915</td></tr><tr><td>EVP [76]</td><td>CVPR'23</td><td>0.846</td><td>0.777</td><td>0.059</td><td>0.895</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.843</td><td>0.742</td><td>0.029</td><td>0.029</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SARNet [77]</td><td>TCSVT'23</td><td>0.868</td><td>0.850</td><td>0.047</td><td>0.927</td><td>0.912</td><td>0.879</td><td>0.021</td><td>0.957</td><td>0.864</td><td>0.800</td><td>0.024</td><td>0.931</td><td>0.886</td><td>0.863</td><td>0.032</td><td>0.937</td></tr><tr><td>MSCAFNet [78]</td><td>TCSVT'23</td><td>0.872</td><td>0.828</td><td>0.046</td><td>0.927</td><td>0.912</td><td>0.865</td><td>0.022</td><td>0.958</td><td>0.865</td><td>0.775</td><td>0.024</td><td>0.927</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>FPNet [30]</td><td>ACMMM'23</td><td>0.852</td><td>0.806</td><td>0.056</td><td>0.905</td><td>0.914</td><td>0.856</td><td>0.022</td><td>0.961</td><td>0.850</td><td>0.748</td><td>0.029</td><td>0.850</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>XMSNet [79]</td><td>ACMMM'23</td><td>0.864</td><td>0.813</td><td>0.048</td><td>0.923</td><td>0.904</td><td>0.855</td><td>0.025</td><td>0.950</td><td>0.861</td><td>0.751</td><td>0.024</td><td>0.927</td><td>0.879</td><td>0.827</td><td>0.034</td><td>0.933</td></tr><tr><td>JCNet [80]</td><td>TIM'23</td><td>0.850</td><td>0.827</td><td>0.054</td><td>0.913</td><td>0.906</td><td>0.872</td><td>0.021</td><td>0.956</td><td>0.852</td><td>0.788</td><td>0.026</td><td>0.927</td><td>0.876</td><td>0.850</td><td>0.035</td><td>0.931</td></tr><tr><td>VSCode [81]</td><td>CVPR'24</td><td>0.838</td><td>0.821</td><td>-</td><td>0.909</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.847</td><td>0.795</td><td>-</td><td>0.925</td><td>0.874</td><td>0.853</td><td>-</td><td>0.930</td></tr><tr><td>GenSAM [82]</td><td>AAAI'24</td><td>0.719</td><td>0.659</td><td>0.113</td><td>0.775</td><td>0.764</td><td>0.680</td><td>0.090</td><td>0.807</td><td>0.775</td><td>0.681</td><td>0.067</td><td>0.838</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>DPSNet-P (Ours)</td><td>2024</td><td><strong>0.888</strong></td><td><strong>0.866</strong></td><td><strong>0.044</strong></td><td><strong>0.932</strong></td><td><strong>0.917</strong></td><td><strong>0.882</strong></td><td><strong>0.020</strong></td><td><strong>0.965</strong></td><td><strong>0.875</strong></td><td><strong>0.806</strong></td><td><strong>0.022</strong></td><td><strong>0.938</strong></td><td><strong>0.891</strong></td><td><strong>0.866</strong></td><td><strong>0.030</strong></td><td><strong>0.941</strong></td></tr></tbody></table>

prediction $L_{pre} = L_{BCE}^w + L_{IOU}^w + \lambda L_{DP}$ from the preliminary prediction stage and the fine-grained prediction $L_{fine} = L_{BCE}^w + L_{IOU}^w + \lambda L_{DP}$ from the fine-grained prediction stage. Through ablation experiments, we determine the optimal value of $\lambda$ to be 0.65. Therefore, the final overall loss function is defined as:

$$L_{total} = L_{pre} + L_{fine} \quad (10)$$

## IV. EXPERIMENTS

### A. *Experimental Setup*

**Implementation Details.** The proposed DPSNet is implemented with PyTorch. We use ResNet-50 [45] pre-trained on ImageNet as the basic model, and the remaining parts are randomly initialized. The input image and ground truth are all resized to 352 × 352 for both training and testing. During training, we use Adam to optimize the network with a learning rate 1e-4, dividing by 10 every 40 epochs and multiple augmentation strategies (i.e., horizontal flipping, border clipping, and random rotating) are applied to avoid overfitting. The

whole training takes about 5.5 hours with the batch size of 36 for training on a PC with an Intel(R) Xeon(R) Gold 6,240, 2.60 GHz CPU and an NVIDIA Tesla V100 GPU.

**Datasets.** We perform experiments on four public COD benchmark datasets. CHAMELEON [22] contains 76 images collected through the Google search engine with the keyword "camouflaged animals". CAMO [23] covers 8 categories and has 1,250 camouflaged images, which is divided into 1K for training and 0.25K for testing. COD10K [24] is currently the most challenging COD dataset, which consists of 5,066 camouflaged images (3,040 for training, 2,026 for testing), covering 5 super-classes and 69 sub-classes. Following the training settings in [19], we use CAMO and COD10K as the training set (4,040 images), and evaluate on the whole CHAMELEON dataset and the testing sets of CAMO and COD10K. NC4K [25] is another large-scale COD testing dataset including 4,121 images from the Internet. It contains much more samples than previous evaluation benchmarks, it can well reveal the generalization ability of COD models.

**Evaluation Metrics.** To provide a comprehensive evaluation of our method's performance in camouflaged object detection
<figure><img src="image_8.png" /><figcaption>Fig. 6. Qualitative comparisons of our proposed DPSNet and five cutting-edge models. From left to right are the original image, Ground Truth, our DPSNet, FSPNet, SINet-V2, C²F-Net, PFNet, and SINet.</figcaption></figure>

tion (COD), we employ four widely recognized metrics, each focusing on a different aspect of detection quality. These metrics offer a holistic assessment, ensuring that both global and local details of camouflaged objects are accurately captured. Structure-measure ($S_α$) [83] focuses on assessing the structural similarity between the predicted results and the ground truth. This metric emphasizes the preservation of essential geometrical and shape features, ensuring that the model not only detects objects but also captures their underlying structure with high fidelity. E-measure ($E_φ$) [84] is designed to reflect human visual perception, providing insights into how well the model performs from both global and local perspectives. This metric ensures that the detected objects are visually consistent with what humans would perceive, making it particularly valuable for tasks involving subtle object distinctions. Weighted F-measure ($F_β^ω$) [85] combines weighted precision and recall to provide a balanced evaluation of a model's accuracy. By giving importance to both correct object detection and avoiding false

positives, this metric offers a reliable synthesis of the model's overall performance, particularly in scenarios where precision and recall may vary. Mean Absolute Error (M) [86] measures the average pixel-level deviation between the predicted map and the ground truth. It is widely adopted to quantify the pixel-wise prediction accuracy, ensuring that the predicted results align closely with the actual object boundaries across the image. These four metrics together provide a robust framework for evaluating our method's capability.

## B. Comparison with the State-of-the-arts

**Quantitative Evaluation.** Table I provides a comprehensive summary of the quantitative results of DPSNet in comparison to 37 state-of-the-art (SOTA) methods across four benchmark datasets. The results clearly demonstrate that our proposed model consistently and significantly outperforms recent methods on all datasets, without the need for any
<table><caption>TABLE II
QUANTITATIVE COMPARISON FOR ABLATION STUDIES ON FOUR DATASET. "B" REPRESENTS RESNET-50 REMOVING ALL PROPOSED MODULES WHILE JUST USING THE PRE-TRAINED MODEL BY A SIMPLE CONCATENATION. "W/O *Baseline*" AND "W/O *Enhanced*" MEANS WITHOUT BASELINE PREDICTION SUB-NETWORK AND ENHANCED PREDICTION SUB-NETWORK, RESPECTIVELY. VER. = VERSION.</caption><thead><tr><th rowspan="2">Ver.</th><th rowspan="2">Method</th><th colspan="4">CAMO-Test</th><th colspan="4">CHAMELEON</th><th colspan="4">COD10K-Test</th><th colspan="4">NC4K</th></tr><tr><th>S<sub>α</sub>↑</th><th>F<sup>ω</sup><sub>β</sub>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th><th>S<sub>α</sub>↑</th><th>F<sup>ω</sup><sub>β</sub>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th><th>S<sub>α</sub>↑</th><th>F<sup>ω</sup><sub>β</sub>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th><th>S<sub>α</sub>↑</th><th>F<sup>ω</sup><sub>β</sub>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th></tr></thead><tbody><tr><td>(a)</td><td>B</td><td>0.790</td><td>0.687</td><td>0.083</td><td>0.848</td><td>0.862</td><td>0.757</td><td>0.040</td><td>0.914</td><td>0.780</td><td>0.604</td><td>0.045</td><td>0.863</td><td>0.818</td><td>0.706</td><td>0.057</td><td>0.883</td></tr><tr><td>(b)</td><td>B+MAAM</td><td>0.803</td><td>0.733</td><td>0.072</td><td>0.865</td><td>0.882</td><td>0.815</td><td>0.031</td><td>0.928</td><td>0.810</td><td>0.691</td><td>0.034</td><td>0.898</td><td>0.834</td><td>0.747</td><td>0.045</td><td>0.889</td></tr><tr><td>(c)</td><td>B+SAEM</td><td>0.806</td><td>0.732</td><td>0.072</td><td>0.870</td><td>0.883</td><td>0.820</td><td>0.030</td><td>0.930</td><td>0.811</td><td>0.695</td><td>0.034</td><td>0.898</td><td>0.835</td><td>0.752</td><td>0.045</td><td>0.900</td></tr><tr><td>(d)</td><td>B+MAAM w/o Baseline</td><td>0.796</td><td>0.717</td><td>0.066</td><td>0.863</td><td>0.880</td><td>0.812</td><td>0.032</td><td>0.925</td><td>0.807</td><td>0.676</td><td>0.036</td><td>0.892</td><td>0.832</td><td>0.739</td><td>0.049</td><td>0.887</td></tr><tr><td>(e)</td><td>B+MAAM+SAEM w/o Enhanced</td><td>0.797</td><td>0.722</td><td>0.076</td><td>0.863</td><td>0.883</td><td>0.821</td><td>0.031</td><td>0.927</td><td>0.809</td><td>0.679</td><td>0.035</td><td>0.894</td><td>0.836</td><td>0.752</td><td>0.048</td><td>0.901</td></tr><tr><td>(f)</td><td>B+MAAM+SAEM+L<sup>w</sup><sub>BCE</sub></td><td>0.809</td><td>0.708</td><td>0.076</td><td>0.844</td><td>0.883</td><td>0.787</td><td>0.035</td><td>0.915</td><td>0.813</td><td>0.644</td><td>0.038</td><td>0.852</td><td>0.840</td><td>0.755</td><td>0.050</td><td>0.879</td></tr><tr><td>(g)</td><td>B+MAAM+SAEM+L<sup>w</sup><sub>BCE</sub> + L<sup>w</sup><sub>IoU</sub></td><td>0.810</td><td>0.734</td><td>0.073</td><td>0.871</td><td>0.884</td><td>0.822</td><td>0.032</td><td>0.931</td><td>0.815</td><td>0.694</td><td>0.034</td><td>0.894</td><td>0.842</td><td>0.760</td><td>0.046</td><td>0.903</td></tr><tr><td>(h)</td><td>B+MAAM+SAEM+L<sup>w</sup><sub>BCE</sub> + L<sup>w</sup><sub>IoU</sub> + L<sub>DP</sub></td><td><b>0.812</b></td><td><b>0.748</b></td><td><b>0.068</b></td><td><b>0.878</b></td><td><b>0.886</b></td><td><b>0.827</b></td><td><b>0.028</b></td><td><b>0.946</b></td><td><b>0.818</b></td><td><b>0.698</b></td><td><b>0.034</b></td><td><b>0.897</b></td><td><b>0.844</b></td><td><b>0.777</b></td><td><b>0.045</b></td><td><b>0.907</b></td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>Fig. 7. Visualizations of feature maps produced by the SAEM and MAAM modules. The images illustrate how these modules enhance feature representation and attention mechanisms, contributing to improved model performance.</figcaption></figure>

post-processing techniques. Notably, our method surpasses all previous methods by a considerable margin across the four evaluation metrics. For instance, compared to CNN-based methods, our method outperforms the cutting-edge C²F-Net-V2 [59] by 8.4%, 4.5%, and 10.4% in terms of $F^ω_β$ on the CAMO, CHAMELEON, and COD10K datasets, respectively. Additionally, when compared to the latest COD method, DINet [73], our method achieves superior performance with improvements of 3.8%, 1.7%, and 2.2% in terms of $S_α$ on the CAMO, COD10K, and NC4K datasets, respectively. Furthermore, we also evaluated the performance of our method based on the transformer architecture [52]. When compared with the SOTA COD method VSCode [81], our method delivers significant improvements in $F^ω_β$, showing gains of 5.3%, 2.7%, and 5.5% on the CAMO, CHAMELEON, and COD10K datasets, respectively. These results further validate the robustness and effectiveness of our method across various challenging scenarios.

**Qualitative Evaluation.** Figure 6 presents a qualitative comparison of our method with other state-of-the-art methods. The visual representations clearly showcase the superior performance of our proposed DPSNet across various scenes. Several compared methods exhibit blurry object boundaries (e.g., 5th to 8th columns). When confronted with objects that closely resemble their surroundings in the foreground and

background (e.g., 1st and 2nd rows), most existing methods fail to detect the complete objects. In contrast, our DPSNet accurately segments the camouflaged objects in these challenging scenarios. In scenes with large objects, DPSNet performs exceptionally well, yielding nearly perfect visualization results compared to other cutting-edge methods (e.g., 3rd and 5th rows). Many existing methods tend to detect uncorrelated background regions when dealing with objects of varying scales and shapes (e.g., 4th and 6th rows). Conversely, our method consistently produces more accurate and complete visual maps with clear boundaries and coherent details. Even for smaller camouflaged objects, DPSNet demonstrates the capability to segment relatively complete edges and contours with remarkable accuracy (e.g., 7th row). The visual comparisons clearly highlight the advanced capabilities of our method, demonstrating its superiority in various challenging scenes. Figure 6 additionally presents visualization results on the PCOD-1200 dataset. From the 8th and 9th rows, we can observe that our method shows better generalization, producing smoother segmentation results compared to the ground-truth. Even in cluttered and occluded environments, as seen in the 8th row, our DPSNet effectively segments the camouflaged objects. Overall, the qualitative results show that our DPSNet can generate clearer, more complete, and accurate visual maps, accentuating the boundaries and intricacies of the
<table><caption>TABLE III
COMPARISON OF DIFFERENT FEATURE AGGREGATION METHODS ON VARIOUS DATASETS. THE RESULTS DEMONSTRATE THE SUPERIOR PERFORMANCE OF OUR SAEM MODULE ACROSS MULTIPLE METRICS.</caption><thead><tr><th rowspan="2">Ver.</th><th rowspan="2">Methods</th><th colspan="3">CAMO-Test</th><th colspan="3">CHAMELEON</th><th colspan="3">COD10K-Test</th><th colspan="3">NC4K</th></tr><tr><th>S<sub>α</sub>↑</th><th>F<sub>β</sub><sup>ω</sup>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th><th>S<sub>α</sub>↑</th><th>F<sub>β</sub><sup>ω</sup>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th><th>S<sub>α</sub>↑</th><th>F<sub>β</sub><sup>ω</sup>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th></tr></thead><tbody><tr><td>(1)</td><td>CAM [87]</td><td>0.800</td><td>0.727</td><td>0.074</td><td>0.863</td><td>0.884</td><td>0.825</td><td>0.031</td><td>0.945</td><td>0.814</td><td>0.689</td><td><b>0.034</b></td><td>0.896</td><td>0.843</td><td>0.772</td><td>0.046</td><td>0.906</td></tr><tr><td>(2)</td><td>ERF [78]</td><td>0.798</td><td>0.729</td><td>0.075</td><td>0.865</td><td>0.878</td><td>0.818</td><td>0.032</td><td>0.946</td><td>0.809</td><td>0.685</td><td>0.035</td><td>0.895</td><td>0.839</td><td>0.770</td><td>0.047</td><td>0.906</td></tr><tr><td>(3)</td><td>RMFE [65]</td><td>0.802</td><td>0.734</td><td>0.070</td><td>0.868</td><td>0.878</td><td>0.817</td><td>0.031</td><td><b>0.947</b></td><td>0.811</td><td>0.688</td><td>0.035</td><td>0.898</td><td>0.839</td><td>0.769</td><td>0.047</td><td>0.905</td></tr><tr><td>(4)</td><td>MFAM [63]</td><td>0.800</td><td>0.727</td><td>0.076</td><td>0.866</td><td>0.876</td><td>0.816</td><td>0.032</td><td>0.945</td><td>0.812</td><td>0.691</td><td><b>0.034</b></td><td><b>0.900</b></td><td>0.840</td><td>0.771</td><td>0.046</td><td>0.906</td></tr><tr><td>(5)</td><td>DGCM [20]</td><td>0.804</td><td>0.734</td><td>0.072</td><td>0.868</td><td>0.879</td><td>0.812</td><td>0.032</td><td>0.941</td><td>0.810</td><td>0.685</td><td>0.035</td><td>0.897</td><td>0.839</td><td>0.767</td><td>0.047</td><td>0.905</td></tr><tr><td>(6)</td><td>RFB [88]</td><td>0.796</td><td>0.726</td><td>0.074</td><td>0.864</td><td>0.876</td><td>0.814</td><td>0.032</td><td>0.942</td><td>0.811</td><td>0.686</td><td>0.035</td><td>0.894</td><td>0.840</td><td>0.770</td><td>0.047</td><td>0.906</td></tr><tr><td>(7)</td><td>ASPP [89]</td><td>0.796</td><td>0.727</td><td>0.074</td><td>0.867</td><td>0.871</td><td>0.809</td><td>0.035</td><td>0.896</td><td>0.810</td><td>0.685</td><td>0.035</td><td>0.896</td><td>0.840</td><td>0.771</td><td>0.046</td><td>0.908</td></tr><tr><td>(8)</td><td>SAEM (Ours)</td><td><b>0.812</b></td><td><b>0.748</b></td><td><b>0.068</b></td><td><b>0.878</b></td><td><b>0.886</b></td><td><b>0.827</b></td><td><b>0.028</b></td><td>0.946</td><td><b>0.818</b></td><td><b>0.698</b></td><td><b>0.034</b></td><td>0.897</td><td><b>0.844</b></td><td><b>0.777</b></td><td><b>0.045</b></td><td><b>0.907</b></td></tr></tbody></table>

<table><caption>TABLE IV
COMPARISON OF DIFFERENT BOUNDARY-AWARE LOSS FUNCTIONS ON VARIOUS DATASETS. THE RESULTS DEMONSTRATE THE SUPERIOR PERFORMANCE OF OUR PROPOSED DETAIL PERCEPTION LOSS (DP LOSS) ACROSS MULTIPLE METRICS.</caption><thead><tr><th rowspan="2">Ver.</th><th rowspan="2">Methods</th><th colspan="3">CAMO-Test</th><th colspan="3">CHAMELEON</th><th colspan="3">COD10K-Test</th><th colspan="3">NC4K</th></tr><tr><th>S<sub>α</sub>↑</th><th>F<sub>β</sub><sup>ω</sup>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th><th>S<sub>α</sub>↑</th><th>F<sub>β</sub><sup>ω</sup>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th><th>S<sub>α</sub>↑</th><th>F<sub>β</sub><sup>ω</sup>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th></tr></thead><tbody><tr><td>(1)</td><td>LogitBCE [63]</td><td>0.803</td><td>0.702</td><td>0.083</td><td>0.830</td><td>0.884</td><td>0.794</td><td>0.040</td><td>0.909</td><td>0.814</td><td>0.668</td><td>0.039</td><td>0.870</td><td>0.840</td><td>0.741</td><td>0.057</td><td>0.868</td></tr><tr><td>(2)</td><td>Dice Loss [90]</td><td>0.803</td><td>0.723</td><td>0.075</td><td>0.859</td><td>0.885</td><td>0.815</td><td>0.032</td><td>0.939</td><td>0.815</td><td>0.682</td><td>0.036</td><td>0.893</td><td><b>0.844</b></td><td>0.767</td><td>0.047</td><td>0.902</td></tr><tr><td>(3)</td><td>UAL Loss [61]</td><td>0.804</td><td>0.728</td><td>0.074</td><td>0.869</td><td><b>0.887</b></td><td><b>0.828</b></td><td>0.031</td><td>0.943</td><td>0.812</td><td>0.686</td><td>0.035</td><td>0.896</td><td>0.843</td><td>0.771</td><td>0.049</td><td>0.905</td></tr><tr><td>(4)</td><td>CEL Loss [34]</td><td>0.804</td><td>0.730</td><td>0.072</td><td>0.867</td><td>0.883</td><td>0.822</td><td>0.032</td><td>0.944</td><td>0.813</td><td>0.689</td><td>0.035</td><td><b>0.897</b></td><td>0.842</td><td>0.770</td><td>0.046</td><td>0.905</td></tr><tr><td>(5)</td><td>DP Loss (Ours)</td><td><b>0.812</b></td><td><b>0.748</b></td><td><b>0.068</b></td><td><b>0.878</b></td><td>0.886</td><td>0.827</td><td><b>0.028</b></td><td><b>0.946</b></td><td><b>0.818</b></td><td><b>0.698</b></td><td><b>0.034</b></td><td><b>0.897</b></td><td><b>0.844</b></td><td><b>0.777</b></td><td><b>0.045</b></td><td><b>0.907</b></td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>Fig. 8. Visual comparisons of different ablation experiments. Letters (a) to (h) refer to Table II.</figcaption></figure>

camouflaged objects.

### C. Ablation Study

**Effectiveness of Hierarchical-synergistic Architecture.**
To validate the effectiveness of our hierarchical-synergistic architecture, we conduct ablation experiments. In Table II, we compare the complete architecture (f) with versions that remove either the baseline prediction sub-network (d) or the enhanced prediction sub-network (e). Adding the enhanced prediction sub-network to the baseline sub-network significantly improves performance. This demonstrates the effectiveness of the enhanced sub-network for accurate camouflaged object detection. In the preliminary prediction stage, the network mainly uses low-level features to locate objects without focusing too much on context. This results in the preliminary prediction. However, feeding these preliminary predictions to the fine-grained prediction stage greatly enhances the performance.

**Effectiveness of MAAM and SAEM.** We also investigate the importance of MAAM and SAEM. As shown in Table II, adding MAAM and SAEM to the basic model (a) improves performance. Notably, adding MAAM to the basic model leads to a significant performance boost. For example, on

the CHAMELEON dataset, MAAM provides 2.0%, 5.8%, and 1.4% improvements in $S_α$, $F_β^ω$, and $E_φ$, respectively. To further explore the effectiveness of SAEM, we compare models with and without it. As shown in Table II, the model with SAEM (c) outperforms the basic model (a) by 4.5%, 7.3%, 9.1%, and 4.6% in terms of $F_β^ω$ on the CAMO, CHAMELEON, COD10K, and NC4K datasets, respectively. To fully verify the effectiveness of SAEM, we conducted a direct comparison with other prominent aggregation methods, as shown in Table III. These methods include the Context Aggregation Module (CAM) from [87], the Edge Refinement Feature (ERF) from MSCAF [78], the Residual Multi-Feature Extraction (RMFE) from BSANet [65], the Multi-Feature Attention Module (MFAM) from FAPNet [63], the Dual-Group Convolution Module (DGCM) from $C^2$F-Net [20], as well as standard modules like RFB [88] and ASPP [89]. We excluded multi-scale aggregation methods or those without available source codes to ensure a consistent comparison. Our results demonstrate that SAEM consistently outperforms these methods across multiple datasets and metrics. For instance, on the CAMO-Test dataset, SAEM achieves the highest $S_α$ score of 0.812 and the lowest $M$ score of 0.068, indicating superior feature aggregation and enhancement capabilities. As illustrated in Figure 7, we also visualize the feature maps generated by the MAAM and SAEM modules to verify their effectiveness. The first to fourth rows represent the input image, ground truth, feature maps from the MAAM module, and those from the SAEM module, respectively. By comparing the second and third rows, we can observe that the Morphological Awareness Adaptation Module (MAAM) accurately perceives the camouflaged objects' morphological features, such as scale, shape, and orientation, even for objects with complex shapes. In contrast, the Semantic Aggregation En-
hancement Module (SAEM) further aggregates and enhances the morphological features captured by MAAM, ensuring the detection of complete camouflaged objects.

**Effectiveness of Detail Perception Loss.** To validate the effectiveness of our proposed Detail Perception Loss ($L_{DP}$), we conduct ablation studies comparing different loss functions based on our DPSNet. As shown in Table II, the model that combines the weighted binary cross-entropy loss ($L_{BCE}^w$), weighted IoU loss ($L_{IoU}^w$), and our $L_{DP}$ achieves the highest performance. We also compare our $L_{DP}$ with several well-known boundary-aware loss functions, as detailed in Table IV. This comparison includes the LogitBCE [63], Dice Loss [90], Uncertainty-Aware Loss (UAL) [61], and Consistency-Enhanced Loss (CEL) [34]. To ensure a fair and comprehensive evaluation, we excluded losses that are not publicly available. The results indicate that our $L_{DP}$ consistently outperforms these existing loss functions across multiple datasets. For instance, in the CAMO-Test dataset, our $L_{DP}$ achieves an impressive score of 0.812 in the first metric, surpassing the next best result from Dice Loss (0.803) and demonstrating a clear advantage in boundary detection capabilities. Additionally, we have added boundary-aware learning models that do not propose new losses to I, marked in red, to compare them directly with our boundary-aware learning method. Furthermore, our $L_{DP}$ shows significant improvements in detecting clear boundaries and accurately locating camouflaged objects, as illustrated in the visual maps in Figure 8. This indicates that our proposed loss function not only enhances overall performance but also improves the model's ability to delineate object boundaries effectively.

**Computational Complexity.** We compare the computational complexities of our DPSNet and nine other state-of-the-art models. All images are resized to 352 x 352, and the inference speed and FLOPs are evaluated on a PC with an Intel(R) Xeon(R) Gold 6,240, 2.60 GHz CPU and an NVIDIA Tesla V100 GPU to ensure a fair comparison. Table V presents the average inference speed, eliminating the impact of random factors. Despite our DPSNet based on ResNet-50 having a larger parameter count compared to some models, it still achieves rapid inference speeds. This is because our model is designed to be computationally efficient while maintaining high performance. The DPSNet version based on Res2Net-50 exhibits competitive inference speed, even with the incorporation of a novel multi-scale structure within each residual block. This enhancement improves model performance but also increases computational complexity and model depth, resulting in slightly reduced backbone FPS and higher backbone Params and FLOPs compared to the ResNet-50 version. Overall, our DPSNet demonstrates a good balance between computational efficiency and performance, making it suitable for real-world applications that require fast and accurate camouflaged object detection.

**Compared with Other SOTA Methods on PCOD_1200 Dataset.** To comprehensively evaluate the performance and robustness of our model and ensure a fair comparison, we conducted additional experiments on the latest PCOD'1200 dataset [87] using the same backbone network (PVTv2) as the state-of-the-art method IPNet [87]. As shown in Table VI, our

method demonstrates competitive results across all evaluation metrics, even without leveraging the degree of linear polarization (DoLP) and angle of polarization (AoLP) as enhanced image inputs for network training. Specifically, our model achieves an $F_\beta^\omega$ score of 0.891, which is the highest among all listed methods, surpassing the IPNet, which achieves 0.882 despite using DoLP for enhancement. This indicates our model's superior ability to accurately detect and segment camouflaged objects, particularly in challenging scenarios. Furthermore, our model also attains the best scores in both $M$ (0.008) and $E_\phi$ (0.976), underscoring its robustness in minimizing segmentation errors and enhancing edge preservation. Although the CMX [94] and IPNet models slightly outperform ours in the $S_\alpha$ metric (both scoring 0.922), the overall performance of our method is highly competitive, and it leads in most key indicators. These results highlight the effectiveness of our method, making it a strong contender among the state-of-the-art methods on the PCOD_1200 dataset.

**Failure Cases and Limitations.** Despite the outstanding performance of our method in most scenarios involving camouflaged objects, it exhibits limitations in certain highly complex and challenging situations. As illustrated in Figure 9 from top to bottom, our method encounters difficulties in scenarios involving multiple objects, occluded objects, and low-contrast objects. These challenges can result in missed camouflaged objects, incomplete occluded parts, and inaccurate edge detection. Although some existing state-of-the-art (SOTA) methods also face similar issues, our method is not exempt from these challenges. We acknowledge these limitations as valuable insights that motivate us to explore more efficient methods in future work. The results in Table I show that some evaluation metrics of our method, based on ResNet-50, slightly lag behind those of ZoomNet [61], BgNet [57], and FEDER [72]. This discrepancy can be attributed to the fact that ZoomNet employs a multi-scale auxiliary training strategy, while our method, along with other methods, uses single-scale training. Additionally, BgNet and FEDER use the $F_\beta$ evaluation metric, whereas our method, like most others, uses the more stringent $F_\beta^\omega$ metric. As is well known, $F_\beta$ values are typically higher than $F_\beta^\omega$ under the same conditions. This is because $F_\beta^\omega$ places greater emphasis on the model's performance in difficult-to-segment areas, such as boundaries and small objects, where errors are more likely to occur. Therefore, $F_\beta^\omega$ is stricter and tends to yield lower scores, reflecting a model's shortcomings in these critical areas. In contrast, $F_\beta$, which does not differentiate between regions of varying importance, is more likely to produce higher scores.

**Discussion.** Our proposed DPSNet differs from other general object detection, salient object detection, and medical image segmentation tasks. General object detection often only needs to detect the regions of ordinary objects with rectangular boxes [95]–[97], without accurately outlining the contours of the objects. Salient object detection [98], [99] aims to identify the most salient or attractive regions in the image, simulating the attention mechanism of the human visual system. This detection method has a wide range of applications in advertising, image foreground extraction, and visual attention analysis. Medical image segmentation [100] mainly faces the task of
<table><caption>TABLE V
AVERAGE TEST SPEED AND COMPLEXITY COMPARISON WITH OTHER CUTTING-EDGE MODELS. "-R": RESNET-50 [45]. "-R2": RES2NET-50 [51].</caption><thead><tr><th>Method</th><th>Ours-R</th><th>Ours-R2</th><th>BGNet [66]</th><th>SINet-V2 [19]</th><th>C<sup>2</sup>F-Net [20]</th><th>PFNet [21]</th><th>SINet [24]</th><th>UGTR [55]</th><th>UJSC [54]</th><th>R-MGL [53]</th><th>SLSR [25]</th></tr></thead><tbody><tr><td>FPS</td><td>66.606</td><td>44.739</td><td>36.231</td><td>25.432</td><td>47.962</td><td>52.058</td><td>43.278</td><td>18.266</td><td>25.406</td><td>15.476</td><td>45.313</td></tr><tr><td>Params(M)</td><td>51.573</td><td>51.868</td><td>60.473</td><td>26.976</td><td>28.411</td><td>46.498</td><td>48.947</td><td>48.868</td><td>217.982</td><td>63.595</td><td>50.935</td></tr><tr><td>FLOPs(G)</td><td>38.368</td><td>40.981</td><td>27.695</td><td>24.481</td><td>26.167</td><td>53.222</td><td>38.757</td><td>1031.168</td><td>112.341</td><td>553.939</td><td>66.625</td></tr></tbody></table>

<figure><img src="image_11.png" /><figcaption>Fig. 9. Illustration of three typical failure cases involving our DPSNet and other state-of-the-art (SOTA) methods. From top to bottom they can be summarized as multiple objects, occluded objects and low contrast objects.</figcaption></figure>

<table><caption>TABLE VI
PERFORMANCE COMPARISON OF OUR METHOD AGAINST STATE-OF-THE-ART (SOTA) METHODS ON THE PCOD_1200 DATASET. OUR METHOD DEMONSTRATES SUPERIOR PERFORMANCE IN MOST METRICS WITHOUT UTILIZING POLARIZATION INFORMATION.</caption><thead><tr><th rowspan="2">Methods</th><th rowspan="2">Year</th><th colspan="4">PCOD_1200</th></tr><tr><th>S<sub>α</sub>↑</th><th>F<sub>β</sub>↑</th><th>M↓</th><th>E<sub>φ</sub>↑</th></tr></thead><tbody><tr><td>SINet [24]</td><td>CVPR'20</td><td>0.862</td><td>0.788</td><td>0.016</td><td>0.902</td></tr><tr><td>SLSR [25]</td><td>CVPR'21</td><td>0.888</td><td>0.845</td><td>0.011</td><td>0.938</td></tr><tr><td>PFNet [21]</td><td>CVPR'21</td><td>0.873</td><td>0.817</td><td>0.014</td><td>0.935</td></tr><tr><td>C<sup>2</sup>F-Net [20]</td><td>IJCAI'21</td><td>0.893</td><td>0.838</td><td>0.012</td><td>0.942</td></tr><tr><td>SINet-V2 [19]</td><td>TPAMI'21</td><td>0.882</td><td>0.819</td><td>0.013</td><td>0.941</td></tr><tr><td>OCENet [91]</td><td>WACV'22</td><td>0.883</td><td>0.827</td><td>0.013</td><td>0.945</td></tr><tr><td>ZoomNet [61]</td><td>CVPR'22</td><td>0.897</td><td>0.842</td><td>0.010</td><td>0.922</td></tr><tr><td>BSANet [65]</td><td>AAAI'22</td><td>0.903</td><td>0.861</td><td>0.011</td><td>0.945</td></tr><tr><td>ERRNet [92]</td><td>PR'22</td><td>0.833</td><td>0.704</td><td>0.023</td><td>0.901</td></tr><tr><td>C<sup>2</sup>F-Net-V2 [59]</td><td>TIP'23</td><td>0.895</td><td>0.845</td><td>0.012</td><td>0.945</td></tr><tr><td>PGSNet [93]</td><td>CVPR'22</td><td>0.916</td><td>0.868</td><td>0.010</td><td>0.965</td></tr><tr><td>CMX [94]</td><td>TITS'23</td><td><strong>0.922</strong></td><td>0.876</td><td>0.009</td><td>0.965</td></tr><tr><td>IPNet [87]</td><td>AAAI'24</td><td><strong>0.922</strong></td><td>0.882</td><td><strong>0.008</strong></td><td>0.970</td></tr><tr><td>DPSNet (Ours)</td><td>2024</td><td>0.918</td><td><strong>0.891</strong></td><td><strong>0.008</strong></td><td><strong>0.976</strong></td></tr></tbody></table>

medical image segmentation. Compared with camouflaged object detection, its detection scope is more limited. Due to the similarity between medical lesions such as tumors and polyps and their background, the MAAM and SAEM modules as well as the detail perception loss function proposed in our DPSNet can also be applied to medical image segmentation tasks, such as polyp segmentation and COVID-19 infection identification. Our camouflaged object detection method can also be applied as initial steps for many visual tasks, such as generating initial target objects for image retrieval and visual tracking. Furthermore, our method can be applied to the detection of artistic images with similar backgrounds and foregrounds, as well as military applications for detecting

camouflaged soldiers and weapons. In summary, our method has broad application value and research significance. It can be extended to other computer vision tasks beyond camouflaged object detection, such as general object detection, salient object detection, and medical image segmentation. Exploring the connections between our DPSNet and these related tasks in other domains can help broaden the impact and applicability of our work.

## V. CONCLUSION

We have proposed a novel framework for camouflaged object detection (COD), which can quickly and accurately locate and segment the objects that are visually blended with the background. Our framework, named Detail Perception Synergistic Network (DPSNet), generates preliminary and fine-grained predictions, and uses a detail perception loss to balance the global and local information. We have also introduced two novel modules, namely, Morphological Awareness Adaptation Module (MAAM) and Semantic Aggregation Enhancement Module (SAEM), which can effectively fuse multi-scale features and enhance discriminative feature representations. Our DPSNet achieves state-of-the-art performance on four benchmark datasets, under four evaluation metrics, with real-time speed. Our work provides a new perspective and a strong baseline for the challenging COD task. For future work, we plan to design more effective modules and loss functions to extend our COD framework to more challenging scenarios, such as camouflaged instance segmentation, dynamic camouflage, and medical image analysis.

## ACKNOWLEDGMENTS

The authors would like to thank all the anonymous reviewers for their invaluable suggestions to improve this manuscript.
This work is in part supported by the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute).

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Sheng Long received the B.E. degree from the College of Computer Science, Sichuan University, Chengdu, China. He is currently working toward the Ph.D. degree in the College of Systems Engineering, National University of Defense Technology, Changsha, China. His main research interests include machine learning, optimization, and adversarial robustness.

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sion.

Jiaxin Yang received the B.S. degree in Management from Xiamen University, Xiamen, China, in 2018, and the M.S. degree in Control Science and Engineering from the National University of Defense Technology, Changsha, China, in 2024. He is currently working toward the Ph.D. degree in Control Science and Engineering with the Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China. His current research interests include long-tailed visual recognition, long-tailed object detection, and multi-modal feature fusing.

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Jun Lei received the B.S. degree in Information System Engineering, and the M.S. and Ph.D. degrees in Control Science and Engineering from the National University of Defense Technology, Changsha, China, in 2010, 2012, and 2017, respectively. He is currently a Lecturer at the National University of Defense Technology. His current research interests include machine learning, computer vision, and deep learning.

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Shuohao Li received the B.S. degree in Information System Engineering, and the M.S. and Ph.D. degrees in Control Science and Engineering from the National University of Defense Technology, Changsha, China, in 2012, 2014, and 2018, respectively. He is currently a research assistant at the National University of Defense Technology. His current research interests include scene graph generation, DeepFake recognition, and deep learning.

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Jun Zhang received her B.S. degree in Applied Mathematics from the Information Engineering University, Zhengzhou, China, in 1996, and her M.S. and Ph.D. degrees in Control Science and Engineering from the National University of Defense Technology, Changsha, China, in 2002 and 2008, respectively. She is currently a professor at the College of System Engineering, National University of Defense Technology, Changsha, China. She has led or participated in over 40 national projects, including major national initiatives and projects funded by the National Natural Science Foundation of China, and holds 9 patents. She has published 4 books and more than 80 papers in top-tier journals and conferences such as *TPAMI*, *IJCAI*, and *PR*. Her research interests include digital intelligence system engineering, information system engineering, artificial intelligence, deep learning, probabilistic graphical models, and visual data computation and analysis. She is a member of the Chinese Command and Control Society. She serves as a reviewer for journals such as *Acta Automatica Sinica*, *Journal of Electronics & Information Technology*, *IEEE Transactions on Cybernetics*, *IEEE Transactions on Artificial Intelligence*, and for conferences such as *ACM MM*.

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Xiaofei Li is currently pursuing the Ph.D. degree in Control Science and Engineering at the Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China. His research interests include computer vision, adversarial attacks, and causal inference, with a particular focus on camouflaged object detection, salient object detection, and fine-grained image recognition. He has published over 10 papers in various conferences and journals, including *IJCAI*, *ICME*, *ICONIP*, *ICPR*, *International Conference on Computer and Communications*, *Image Processing and Media Computing*, *Neurocomputing*, and *Mathematics*. He also serves as a reviewer for several prestigious journals and conferences, such as *IEEE TIP*, *IEEE TCSVT*, *SCIENCE CHINA Information Sciences*, *NeurIPS*, *ICLR*, and *ACM MM*. He has been nominated for the Best Student Paper Award at *ICONIP*.
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**Laurent D. Cohen** (Fellow, IEEE) was a student at the École Normale Supérieure, rue d'Ulm in Paris, France, from 1981 to 1985. He received the Master's and Ph.D. degrees in applied mathematics from the University of Paris 6, France, in 1983 and 1986, respectively. He received the Habilitation à Diriger des Recherches from the University of Paris 9 Dauphine in 1995. From 1985 to 1987, he was a member of the computer graphics and image processing group at Schlumberger Palo Alto Research, Palo Alto, California, and Schlumberger Montrouge Research, Montrouge, France, and remained a consultant with Schlumberger afterward. He began working with INRIA, France, in 1988, mainly with the medical image understanding group EPIDAURE. He obtained a position of Research Scholar (Chargé then Directeur de Recherche 1st class) with the French National Center for Scientific Research (CNRS) in the Applied Mathematics and Image Processing group at CEREMADE,

Université Paris Dauphine, Paris, France, in 1990. His research interests and teaching at the university include applications of partial differential equations and variational methods to image processing and computer vision, such as deformable models, minimal paths, geodesic curves, surface reconstruction, image segmentation, registration, and restoration. He has been an editorial member of the *Journal of Mathematical Imaging and Vision, Medical Image Analysis*, and *Machine Vision and Applications*. He was also a member of the program committee for about 60 international conferences. He has authored about 300 publications in international journals, conferences, or book chapters, and holds 6 patents. In 2002, he received the CS 2002 prize for Signal and Image Processing. In 2006, he received the Taylor & Francis Prize: "2006 prize for Outstanding Innovation in Computer Methods in Biomechanics and Biomedical Engineering." He was the 2009 laureate of the Grand Prix EADS de l'Académie des Sciences in France. He was promoted to IEEE Fellow in 2010 for contributions to computer vision technology for medical imaging. Since 2019, he is a PRAIRIE chair.