Saliency Object Detection Utilizing Adaptive Convolutional Attention and Mask Structure
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摘要: 显著目标检测(SOD)旨在模仿人类视觉系统注意力机制和认知机制来自动提取场景中的显著物体。虽然现有基于卷积神经网络 (CNN)或Transformer的模型不断刷新该领域方法的性能，但较少研究关注以下两个问题：(1)此领域多数方法常采用逐像素点的密集预测方式以获取像素显著值，然而该方式不符合基于人类视觉系统的场景解析机制，即人眼通常对语义区域进行整体分析而非关注像素级信息；(2)增强上下文信息关联在SOD任务中受到广泛关注，但通过Transformer主干结构获取长程关联特征不一定具有优势。SOD应更关注目标在适当区域内其中心-邻域差异性而非全局长程依赖。针对上述问题，该文提出一种新的显著目标检测模型，将CNN形式的自适应注意力和掩码注意力集成到网络中，以提高显著目标检测的性能。该算法设计了基于掩码感知的解码模块，通过将交叉注意力限制在预测的掩码区域来感知图像特征，有助于网络更好地聚焦于显著目标的整体区域。同时，该文设计了基于卷积注意力的上下文特征增强模块，与Transformer逐层建立长程关系不同，该模块仅捕获最高层特征中的适当上下文关联，避免引入无关的全局信息。该文在4个广泛使用的数据集上进行了实验评估，结果表明，该文提出的方法在不同场景下均取得了显著的性能提升，具有良好的泛化能力和稳定性。
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关键词:
- 显著目标检测 /
- 卷积神经网络形式的自适应注意力 /
- 掩码注意力 /
- 特征增强
Abstract:Objective Salient Object Detection (SOD) aims to replicate the human visual system’s attentional processes by identifying visually prominent objects within a scene. Recent advancements in Convolutional Neural Networks (CNNs) and Transformer-based models have improved performance; however, several limitations remain: (1) Most existing models depend on pixel-wise dense predictions, diverging from the human visual system’s focus on region-level analysis, which can result in inconsistent saliency distribution within semantic regions. (2) The common application of Transformers to capture global dependencies may not be ideal for SOD, as the task prioritizes center-surround contrasts in local areas rather than global long-range correlations. This study proposes an innovative SOD model that integrates CNN-style adaptive attention and mask-aware mechanisms to enhance contextual feature representation and overall performance. Methods The proposed model architecture comprises a feature extraction backbone, contextual enhancement modules, and a mask-aware decoding structure. A CNN backbone, specifically Res2Net, is employed for extracting multi-scale features from input images. These features are processed hierarchically to preserve both spatial detail and semantic richness. Additionally, this framework utilizes a top-down pathway with feature pyramids to enhance multi-scale representations. High-level features are further refined through specialized modules to improve saliency prediction. Central to this architecture is the ConvoluTional attention-based contextual Feature Enhancement (CTFE) module. By using adaptive convolutional attention, this module effectively captures meaningful contextual associations without relying on global dependencies, as seen in Transformer-based methods. The CTFE focuses on modeling center-surround contrasts within relevant regions, avoiding unnecessary computational overhead. Features refined by the CTFE module are integrated with lower-level features through the Feature Fusion Module (FFM). Two fusion strategiesAttention-Fusion and Simple-Fusion—were evaluated to identify the most effective method for merging hierarchical features. The decoding process is managed by the Mask-Aware Transformer (MAT) module, which predicts salient regions by restricting attention to mask-defined areas. This strategy ensures that the decoding process prioritizes regions relevant to saliency, enhancing semantic consistency while reducing noise from irrelevant background information. The MAT module’s ability to generate both masks and object confidence scores makes it particularly suited for complex scenes. Multiple loss functions guide the training process: Mask loss, computed using Dice loss, ensures that predicted masks closely align with ground truth. Ranking loss prioritizes the significance of salient regions, while edge loss sharpens boundaries to clearly distinguish salient objects from their background. These objectives are optimized jointly using the Adam optimizer with a dynamically adjusted learning rate. Results and Discussions Experiments were conducted using the PyTorch framework on an RTX 3090 GPU, with training configurations optimized for SOD datasets. The input resolution was set to 384×384 pixels, and data augmentation techniques, such as horizontal flipping and random cropping, were applied. The learning rate was initialized at 6e–6 and adjusted dynamically, with the Adam optimizer employed to minimize the combined loss functions. Experimental evaluations were performed on four widely used datasets: SOD, DUTS-TE, DUT-OMRON, and ECSSD. The proposed model demonstrated exceptional performance across all datasets, showing significant improvements in Mean Absolute Error (MAE) and maximum F-measure metrics. For instance, on the DUTS-TE dataset, the model achieved an MAE of 0.023 and a maximum F-measure of 0.9508, exceeding competing methods such as MENet and VSCode. Visual comparisons indicate that the proposed method generates saliency maps that closely align with the ground truth, effectively addressing challenging scenarios including fine structures, multiple objects, and complex backgrounds. In contrast, other methods often incorporate irrelevant regions or fail to accurately capture object details. Ablation experiments validated the effectiveness of crucial components. For example, the incorporation of the CTFE module resulted in a reduction of MAE from 0.109 to 0.102. Additionally, the Simple-Fusion strategy outperformed the Attention-Fusion approach, yielding a lower MAE and a higher maximum F-measure score. The integration of IOU and BCE-based edge loss further enhanced boundary sharpness, demonstrating superior performance compared to Canny-based edge loss. Heatmaps illustrate the contributions of the CTFE and MAT modules in emphasizing salient regions while preserving semantic consistency. The CTFE effectively accentuates center-surround contrasts, while the MAT captures global object-level semantics. These visualizations highlight the model’s ability to focus on critical areas while minimizing background noise. Conclusions This study presents a novel SOD framework that integrates CNN-style adaptive attention with mask-aware decoding mechanisms. The proposed model addresses the limitations of existing approaches by enhancing semantic consistency and contextual representation while avoiding excessive dependence on global variables. Comprehensive evaluations demonstrate its robustness, generalization capability, and significant performance enhancements across multiple benchmarks. Future research will investigate further optimization of the architecture and its application to multimodal SOD tasks, including RGB-D and RGB-T saliency detection. -
表 1 所有参与评价方法在4个数据集上的Max F-measure, MAE测度的定量评价结果
方法(年份) 速度 (fps) SOD ECSSD DUTS-TE DUT-OMRON MAE↓ $ {F}_{\beta }^{\mathrm{m}\mathrm{a}\mathrm{x}} $↑ MAE↓ $ {F}_{\beta }^{\mathrm{m}\mathrm{a}\mathrm{x}} $↑ MAE↓ $ {F}_{\beta }^{\mathrm{m}\mathrm{a}\mathrm{x}} $↑ MAE↓ $ {F}_{\beta }^{\mathrm{m}\mathrm{a}\mathrm{x}} $↑ EGNet(2019) 30.5 0.0969 0.8778 0.0374 0.9474 0.0386 0.8880 0.0528 0.8155 PoolNet(2019) 32.0 0.1000 0.8690 0.0390 0.9440 0.0400 0.8860 0.0560 0.8300 MINet(2020) 86.1 0.0920 0.8680 0.0342 0.9475 0.0373 0.8833 0.0559 0.8098 AADFNet(2020) 15.0 0.0903 0.8677 0.0280 0.9543 0.0314 0.8993 0.0488 0.8143 SACNet(2021) 11.2 0.0934 0.8804 0.0309 0.9512 0.0339 0.8944 0.0523 0.8287 ICON(2022) 58.5 0.0841 0.8790 0.0318 0.9503 0.0370 0.8917 0.0569 0.8254 MENet(2023) 45.0 0.0874 0.8780 0.0307 0.9549 0.0281 0.9123 0.0380 0.8337 VSCode(2024) 39.8 0.0602 0.8817 0.0245 0.9560 0.0262 0.9150 0.0473 0.8315 本文 46.0 0.0567 0.8872 0.0230 0.9508 0.0243 0.8966 0.0352 0.8290 
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表 2 不同模块的定量消融实验结果
实验 方法 SOD MAE↓ $ {F}_{\beta }^{\mathrm{m}\mathrm{a}\mathrm{x}} $↑ a Baseline 0.109 1 0.869 6 b Baseline+CTFE 0.102 0 0.875 5 c Baseline+CTFE+MAT 0.056 7 0.887 2 d Baseline+CTFE+MAT+
Canny Loss0.058 0 0.885 3 e Baseline+CTFE+MAT+
IOU_BCE Loss0.056 7 0.887 2 f Attention-Fusion 0.064 7 0.876 1 g Simple-Fusion 0.056 7 0.887 2
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表 3 不同损失比重的实验结果
损失比重 SOD ${L_{{\text{mask}}}}$ ${L_{{\text{rank}}}}$ ${L_{{\text{edge}}}}$ MAE↓ $ {F}_{\beta }^{\mathrm{m}\mathrm{a}\mathrm{x}} $↑ 1 0.5 0.5 0.060 0 0.883 3 0.5 1 0.5 0.058 9 0.873 5 0.5 0.5 1 0.073 5 0.871 4 1 1 1 0.056 7 0.887 2
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