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: Salient Object Detection (SOD) aims to mimic the human visual system’s attention and cognitive mechanisms to automatically extract prominent objects from a scene. While existing Convolutional Neural Network (CNN)- or Transformer-based models continuously advance performance in this field, there is less research addressing two specific issues: (1) Most methods in this domain commonly use a pixel-wise dense prediction approach to obtain pixel saliency values. However, this approach does not align with the human visual system’s scene analysis mechanism, where the human eye usually performs a holistic analysis of semantic regions rather than focusing on pixel-level information. (2) Enhancing contextual information association is widely emphasized in SOD tasks, but acquiring long-range contextual features through a Transformer backbone does not necessarily offer advantages. SOD should focus more on the center-neighborhood differences within appropriate regions rather than global long-range dependencies. To address these issues, we propose a novel salient object detection model that integrates CNN-based adaptive attention and masked attention into the network to enhance SOD performance. The proposed algorithm designs a mask-aware decoding module that perceives image features by restricting cross-attention to the predicted mask region, helping the network better focus on the entire region of salient objects. Additionally, we design a convolutional attention-based contextual feature enhancement module, which, unlike Transformers that establish long-range relationships layer by layer, captures appropriate contextual associations only in the highest-level features, avoiding the introduction of irrelevant global information. We conducted experimental evaluations on four widely used datasets, and the results demonstrate that our proposed method achieves significant performance improvements across different scenarios, showcasing good generalization ability and stability. -
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
Methods (Years) Speed (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(2022) 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 ours 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
Experiments Methods 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
Weight of Loss 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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