Saliency Detection of Panoramic Images Based on Robust Vision Transformer and Multiple Attention
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摘要: 针对当前全景图像显著性检测方法存在检测精度偏低、模型收敛速度慢和计算量大等问题，该文提出一种基于鲁棒视觉变换和多注意力的U型网络(URMNet)模型。该模型使用球形卷积提取全景图像的多尺度特征，减轻了全景图像经等矩形投影后的失真。使用鲁棒视觉变换模块提取4种尺度特征图所包含的显著信息，采用卷积嵌入的方式降低特征图的分辨率，增强模型的鲁棒性。使用多注意力模块，根据空间注意力与通道注意力间的关系，有选择地融合多维度注意力。最后逐步融合多层特征，形成全景图像显著图。纬度加权损失函数使该文模型具有更快的收敛速度。在两个公开数据集上的实验表明，该文所提模型因使用了鲁棒视觉变换模块和多注意力模块，其性能优于其他6种先进方法，能进一步提高全景图像显著性检测精度。Abstract: Considering the problems of low detection accuracy, slow model convergence speed and large amount of computation in current panorama image saliency detection methods, a U-Net with Robust vision transformer and Multiple attention at tention modules (URMNet) is proposed. Sphere convolution is used to extract multi-scale features of panoramic images of the model,while reducing the distortion of panoramic images after equirectangular projection.The robust visual transformer module is used to extract the salient information contained in the feature maps of four scales, and the convolutional embedding is used to reduce the resolution of the feature maps and enhance the robustness of the model. The multiple attention module is used to integrate selectively multi-dimensional attention according to the relationship between spatial attention and channel attention. Finally, the multi-layer features are gradually fused to form a panoramic image saliency map. The latitude weighted loss function is used to make the model in this paper have a faster convergence rate. Experiments on two public datasets show that the model proposed in this paper outperforms other 6 advanced methods due to the use of a robust visual transformer module and a multiple attention module, and can further improve the saliency detection accuracy of panoramic images.
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表 1 不同加权因子的实验结果
SMA β CAM 1−β CC↑ SIM↑ KLDiv↓ NSS↑ AUC_Judd↑ AUC_Borji↑ Grade 0 1.0 0.9005 0.7787 0.1970 3.5346 0.9914 0.9755 3.3809 0.2 0.8 0.8803 0.7785 0.4403 3.4964 0.9861 0.9618 0.7047 0.4 0.6 0.9008 0.7921 0.5070 3.7190 0.9936 0.9795 3.4726 0.5 0.5 0.9067 0.8119 0.2198 3.2849 0.9898 0.9772 3.5050 0.6 0.4 0.8912 0.7871 0.2350 3.2212 0.9840 0.9655 1.2312 0.8 0.2 0.8771 0.7561 0.2317 3.2893 0.9864 0.9756 1.1757 1.0 0 0.9023 0.7890 0.3775 3.6737 0.9919 0.9774 3.4871 
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表 2 AAOI数据集上各模型客观指标对比
方法 CC↑ SIM↑ KLDiv↓ NSS↑ AUC_Judd↑ AUC_Borji↑ URMNet 0.8934 0.7918 0.1787 3.7113 0.9865 0.9707 U-Net(2015)[16] 0.8550 0.7694 0.2647 2.9639 0.9741 0.9582 AttU-Net(2018)[17] 0.8663 0.7675 0.2359 3.3212 0.9796 0.9632 Spherical U-Net(2018)[3] 0.7832 0.7304 0.3167 2.4795 0.9467 0.9295 panoramic CNN(2020)[5] 0.8520 0.7533 0.2412 3.1999 0.9778 0.9641 QAU-Net(2021)[18] 0.7314 0.6530 0.4203 1.8678 0.9226 0.8926 UCTransNet(2021)[19] 0.8619 0.7731 0.2105 3.0204 0.9814 0.9625
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表 3 ASalient360数据集上各模型客观指标对比
方法 CC↑ SIM↑ KLDiv↓ NSS↑ AUC_Judd↑ AUC_Borji↑ URMNet 0.6683 0.6602 0.5834 2.9874 0.9449 0.9336 U-Net(2015)[16] 0.6061 0.6404 0.4397 2.6228 0.9180 0.8973 AttU-Net(2018)[17] 0.5589 0.6262 0.4892 1.9840 0.8871 0.8644 Spherical U-Net(2018)[3] 0.6028 0.6412 0.6714 2.5834 0.9384 0.9183 panoramic CNN(2020)[5] 0.6225 0.6527 0.4049 2.0324 0.8975 0.8482 QAU-Net(2021)[18] 0.5641 0.6280 0.5138 2.7556 0.8889 0.8859 UCTransNet(2021)[19] 0.5429 0.6165 0.5246 2.0041 0.8990 0.8683
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表 4 网络和损失函数的消融实验
模型 损失函数 CC↑ SIM↑ KLDiv↓ NSS↑ AUC_Judd↑ AUC_Borji↑ Baseline URMNet L1 L2 L3 Loss √ √ 0.7389 0.6935 2.2079 1.5107 0.8634 0.8310 √ √ 0.7300 0.6886 2.2614 1.5002 0.8636 0.8169 √ √ 0.6629 0.6604 1.2939 1.2369 0.8184 0.7806 √ √ 0.8604 0.7618 0.3906 3.0463 0.9846 0.9614 √ √ 0.7456 0.7007 2.2068 1.5043 0.8627 0.8304 √ √ 0.7565 0.6845 2.9497 1.6848 0.8909 0.8166 √ √ 0.7557 0.6909 3.1000 1.6607 0.8830 0.8465 √ √ 0.9067 0.8119 0.2198 3.2849 0.9898 0.9772
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表 5 RVT和MA模块消融实验结果
RVT MA CC↑ SIM↑ KLDiv↓ NSS↑ AUC_Judd↑ AUC_Borji↑ × × 0.8335 0.7392 0.2947 2.9639 0.9779 0.9604 √ × 0.8664 0.7788 0.3682 3.0885 0.9821 0.9702 × √ 0.8553 0.7118 0.3317 3.4291 0.9848 0.9576 √ √ 0.8922 0.7805 0.2278 3.3100 0.9910 0.9786
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表 6 不同模型泛化性能对比
方法 CC↑ SIM↑ KLDiv↓ NSS↑ AUC_Judd↑ AUC_Borji↑ URMNet 0.5899 0.6116 1.0708 1.8395 0.9181 0.8917 U-Net(2015)[16] 0.5402 0.5733 1.9622 1.6975 0.9022 0.8750 AttU-Net(2018)[17] 0.4906 0.5572 1.9561 1.4668 0.8896 0.8556 panoramic CNN(2020)[5] 0.5146 0.5770 1.1245 1.4486 0.8865 0.8504 QAU-Net(2021)[18] 0.5044 0.5989 0.5373 1.5522 0.8854 0.8362 UCTransNet(2021)[19] 0.5410 0.5760 1.8189 1.7359 0.9100 0.8874
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