Sequential Dual Attention Network for Rain Streak Removal in a Single Image

Lin, Chih-Yang; Zhuang, Tao; Xu, Aisheng; Li, Kang; Akhyar, Fityanul

doi:10.1109/tip.2020.3025402

“…3. We illustrate that the DNNet performance with DAN is superior to the results of applying SENet [43], CBAM [44], SDAB [47] on all performance metrics, and 2.416% higher than the secondbest performance for Rank-1 accuracy.…”

Section: ) Ablation Studiesmentioning

confidence: 86%

“…The A 2 -Nets [46] method proposed a double attention block to determine the novel relation features from the spatialtemporal spaces of the images. Lin et al [47] proposed a novel framework containing sequential dual attention block (SDAB) for removing rain streaks in a single image. We applied the dual attention network (DAN) [31] as the second step in the DNNet framework; the DAN presents NL-based spatial and channel attention to informational features around feature maps.…”

Section: Attention Modulementioning

confidence: 99%

Dog Nose-Print Identification Using Deep Neural Networks

Bae

¹

,

Pak²,

Lee

³

2021

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Recently, there has been rapid growth in the number of people who own companion pets (cats and dogs) due to low birth rates, an increasingly aging population, and an increasing number of singleperson households. This trend has resulted in a growing interest in problems requiring solutions, such as missing pets and false insurance claims. Traditional non-biometric-based methods cannot address these problems. This paper proposes a novel deep-learning model that can extract discriminative features through dog nose-print patterns for individual identification. We present a robust baseline for how individual dogs can be identified. The proposed dog nose network (DNNet) is a convolutional neural network (CNN)-based Siamese network structure comprising feature extraction and self-attention modules. Moreover, there is no need for a separate scanning device because it uses popular mobile devices to acquire the dataset. Besides high recognition performance, the proposed method also ensures simplicity and efficiency. The proposed method achieves better recognition performance than state-of-the-art methods for the collected dog noseprint dataset. It achieves recognition performance superior to state-of-the-art methods for the collected dog nose-print dataset. Using multiple datasets through cross-validation, we acquired an average identification accuracy of 98.972% with the Rank-1 approach. Additional performance benefits were demonstrated through the receiver operating characteristic (ROC) curve, t-distributed stochastic neighbor embedding (t-SNE), and confusion matrix.

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“…MPRNet [50] CCN [34] Semi-DerainGAN [58] CVID [59] RCDNet [53] Pan et al [60] MSPFN [44] RDDAN [61] JDNet [45] QuDeC [62] Syn2Real [ [70] GraNet [46] LPNet [71] PReNet [31] DAF-Net [22] ReHEN [72] DDC-Net [35] RR-GAN [73] MH-DerainNet [74] SIRR [75] SPANet [33] ReMAEN [76] UD-GAN [77] UMRL [78] ID-CGAN [32] Li et al [23] JORDER-E [56] JORDER [21] RWL [79] DualCNN [80] NLEDN [81] RESCAN [55] ResGuideNet [82] Qian et al [25] DID-MDN [24] Li et al [83] DerainNet [83] Fu et al [20] Quan et al [52] SSDRNet [67] MOEDN [56] RICNet [35] JRGR [57] RLNet [59] QSMD [58] Fig. 7: The division of recent SID methods from six aspects based on the three factors.…”

Section: Synthetical Mathematical General Specificmentioning

confidence: 99%

Data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

Zhang¹,

Yue²,

Zhang³

et al. 2021

Preprint

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<div>Abstract—Due to the powerful fitting ability of neural networks and massive training data, data-driven Single Image Deraining (SID) methods have obtained significant performance, and most of the existing studies focus on improving the deraining performance by proposing different kinds of deraining networks. However, the generalization ability of current SID methods may still be limited in the real scenario, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (such as object detection). The main reason may be because the current works mainly focus on designing new deraining neural networks, while neglecting the interpretation of the solving process. To investigate these issues, we in this paper re-examine the three important factors (i.e.,data, rain model and network architecture) for the SID task, and specifically analyze them by proposing new and more reasonable criteria (i.e., general vs. specific, synthetical vs. mathematical, black-box vs. white-box). We will also study the relationship of the three factors from new perspectives of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. Besides, we also profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets via extensive experiments to reveal the effectiveness of SID data. Finally, with the above comprehensive review and in-depth analysis, we also draw some valuable conclusions and suggestions for future research.</div>

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“…( [52] LPNet [78] PReNet [25] DAF-Net [20] ReHEN [79] DDC-Net [41] RR-GAN [80] MH-DerainNet [81] SIRR [28] SPANet [27] ReMAEN [82] UD-GAN [83] UMRL [84] ID-CGAN [22] Li et al [21] JORDER-E [60] JORDER [19] RWL [85] DualCNN [86] NLEDN [87] RESCAN [24] ResGuideNet [88] Qian et al [26] DID-MDN [23] SMRNet [89] DerainNet [57] Fu et al [55] Quan et al [58] SSDRNet [74] MOSS [63] RICNet [39] JRGR [61] RLNet [62] QSMD [38] VRGNet [64] Fig. 7: The division of recent 55 SID methods from six aspects based on the three factors.…”

Section: Implicit Solving Paradigmmentioning

confidence: 99%

Data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

Zhang¹,

Wei²,

Zhang³

et al. 2021

Preprint

3

0

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<div>Abstract—Due to the powerful fitting ability of neural networks and massive training data, data-driven Single Image Deraining (SID) methods have obtained significant performance, and most of the existing studies focus on improving the deraining performance by proposing different kinds of deraining networks. However, the generalization ability of current SID methods may still be limited in the real scenario, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (such as object detection). The main reason may be because the current works mainly focus on designing new deraining neural networks, while neglecting the interpretation of the solving process. To investigate these issues, we in this paper re-examine the three important factors (i.e.,data, rain model and network architecture) for the SID task, and specifically analyze them by proposing new and more reasonable criteria (i.e., general vs. specific, synthetical vs. mathematical, black-box vs. white-box). We will also study the relationship of the three factors from new perspectives of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. Besides, we also profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets via extensive experiments to reveal the effectiveness of SID data. Finally, with the above comprehensive review and in-depth analysis, we also draw some valuable conclusions and suggestions for future research.</div>

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Sequential Dual Attention Network for Rain Streak Removal in a Single Image

Cited by 54 publications

References 45 publications

Dog Nose-Print Identification Using Deep Neural Networks

Dog Nose-Print Identification Using Deep Neural Networks

Data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

Data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

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