Original: P ROMOTING S ALIENCY F ROM D EPTH : D EEP U NSUPERVISED RGB-D S ALIENCY D ETECTION
Revised: P ROMOTING S ALIENCY F ROM D EPTH : D EEP U NSUPERVISED RGB-D S ALIENCY D ETECTION

Original: Anonymous authors
Revised: Anonymous authors

Original: Paper under double-blind review
Revised: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Growing interests in RGB-D salient object detection (RGB-D SOD) have been witnessed in recent years, owing partly to the popularity of depth sensors and the rapid progress of deep learning techniques.
Revised: Growing interests in RGB-D salient object detection (RGB-D SOD) have been witnessed in recent years, owing partly to the popularity of depth sensors and the rapid progress of deep learning techniques.

Original: However, existing RGB-D SOD methods typically demand large quantity of training images being thoroughly annotated at pixel-level.
Revised: However, existing RGB-D SOD methods typically demand large quantity of training images being thoroughly annotated at pixel-level.

Original: The laborious and time-consuming manual annotation becomes a real bottleneck in various practical scenarios.
Revised: The laborious and time-consuming manual annotation becomes a real bottleneck in various practical scenarios.

Original: On the other hand, current unsupervised RGB-D SOD methods still heavily rely on handcrafted feature representations.
Revised: On the other hand, current unsupervised RGB-D SOD methods still heavily rely on handcrafted feature representations.

Original: In this paper, we make the earliest effort to propose a deep unsupervised
Revised: In this paper, we make the earliest effort to propose a deep unsupervised

Original: RGB-D saliency detection approach that does not need any manual pixel-level annotations in the training stage.
Revised: RGB-D saliency detection approach that does not need any manual pixel-level annotations in the training stage.

Original: It is made possible by two key ingredients in our training pipeline.
Revised: It is made possible by two key ingredients in our training pipeline.

Original: First, a depth-disentangled saliency update (DSU)  is designed to automatically produce pseudo-labels with iterative follow-up reﬁnements, which provides more trustworthy supervisions for training the saliency network.
Revised: First, a depth-disentangled saliency update (DSU) framework is designed to automatically produce pseudo-labels with iterative follow-up reﬁnements, which provides more trustworthy supervision signals for training the saliency network.

Original: Second, an attentive training strategy is introduced to tackle the issue of noisy pseudolabels, by properly re-weighting to highlight the more reliable pseudo-labels.
Revised: Second, an attentive training strategy is introduced to tackle the issue of noisy pseudo-labels, by properly re-weighting to highlight the more reliable pseudo-labels.

Original: Extensive experiments demonstrate the superior efﬁciency and effectiveness of our approach in addressing unsupervised RGB-D SOD.
Revised: Extensive experiments demonstrate the superior efﬁciency and effectiveness of our approach in addressing unsupervised RGB-D SOD.

Original: Moreover, our approach can be easily adapted to tackle the fully-supervised scenario, where empirical studies show that by incorporating our method, the performance of existing RGB-D SOD models can be signiﬁcantly improved.
Revised: Moreover, our approach can be easily adapted to tackle the fully-supervised scenario, where empirical studies show that by incorporating our method, the performance of existing RGB-D SOD models can be signiﬁcantly improved.

Original: Source code will be  publicly available.
Revised: Source code will be made publicly available.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: The recent development of RGB-D salient object detection, i.e. , RGB-D SOD, is especially fueled by the increasingly accessible 3D imaging sensors (Giancola et al., 2018) and their diverse applications, including image caption generation (Xu et al., 2015), image retrieval (Ko et al., 2004; Shao & Brady, 2006) and video analysis (Liu et al., 2008; Wang et al., 2018), to name a few.
Revised: The recent development of RGB-D salient object detection, i.e. , RGB-D SOD, is especially fueled by the increasingly accessible 3D imaging sensors (Giancola et al., 2018) and their diverse applications, including image caption generation (Xu et al., 2015), image retrieval (Ko et al., 2004; Shao & Brady, 2006) and video analysis (Liu et al., 2008; Wang et al., 2018), to name a few.

Original: Provided the multimodality input of an RGB image and its depth map, the task of RGB-D SOD is to effectively identify and segment the most distinctive objects in a scene.
Revised: Provided the multimodality input of an RGB image and its depth map, the task of RGB-D SOD is to effectively identify and segment the most distinctive objects in a scene.

Original: The state-of-the-art RGB-D SOD approaches (Li et al., 2021;
Revised: The state-of-the-art RGB-D SOD approaches (Li et al., 2021;

Original: Ji et al., 2021; Chen et al., 2020b; Li et al., 2020b) typically entail an image-to-mask mapping pipeline that is based on the powerful deep learning paradigms of e.g. , VGG16 (Simonyan & Zisserman, 2015) or ResNet50 (He et al., 2016), which have proven to be effective, and have achieved excellent performance.
Revised: Ji et al., 2021; Chen et al., 2020b; Li et al., 2020b) typically entail an image-to-mask mapping pipeline that is based on the powerful deep learning paradigms of e.g. , VGG16 (Simonyan & Zisserman, 2015) or ResNet50 (He et al., 2016), which have proven to be effective, and have achieved excellent performance.

Original: On the other hand, these RGB-D SOD methods are fully supervised, thus demand a signiﬁcant amount of pixel-level training annotations.
Revised: On the other hand, these RGB-D SOD methods are fully supervised, thus demand a signiﬁcant amount of pixel-level training annotations.

Original: This however becomes much less appealing in practical scenarios, owing to the laborious and time-consuming process in obtaining manual annotations.
Revised: This however becomes much less appealing in practical scenarios, owing to the laborious and time-consuming process in obtaining manual annotations.

Original: It is therefore natural and desirable to contemplating unsupervised alternatives.
Revised: It is therefore natural and desirable to contemplating unsupervised alternatives.

Original: Unfortunately, existing unsupervised RGB-D
Revised: Unfortunately, existing unsupervised RGB-D

Original: SOD methods, such as global priors (Ren et al., 2015), center prior (Zhu et al., 2017b), and depth contrast prior (Ju et al., 2014), rely primarily on handcrafted feature representations.
Revised: SOD methods, such as global priors (Ren et al., 2015), center prior (Zhu et al., 2017b), and depth contrast prior (Ju et al., 2014), rely primarily on handcrafted feature representations.

Original: This is in stark contrast to the deep representations learned by their supervised SOD counterparts, which in effect imposes severe limitations on the feature representation power that may otherwise beneﬁt greatly from the potentially abundant unlabeled RGB-D images.
Revised: This is in stark contrast to the deep representations learned by their supervised SOD counterparts, which in effect imposes severe limitations on the feature representation power that may otherwise beneﬁt greatly from the potentially abundant unlabeled RGB-D images.

Original: These observations motivate us to explore a new problem of deep unsupervised RGB-D saliency detection : given an unlabeled set of RGB-D images, deep neural network is trained to predict saliency without any laborious human annotations in the training stage.
Revised: These observations motivate us to explore a new problem of deep unsupervised RGB-D saliency detection : given an unlabeled set of RGB-D images, deep neural network is trained to predict saliency without any laborious human annotations in the training stage.

Original: A relatively straightforward idea is to 
Revised: A relatively straightforward idea is to exploit the outputs from traditional RGB-D method as pseudo-labels, which are internally employed

Original: SE
Revised: SE

Original: DSR
Revised: DSR

Original: CDCP
Revised: CDCP

Original: GP
Revised: GP

Original: DES DCMC MST
Revised: DES DCMC MST

Original: ACSD
Revised: ACSD

Original: LHM
Revised: LHM

Original: BSCA RBD CDB
Revised: BSCA RBD CDB

Original: Baseline 12 10 8 6 4 

Revised: Baseline 12 10 8 6 4

Original: Inference time (s)
Revised: Inference time (s)

Original: W e i gh t ed F - m ea s u r e
Revised: W e i gh t ed F - m ea s u r e

Original: A cc u r a cy ( % )
Revised: A cc u r a cy ( % )

Original: RGB-based method
Revised: RGB-based method

Original: RGB&D-based method
Revised: RGB&D-based method

Original: Ours +37%
Revised: Ours +37%

Original: Unlabeled/LabeldedTrainingData ： RGBDepthGT
Revised: Unlabeled/LabeldedTrainingData ： RGBDepthGT

Original: Tradition ， Baseline ， Ours
Revised: Tradition ， Baseline ， Ours

Original: Initial Label
Revised: Initial Label

Original: Baseline
Revised: Baseline

Original: Ours
Revised: Ours

Original: RGB Image
Revised: RGB Image

Original: Ground Truth
Revised: Ground Truth

Original: [Figure]
Revised: [Figure]

Original: exploit the outputs from traditional RGB-D method as pseudo-labels, which are internally employed to train the saliency prediction network (‘baseline’).
Revised: to train the saliency prediction network (‘baseline’).

Original: Moreover, the input depth map may serve as a complementary source of information in reﬁning the pseudo-labels, as it contains cues of spatial scene layout that may help in exposing the salient objects.
Revised: Moreover, the input depth map may serve as a complementary source of information in reﬁning the pseudo-labels, as it contains cues of spatial scene layout that may help in exposing the salient objects.

Original: Nevertheless, practical examination reveals two main issues: (1) Inconsistency and large variations in raw depth maps : as illustrated in
Revised: Nevertheless, practical examination reveals two main issues: (1) Inconsistency and large variations in raw depth maps : as illustrated in

Original: Fig.
Revised: Fig.

Original: 1 (a), similar depth values are often shared by a salient object and its surrounding, making it very difﬁcult in extracting the salient regions from depth without explicit pixel-level supervision; (2) Noises from unreliable pseudo-labels : some unreliable pseudo-labels may inevitably bring false positive into training, resulting in severely damage in its prediction performance.
Revised: 1 (a), similar depth values are often shared by a salient object and its surrounding, making it very difﬁcult in extracting the salient regions from depth without explicit pixel-level supervision; (2) Noises from unreliable pseudo-labels : some unreliable pseudo-labels may inevitably bring false positive into training, resulting in severe damage in its prediction performance.

Original: To address the above challenges, we propose a dedicated method that contains the following two key components.
Revised: To address the above challenges, we propose a dedicated method that contains the following two key components.

Original: First, a depth-disentangled saliency update (DSU) strategy is proposed to iteratively reﬁne & update the pseudo-labels by engaging the depth input.
Revised: First, a depth-disentangled saliency update (DSU) framework is proposed to iteratively reﬁne & update the pseudo-labels by engaging the depth knowledge.

Original: In it, a depth-disentangled network is to explicitly learn the discriminative saliency cues and non-salient background fromthe raw depth input, denoted as saliency-guided depth D Sal and non-saliency-guided depth D NonSal , respectively.
Revised: In it, a depth-disentangled network is to explicitly learn the discriminative saliency cues and non-salient background from raw depth map, denoted as saliency-guided depth D Sal and non-saliency-guided depth D NonSal , respectively.

Original: This is followed by a depth-disentangled label update (DLU) module that takes advantage of D Sal to emphasize saliency response from pseudo-label, and utilizes D NonSal to eliminate the background inﬂuence, gearing toward facilitating more trustworthy supervision signals in training the saliency network.
Revised: This is followed by a depth-disentangled label update (DLU) module that takes advantage of D Sal to emphasize saliency response from pseudo-label, and utilizes D NonSal to eliminate the background inﬂuence, gearing toward facilitating more trustworthy supervision signals in training the saliency network.

Original: Note the DSU module is not engaged at test time.
Revised: We note that the DSU module is not performed at test time, so does not affect the inference speed.

Original: That is, in the inference stage, our trained model takes as input only the RGB image, without involving the depth map in computation.
Revised: That is, in the inference stage, our trained model takes as input only the RGB image, without involving the depth map into computation.

Original: Second, an attentive training strategy is introduced to alleviate the issue of noisy pseudo-labels, by re-weighting the training samples in each training batch to focus on those more reliable pseudolabels.
Revised: Second, an attentive training strategy is introduced to alleviate the issue of noisy pseudo-labels, by re-weighting the training samples in each training batch to focus on those more reliable pseudo-labels.

Original: As demonstrated in Fig.
Revised: As demonstrated in Fig.

Original: 1 (b), our approach works effectively and efﬁciently in practice, signiﬁcantly outperforming current unsupervised SOD methods on the widely-used NLPR benchmark.
Revised: 1 (b), our approach works effectively and efﬁciently in practice, signiﬁcantly outperforming current unsupervised SOD methods on the widely-used NLPR benchmark.

Original: Speciﬁcally, it improves over the baseline by 37%, a signiﬁcant amount without incurring extra computation cost.
Revised: Speciﬁcally, it improves over the baseline by 37%, a signiﬁcant amount without incurring extra computation cost.

Original: Consequently, the test time execution of our approach is at 35 frame-persecond (FPS), the fastest among all RGB-D unsupervised methods, and on par with the most efﬁcient RGB-based methods.
Revised: Consequently, the test time execution of our approach is at 35 frame-per-second (FPS), the fastest among all RGB-D unsupervised methods, and on par with the most efﬁcient RGB-based methods.

Original: In summary, our main contributions are as follows:
Revised: In summary, our main contributions are as follows:

Original: • To our knowledge, our work is the ﬁrst in exploring deep representation to tackle the problem of unsupervised RGB-D saliency detection.
Revised: • To our knowledge, our work is the ﬁrst in exploring deep representation to tackle the problem of unsupervised RGB-D saliency detection.

Original: This is enabled by two key components in the training process, namely the DSU strategy to produce & reﬁne pseudo-labels, and the attentive training strategy to alleviate the inﬂuence of noisy pseudo-labels.
Revised: This is enabled by two key components in the training process, namely the DSU strategy to produce & reﬁne pseudo-labels, and the attentive training strategy to alleviate the inﬂuence of noisy pseudo-labels.

Original: This results in a light-weight architecture that engages only RGB data at test time ( i.e., w/o depth map), achieving a signiﬁcant improvement without extra computation cost.
Revised: This results in a light-weight architecture that engages only RGB data at test time ( i.e., w/o depth map), achieving a signiﬁcant improvement without extra computation cost.

Original: • Empirically, our approach outperforms state-of-the-art unsupervised methods on four wellknown benchmarks.
Revised: • Empirically, our approach outperforms state-of-the-art unsupervised methods on four public benchmarks.

Original: Moreover, it runs in real time at 35 FPS, much faster thanany existing unsupervised RGB-D SOD methods, and at least on par with the fastest RGB counterparts.
Revised: Moreover, it runs in real time at 35 FPS, much faster than existing unsupervised RGB-D SOD methods, and at least on par with the fastest RGB counterparts.

Original: • Our approach could be adapted to work with fully-supervised scenario.
Revised: • Our approach could be adapted to work with fully-supervised scenario.

Original: As demonstrated in Sec. 4.4, augmented with our proposed DSU module, the performance of existing RGB-D SOD models can be signiﬁcantly boosted.
Revised: As demonstrated in Sec. 4.4, augmented with our proposed DSU module, the performance of existing RGB-D SOD models can be signiﬁcantly boosted.

Original: Source code will be made publicly available.
Revised: Source code will be made publicly available.

Original:  R ELATED W ORK
Revised:  R ELATED W ORK

Original: Remarkable progresses have been made recently in salient object detection (Ma et al., 2021;
Revised: Remarkable progresses have been made recently in salient object detection (Ma et al., 2021;

Original: Xu et al., 2021; Wei et al., 2020; Wu et al., 2019b; Fan et al., 2017; Liu et al., 2018), where the performance tends to deteriorate in the presence of cluttered or low-contrast backgrounds.
Revised: Xu et al., 2021; Wei et al., 2020; Wu et al., 2019b; Fan et al., 2017; Liu et al., 2018), where the performance tends to deteriorate in the presence of cluttered or low-contrast backgrounds.

Original: Moreover, promising results are shown by a variety of recent efforts (Chen et al., 2021a;
Revised: Moreover, promising results are shown by a variety of recent efforts (Chen et al., 2021a;

Original: Luo et al., 2020;
Revised: Luo et al., 2020;

Original: Chen et al., 2020a;
Revised: Chen et al., 2020a;

Original: Cong et al., 2020; 2018; Desingh et al., 2013;
Revised: Cong et al., 2020; 2018; Desingh et al., 2013;

Original: Li et al., 2020a; Liao et al., 2020;
Revised: Li et al., 2020a; Liao et al., 2020;

Original: Shigematsu et al., 2017) that integrate effective depth cues to tackle these issues.
Revised: Shigematsu et al., 2017) that integrate effective depth cues to tackle these issues.

Original: Those methods, however, typically demand extensive annotations, which are labor-intensive and time-consuming.
Revised: Those methods, however, typically demand extensive annotations, which are labor-intensive and time-consuming.

Original: This naturally leads to the consideration of unsupervised SODs that do not rely on such annotations.
Revised: This naturally leads to the consideration of unsupervised SODs that do not rely on such annotations.

Original: Prior to the deep learning era (Wang et al., 2021; Zhao et al., 2020a), traditional RGB-D saliency methods are mainly based on the manually-crafted RGB features and depth cues to infer a saliency map.
Revised: Prior to the deep learning era (Wang et al., 2021; Zhao et al., 2020a), traditional RGB-D saliency methods are mainly based on the manually-crafted RGB features and depth cues to infer a saliency map.

Original: Due to their lack of reliance on manual human annotation, these traditional methods can be regarded as early manifestations of unsupervised SOD.
Revised: Due to their lack of reliance on manual human annotation, these traditional methods can be regarded as early manifestations of unsupervised SOD.

Original: Ju et al .
Revised: Ju et al .

Original: (Ju et al., 2014) consider the incorporation of a prior induced from anisotropic center-surround operator, and an additional depth prior.
Revised: (Ju et al., 2014) consider the incorporation of a prior induced from anisotropic center-surround operator, and an additional depth prior.

Original: Ren et al .
Revised: Ren et al .

Original: (Ren et al., 2015) also introduce two priors: normalized depth prior and the globalcontext surface orientation prior.
Revised: (Ren et al., 2015) also introduce two priors: normalized depth prior and the globalcontext surface orientation prior.

Original: In contrary to direct utilization of the depth contrast priors, local background enclosure prior is explicitly developed by (Feng et al., 2016).
Revised: In contrary to direct utilization of the depth contrast priors, local background enclosure prior is explicitly developed by (Feng et al., 2016).

Original: Interested readers may refer to (Fan et 
Revised: Interested readers may refer to (Fan et al., 2020a;

Revised: Peng et al., 2014;

Revised: Zhou et al., 2021) for more in-depth surveys and analyses.

Original: However, by relying on manually-crafted priors, these methods tend to have inferior performance.
Revised: However, by relying on manually-crafted priors, these methods tend to have inferior performance.

Original: Meanwhile, considerable performance gain has been achieved by recent efforts in RGB-based unsupervised SOD (Zhang et al., 2017; 2018; Nguyen et al., 2019), which instead construct automated feature representations using deep learning.
Revised: Meanwhile, considerable performance gain has been achieved by recent efforts in RGB-based unsupervised SOD (Zhang et al., 2017; 2018; Nguyen et al., 2019), which instead construct automated feature representations using deep learning.

Original: A typical strategy is to leverage the noisy output produced by traditional methods as pseudo-label ( i.e. , supervisory signal) for training saliency prediction net.
Revised: A typical strategy is to leverage the noisy output produced by traditional methods as pseudo-label ( i.e. , supervisory signal) for training saliency prediction net.

Original: The pioneering work of Zhang et al .
Revised: The pioneering work of Zhang et al .

Original: (Zhang et al., 2017) fuses the outputs of multiple unsupervised saliency models as guiding signals in CNN training.
Revised: (Zhang et al., 2017) fuses the outputs of multiple unsupervised saliency models as guiding signals in CNN training.

Original: In (Zhang et al., 2018), competitive performance is achieved by ﬁtting a noise modeling module to the noise distribution of pseudolabel.
Revised: In (Zhang et al., 2018), competitive performance is achieved by ﬁtting a noise modeling module to the noise distribution of pseudolabel.

Original: Instead of directly using pseudo-labels from handcrafted methods, Nguyen et al .
Revised: Instead of directly using pseudo-labels from handcrafted methods, Nguyen et al .

Original: (Nguyen et al., 2019) further reﬁne pseudo-labels via a self-supervision iterative process.
Revised: (Nguyen et al., 2019) further reﬁne pseudo-labels via a self-supervision iterative process.

Original: These methods demonstrate that the incorporation of powerful deep neural network brings better feature representation than those unsupervised SOD counterparts based on handcrafted features.
Revised: These methods demonstrate that the incorporation of powerful deep neural network brings better feature representation than those unsupervised SOD counterparts based on handcrafted features.

Original: In this work, a principled research investigation on deep unsupervised RGB-D saliency detection is presented for the ﬁrst time.
Revised: In this work, a principled research investigation on deep unsupervised RGB-D saliency detection is presented for the ﬁrst time.

Original: Different from existing deep RGB-based unsupervised SOD (Zhang et al., 2017; 2018) that use pseudo-labels from the handcrafted methods directly, our work aims to reﬁne and update pseudo-labels to remove the undesired noise.
Revised: Different from existing deep RGB-based unsupervised SOD (Zhang et al., 2017; 2018) that use pseudo-labels from the handcrafted methods directly, our work aims to reﬁne and update pseudo-labels to remove the undesired noise.

Original: And compared to the reﬁnement method (Nguyen et al., 2019) using self-supervision technique, our approach instead adopts disentangled depth information to promote pseudo-labels.
Revised: And compared to the reﬁnement method (Nguyen et al., 2019) using self-supervision technique, our approach instead adopts disentangled depth information to promote pseudo-labels.

Original:  M ETHODOLOGY 3. O VERVIEW
Revised:  M ETHODOLOGY 3. O VERVIEW

Original: Fig.
Revised: Fig.

Original: 2 presents an overview of our Depth-disentangled Saliency Update (DSU) framework.
Revised: 2 presents an overview of our Depth-disentangled Saliency Update (DSU) framework.

Original: Overall our DSU strives to signiﬁcantly improve the quality ofnoisy pseudo-labels, that leads to more trustworthy supervision signals for  the saliency network.
Revised: Overall our DSU strives to signiﬁcantly improve the quality of pseudo-labels, that leads to more trustworthy supervision signals for training the saliency network.

Original: It consists of three key components.
Revised: It consists of three key components.

Original: First is a saliency network responsible for saliency prediction, whose initial supervision signal is provided by traditional handcrafted method without using human annotations.
Revised: First is a saliency network responsible for saliency prediction, whose initial supervision signal is provided by traditional handcrafted method without using human annotations.

Original: Second , a depth network and a depth-disentangled network are designed to decompose depth cues into saliency-guided depth D Sal and non-saliency-guided depth D NonSal , to explicitly depict saliency cues in the spatial layout.
Revised: Second , a depth network and a depth-disentangled network are designed to decompose depth cues into saliency-guided depth D Sal and non-saliency-guided depth D NonSal , to explicitly depict saliency cues in the spatial layout.

Original: The supervision signals for training the depth network and depth-disentangled network (red arrows in

Original: Fig. 2) will be detailed in Sec.

Original: Third , a depth-disentangled label update (DLU) module is devised to reﬁne and update the pseudo-labels, by engaging the learned D Sal and D NonSal .
Revised: Third , a depth-disentangled label update (DLU) module is devised to reﬁne and update the pseudolabels, by engaging the learned D Sal and D NonSal .

Original: Extensive experiments reveal that the updated pseudo labels could in turn provide more trustworthy supervisions for the saliency network.
Revised: The updated pseudo-labels could in turn provide more trustworthy supervisions for the saliency network.

Original: Moreover, an attentive training strategy (ATS) is incorporated when training the saliency network, tailored for noisy unsupervised learning by mitigating the ambiguities caused by noisy labels.
Revised: Moreover, an attentive training strategy (ATS) is incorporated when training the saliency network, tailored for noisy unsupervised learning by mitigating the ambiguities caused by noisy pseudo-labels.

Original: We note that the DSU and ATS are not performed at test time, 
Revised: We note that the DSU and ATS are not performed at test time, so does not affect the inference speed. In other words, the inference stage

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: so does not affect the inference time.

Original: In other words, the inference stage involves only the black dashed portion of the proposed network architecture in Fig. 
Revised: involves only the black dashed portion of the proposed network architecture in Fig. i

Original: 2, i.e. , only RGB images are used for predicting saliency, which enables very efﬁcient detection.
Revised: [Figure] e. , only RGB images are used for predicting saliency, which enables very efﬁcient detection.

Original: 3. D EPTH - DISENTANGLED S ALIENCY U PDATE
Revised: 3. D EPTH - DISENTANGLED S ALIENCY U PDATE

Original: The scene geometry embedded in the depth map can serve as a complementary information source to reﬁne the pseudo-labels.
Revised: The scene geometry embedded in the depth map can serve as a complementary information source to reﬁne the pseudo-labels.

Original: Nevertheless, as discussed in Sec.1, a direct adoption of the raw depth may not necessarily lead to good performance due to the large value variations and inherit noises in raw depth map.
Revised: Nevertheless, as discussed in Sec.1, a direct adoption of the raw depth may not necessarily lead to good performance due to the large value variations and inherit noises in raw depth map.

Original: Without explicit pixel-wise supervision, the saliency model may unfortunately be confused by nearby background regions having the same depth but with different contexts; meanwhile, depth variations in salient or background regions may also result in false responses.
Revised: Without explicit pixel-wise supervision, the saliency model may unfortunately be confused by nearby background regions having the same depth but with different contexts; meanwhile, depth variations in salient or background regions may also result in false responses.

Original: These observations motivate us to design a depth-disentangled network to effectively capture discriminative saliency cues from depth, and a depth-disentangled label update (DLU) strategy to improve and reﬁne pseudo-labels by engaging the disentangled depth knowledges.
Revised: These observations motivate us to design a depth-disentangled network to effectively capture discriminative saliency cues from depth, and a depth-disentangled label update (DLU) strategy to improve and reﬁne pseudo-labels by engaging the disentangled depth knowledges.

Original: 3.2. D EPTH - DISENTANGLED N ETWORK
Revised: 3.2. D EPTH - DISENTANGLED N ETWORK

Original: The depth-disentangled network aims at capturing valuable saliency as well as redundant non-salient cues from raw depth map.
Revised: The depth-disentangled network aims at capturing valuable saliency as well as redundant non-salient cues from raw depth map.

Original: As presented in the bottom of Fig.
Revised: As presented in the bottom of Fig.

Original: 2, informative depth feature F Depth are ﬁrst extracted from the depth network under the supervision of raw depth map, using the mean square error (MSE) loss function, i.e., l d, 1 .
Revised: 2, informative depth feature F Depth are ﬁrst extracted from the depth network under the supervision of raw depth map, using the mean square error (MSE) loss function, i.e., l d, 1 .

Original: The F Depth is then decomposed into saliency-guided depth D Sal and non-saliency-guided depth D NonSal following two principles: 1) explicitly guiding the model to learn saliency-speciﬁc cues from depth; 2) ensuring the coherence between the disentangled and original depth features.
Revised: The F Depth is then decomposed into saliency-guided depth D Sal and non-saliency-guided depth D NonSal following two principles: 1) explicitly guiding the model to learn saliency-speciﬁc cues from depth; 2) ensuring the coherence between the disentangled and original depth features.

Original: Speciﬁcally, in the bottom right of Fig. 2, we ﬁrst construct the spatial supervision signals for the depth-disentangled network.
Revised: Speciﬁcally, in the bottom right of Fig. 2, we ﬁrst construct the spatial supervision signals for the depth-disentangled network.

Original: Given the rough saliency prediction Sal pred from the saliency network and the raw depth map D map , the (non-)saliency-guided depth masks, i.e., D LabelSal and D LabelNonSal , can be obtained by multiplying Sal pred (or 1 − Sal pred ) and depth map D map in a spatial attention manner.
Revised: Given the rough saliency prediction Sal pred from the saliency network and the raw depth map D map , the (non-)saliency-guided depth masks, i.e., D LabelSal and D LabelNonSal , can be obtained by multiplying Sal pred (or 1 − Sal pred ) and depth map D map in a spatial attention manner.

Original: Since the predicted saliency may contain errors introduced from the inaccurate pseudolabels, we employ a holistic attention (HA) operation (Wu et al., 2019a) to smooth the coverage area of the predicted saliency, so as to effectively perceive more saliency area from depth.
Revised: Since the predicted saliency may contain errors introduced from the inaccurate pseudolabels, we employ a holistic attention (HA) operation (Wu et al., 2019a) to smooth the coverage area of the predicted saliency, so as to effectively perceive more saliency area from depth.

Original: Formally, the (non-)saliency-guided depth masks are generated by:
Revised: Formally, the (non-)saliency-guided depth masks are generated by:

Original: [Equation]
Revised: [Equation]

Original: where F G ( · , k ) represents the HA operation, which is implemented using the convolution operation with Gaussian kernel k and zero bias; the size and standard deviation of the Gaussian kernel k are
Revised: where F G ( · , k ) represents the HA operation, which is implemented using the convolution operation with Gaussian kernel k and zero bias; the size and standard deviation of the Gaussian kernel k are

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: initialized with 32 and 4, respectively, which are then ﬁnetuned through the training procedure; Ψ max ( · , · ) is a maximum function to preserve the higher values from the Gaussian ﬁltered map and the original map. ⊗ denotes pixel-wise multiplication.
Revised: initialized with 32 and 4, respectively, which are then ﬁnetuned through the training procedure; Ψ max ( · , · ) is a maximum function to preserve the higher values from the Gaussian ﬁltered map and the original map; ⊗ denotes pixel-wise multiplication.

Original: Building upon the guidance of D LabelSal and D LabelNonSal , F Depth is fed into D-Sal CNN and D-NonSal
Revised: Building upon the guidance of D LabelSal and D LabelNonSal , F Depth is fed into D-Sal CNN and D-NonSal

Original: CNN to explicitly learn valuable saliency and redundant non-salient cues from depth map, generating D Sal and D NonSal , respectively.
Revised: CNN to explicitly learn valuable saliency and redundant non-salient cues from depth map, generating D Sal and D NonSal , respectively.

Original: The loss functions here ( i.e., l Sal and l NonSal ) are MSE loss.
Revised: The loss functions here ( i.e., l Sal and l NonSal ) are MSE loss.

Original: Detailed structures of D-Sal and D-NonSal CNNs are in the appendix.
Revised: Detailed structures of D-Sal and D-NonSal CNNs are in the appendix.

Original: To further ensure the coherence between the disentangled and original depth features, a consistency loss, l con , is employed as:
Revised: To further ensure the coherence between the disentangled and original depth features, a consistency loss, l con , is employed as:

Original: [Equation]
Revised: [Equation]

Original: where ˜ F Depth is the sum of the disentangled F DepthSal and F DepthNonSal , which denotes the regenerated depth feature; H , W and C are the height, width and channel of F Depth and ˜ F Depth ; (cid:107)·(cid:107) 2 represents
Revised: where ˜ F Depth is the sum of the disentangled F DepthSal and F DepthNonSal , which denotes the regenerated depth feature; H , W and C are the height, width and channel of F Depth and ˜ F Depth ; (cid:107)·(cid:107) 2 represents

Original: Euclidean norm.
Revised: Euclidean norm.

Original: Here, ˜ F Depth is also under the supervision of depth map, using MSE loss, i.e., l d, 2 .
Revised: Here, ˜ F Depth is also under the supervision of depth map, using MSE loss, i.e., l d, 2 .

Original: Then, the overall training objective for the depth network and the depth-disentangled network is as:
Revised: Then, the overall training objective for the depth network and the depth-disentangled network is as:

Original: [Equation]
Revised: [Equation]

Original: where n denotes the n th sample in a mini-batch with N training samples; λ is set to 0.2 in the experiments to balance the consistency loss
Revised: where n denotes the n th sample in a mini-batch with N training samples; λ is set to 0.2 in the experiments to balance the consistency loss

Original: l con and other loss terms.
Revised: l con and other loss terms.

Original: As empirical evidences in
Revised: As empirical evidences in

Original: Fig.
Revised: Fig.

Original: 3 suggested, comparing to the raw depth map, D Sal is better at capturing discriminative cues of the salient objects, while D NonSal better depicts the complementary background context.
Revised: 3 suggested, comparing to the raw depth map, D Sal is better at capturing discriminative cues of the salient objects, while D NonSal better depicts the complementary background context.

Original: In the next step, the learned D Sal and D NonSal are fed into our Depth-disentangled Label Update, to obtain the improved pseudo-labels for training the saliency network.
Revised: In the next step, the learned D Sal and D NonSal are fed into our Depth-disentangled Label Update, to obtain the improved pseudo-labels.

Original: 3.2. D EPTH - DISENTANGLED L ABEL U PDATE
Revised: 3.2. D EPTH - DISENTANGLED L ABEL U PDATE

Original: To maintain more reliable supervision signals in training, a depth-disentangled label update (DLU) strategy is devised to iteratively reﬁne & update pseudo-labels.
Revised: To maintain more reliable supervision signals in training, a depth-disentangled label update (DLU) strategy is devised to iteratively reﬁne & update pseudo-labels.

Original: Speciﬁcally, as shown in the upper stream of Fig. 2, using the obtained Sal pred , D Sal and D NonSal , the DLU simultaneously highlights the salient regions in the coarse saliency prediction by the sum of Sal pred and D Sal , and suppresses the non-saliency negative responses by subtracting D NonSal in a pixel-wise manner.
Revised: Speciﬁcally, as shown in the upper stream of Fig. 2, using the obtained Sal pred , D Sal and D NonSal , the DLU simultaneously highlights the salient regions in the coarse saliency prediction by the sum of Sal pred and D Sal , and suppresses the non-saliency negative responses by subtracting D NonSal in a pixel-wise manner.

Original: This process can be formulated as:
Revised: This process can be formulated as:

Original: [Equation]
Revised: [Equation]

Original: To avoid the value overﬂow of the obtained S temp ( i.e. , removing negative numbers and normalizing the results to the range of [0, 1]), a thresholding operation and a normalization process are performed as:
Revised: To avoid the value overﬂow of the obtained S temp ( i.e. , removing negative numbers and normalizing the results to the range of [0, 1]), a thresholding operation and a normalization process are performed as:

Original: [Equation]
Revised: [Equation]

Original: where min ( · ) and max ( · ) denote the minimum and maximum functions.
Revised: where min ( · ) and max ( · ) denote the minimum and maximum functions.

Original: Finally, a fully-connected conditional random ﬁeld (CRF (Hou et al., 2017)) is applied to S N , to generate the enhanced saliency map S map as the updated pseudo-labels.
Revised: Finally, a fully-connected conditional random ﬁeld (CRF (Hou et al., 2017)) is applied to S N , to generate the enhanced saliency map S map as the updated pseudo-labels.

Original: The empirical result of the proposed DSU step is showcased in Fig.
Revised: The empirical result of the proposed DSU step is showcased in Fig.

Original: 3, with quantitative results presented in Table 5.
Revised: 3, with quantitative results presented in Table 4.

Original: These internal evidences suggest that the DSU leads to noticeable improvement after merely several iterations.3.2. D ISCUSSION [Table]
Revised: These internal evidences suggest that the DSU leads to noticeable improvement after merely several iterations.

Original: Is depth-disentanglement necessary?

Original: In our DSU, we disentangle the raw depth map into valuable salient parts as well as redundant non-salient areas to simultaneously highlight the salient regions and suppress the non-salient negative responses.

Original: To verify the necessity of depth disentanglement, we remove the D NonSal branch in the depth-disentangled network and only use the D Sal to update pseudo-labels in DLU.

Original: Empirical results in Table 1 reveal that removing D NonSal (row (a)) leads to signiﬁcant inferior performance when comparing with the original DSU (row (c)), which proves the superiority of our DSU design.

Original: How about using raw depth labels in DSU?

Original: We also consider to directly utilize the raw disentangled depth labels, i.e., D LabelSal and D LabelNonSal , to update pseudo-labels in DSU.

Original: However, a direct adoption of the raw depth labels does not lead to good performance, as empirically exhibited in Table 1 (b).

Original: The DSU using raw labels performs worse compared to our original DSU design in row (c).

Original: This is partly due to the large value variations and intrinsic label noises in raw depth map as discussed before.

Original: On the other hand, beneﬁting from the capability of deep-learning-based networks to learn general representations from large-scale data collections, using D Sal and D NonSal in our DSU strategy is able to eliminate the potential biases in a single raw depth label.

Original: Visual evidences as exhibited in the 4 th -7 th columns of Fig.

Original: 3 also support our claim.

Original: 3. A TTENTIVE T RAINING S TRATEGY
Revised: 3. A TTENTIVE T RAINING S TRATEGY

Original: When training the saliency network using pseudo-labels, an attentive training strategy (ATS) is proposed to tailor for the deep unsupervised learning context, to reduce the inﬂuence of ambiguous pseudo-labels, and concentrate on the more reliable training examples.
Revised: When training the saliency network using pseudo-labels, an attentive training strategy (ATS) is proposed to tailor for the deep unsupervised learning context, to reduce the inﬂuence of ambiguous pseudo-labels, and concentrate on the more reliable training examples.

Original: This strategy is inspired by the human learning process of understanding new knowledge, that is, from general to speciﬁc understanding cycle (Dixon, 1999; Peltier et al., 2005).
Revised: This strategy is inspired by the human learning process of understanding new knowledge, that is, from general to speciﬁc understanding cycle (Dixon, 1999; Peltier et al., 2005).

Original: The ATS alternates between two steps to re-weigh the training instances in a mini-batch.
Revised: The ATS alternates between two steps to re-weigh the training instances in a mini-batch.

Original: To be speciﬁc, we ﬁrst start by settling the related loss functions.
Revised: To be speciﬁc, we ﬁrst start by settling the related loss functions.

Original: For the n th sample in a mini-batch with N training samples, we deﬁne the binary cross-entropy loss between the predicted saliency
Revised: For the n th sample in a mini-batch with N training samples, we deﬁne the binary cross-entropy loss between the predicted saliency

Original: Sal npred and the pseudo-label S nmap as:
Revised: Sal npred and the pseudo-label S nmap as:

Original: [Equation]
Revised: [Equation]

Original: Then, the training objective for the saliency network in current mini-batch is deﬁned as an attentive binary cross-entropy loss L sal , which can be represented as follows:
Revised: Then, the training objective for the saliency network in current mini-batch is deﬁned as an attentive binary cross-entropy loss L sal , which can be represented as follows:

Original: [Equation]
Revised: [Equation]

Original: where α n represents the weight of the n th training sample at current training mini-batch.
Revised: where α n represents the weight of the n th training sample at current training mini-batch.

Original: [Figure]
Revised: [Figure]

Original: The ATS starts from a uniform weight for each training sample in step one to learn general representations across a lot of training data.
Revised: The ATS starts from a uniform weight for each training sample in step one to learn general representations across a lot of training data.

Original: Step two decreases the importance of ambiguous training instances through the imposed attentive loss; the higher the loss value, the less weight an instance is to get.
Revised: Step two decreases the importance of ambiguous training instances through the imposed attentive loss; the higher the loss value, the less weight an instance is to get.

Original: In this paper
Revised: In this paper

Original: [Figure]
Revised: [Figure]

Original: we deﬁne step one and two as a training round ( 2 τ epochs).
Revised: we deﬁne step one and two as a training round ( 2 τ epochs).

Original: During each training round, the saliency loss l sal and depth loss l depth are optimized simultaneously to train their network parameters.
Revised: During each training round, the saliency loss L sal and depth loss L depth are optimized simultaneously to train their network parameters.

Original: The proposed DLU is taken at the end of each training round to update the pseudo-labels for the saliency network; meanwhile, D LabelSal and D LabelNonSal in Eqs.1 andare also updated using the improved Sal pred .
Revised: The proposed DLU is taken at the end of each training round to update the pseudo-labels for the saliency network; meanwhile, D LabelSal and D LabelNonSal in Eqs.1 andare also updated using the improved Sal pred .

Original: This allows the network to make efﬁcient use of the updated pseudo labels.
Revised: This allows the network to make efﬁcient use of the updated pseudo labels.

Original: As suggested by Fig.
Revised: As suggested by Fig.

Original: 4 and Table 4, the use of our attentive training strategy leads to more signiﬁcant error reduction compared with the backbone solely using uniform weight or attentive weight for each instance.
Revised: 4 and Table 3, the use of our attentive training strategy leads to more signiﬁcant error reduction compared with the backbone solely using uniform weight or attentive weight for each instance.

Original: Besides, when our DSU and the attentive training strategy are both incorporated to the backbone, better results are achieved.
Revised: Besides, when our DSU and the attentive training strategy are both incorporated to the backbone, better results are achieved.

Original:  E XPERIMENTS AND A NALYSES 4. D ATASETS AND E VALUATION M ETRICS
Revised:  E XPERIMENTS AND A NALYSES 4. D ATASETS AND E VALUATION M ETRICS

Original: Extensive experiments are conducted over four large-scale RGB-D SOD benchmarks.
Revised: Extensive experiments are conducted over four large-scale RGB-D SOD benchmarks.

Original: NJUD (Ju et al., 2014) in its latest version consists of 1,985 samples, that are collected from the Internet and 3D movies; NLPR (Peng et al., 2014) has 1,000 stereo images collected with Microsoft Kinect;
Revised: NJUD (Ju et al., 2014) in its latest version consists of 1,985 samples, that are collected from the Internet and 3D movies; NLPR (Peng et al., 2014) has 1,000 stereo images collected with Microsoft Kinect;

Original: STERE (Niu et al., 2012) contains 1,000 pairs of binocular images downloaded from the Internet;
Revised: STERE (Niu et al., 2012) contains 1,000 pairs of binocular images downloaded from the Internet;

Original: DUTLF-Depth (Piao et al., 2019) has 1,200 real scene images captured by a Lytro2 camera.
Revised: DUTLF-Depth (Piao et al., 2019) has 1,200 real scene images captured by a Lytro2 camera.

Original: We follow the setup of (Fan et al., 2020a) to construct the training set, which includes 1,485 samples from NJUD and 700 samples from NLPR, respectively.
Revised: We follow the setup of (Fan et al., 2020a) to construct the training set, which includes 1,485 samples from NJUD and 700 samples from NLPR, respectively.

Original: Data augmentation is also performed by randomly rotating, cropping and ﬂipping the training images to avoid potential overﬁtting.
Revised: Data augmentation is also performed by randomly rotating, cropping and ﬂipping the training images to avoid potential overﬁtting.

Original: The remaining images are reserved for testing.
Revised: The remaining images are reserved for testing.

Original: Here, ﬁve widely-used evaluation metrics are adopted: E-measure ( E ξ )
Revised: Here, ﬁve widely-used evaluation metrics are adopted: E-measure ( E ξ )

Original: (Fan et al., 2018), weighed
Revised: (Fan et al., 2018), weighed

Original: F-measure ( F w β ) (Margolin et al., 2014), F-measure ( F β ) (Achanta et al., 2009), Mean Absolute
Revised: F-measure ( F wβ ) (Margolin et al., 2014), F-measure ( F β ) (Achanta et al., 2009), Mean Absolute

Original: Error (MAE or M ) (Borji et al., 2015), and inference time(s) or FPS (Frames Per Second).
Revised: Error (MAE or M ) (Borji et al., 2015), and inference time(s) or FPS (Frames Per Second).

Original: Implementation details are presented in the Appx A.3.
Revised: Implementation details are presented in the Appx A.3.

Revised: DUT: 0107 ; 0718; 1377; NJUD: 001144_left; NLPR: 2_07-11-17; 9_12-47-12 ; 10_03-27-39 ; 10_12-31-

Original: [Table]
Revised: [Figure]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure][Table] [Table] [Figure]
Revised: [Figure]

Original: [Table][Figure] [Table] [Figure]
Revised: [Table]

Original: [Table]

Original: [Figure][Table] [Figure] [Table]
Revised: [Figure]

Original: c

Original: [Table]
Revised: [Table]

Original: a f t

Original: [Table]
Revised: [Figure]

Original: U

Original: [Table]
Revised: [Table]

Original: 4. C OMPARISON WITH THE S TATE - OF - THE - ARTS
Revised: 4. C OMPARISON WITH THE S TATE - OF - THE - ARTS

Original: Our approach is compared with 15 unsupervised SOD methods,
Revised: Our approach is compared with 15 unsupervised SOD methods,

Original: i
Revised: i

Original: [Table]
Revised: [Table]

Original: e. , without using any human annotations.
Revised: e. , without using any human annotations.

Original: Their results are either directly furnished by the authors of the respective papers
Revised: Their results are either directly furnished by the authors of the respective papers

Original: [Table]
Revised: [Table]

Original: or generated by re-running their original implementations.
Revised: or generated by re-running their original implementations.

Original: In this paper [Table] we make the ﬁrst attempt to address deep-learning-based unsupervised RGB-D SOD.
Revised: In this paper, we make the ﬁrst attempt to address deep-learning-based unsupervised RGB-D SOD.

Original: Since existing unsupervised RGB-D methods are all based on handcrafted feature representations, we additionally provide several RGB-based methods ( e.g. , USD and DeepUSPS) for reference purpose.
Revised: Since existing unsupervised RGB-D methods are all based on handcrafted feature representations, we additionally provide several RGB-based methods ( e.g. , USD and DeepUSPS) for reference purpose.

Original: This gives more observational evidences for the related works.
Revised: This gives more observational evidences for the related works.

Original: The RGB-based methods are speciﬁcally marked by † in Table 2.
Revised: These RGB-based methods are speciﬁcally marked by † in Table 1.

Original: Quantitative results are listed in Table 2, where our approach clearly outperforms the state-of-theart unsupervised SOD methods in both RGB-D and RGB only scenarios.
Revised: Quantitative results are listed in Table 1, where our approach clearly outperforms the state-of-theart unsupervised SOD methods in both RGB-D and RGB only scenarios.

Original: This is due to our DSU framework that leads to trustworthy supervision signals for saliency network.
Revised: This is due to our DSU framework that leads to trustworthy supervision signals for saliency network.

Original: Furthermore, our network design leads to a light-weight architecture in the inference stage, shown as the black dashed portion in Fig.
Revised: Furthermore, our network design leads to a light-weight architecture in the inference stage, shown as the black dashed portion in Fig.

Original: This enables efﬁcient & effective detection of salient objects and brings a largemargin improvement over the backbone network without introducing additional depth input and computational costs, as shown in Fig.
Revised: This enables efﬁcient & effective detection of salient objects and brings a largemargin improvement over the backbone network without introducing additional depth input and computational costs, as shown in Fig.

Original: 1 and Table 2.
Revised: 1 and Table 1.

Original: Qualitatively, saliency predictions of competing methods are exhibited in Fig.
Revised: Qualitatively, saliency predictions of competing methods are exhibited in Fig.

Original: These results consistently proves the superiority of our method.
Revised: These results consistently proves the superiority of our method.

Original: 4. A NALYSIS OF THE E MPIRICAL R ESULTS
Revised: 4. A NALYSIS OF THE E MPIRICAL R ESULTS

Original: The focus here is on the evaluation of the contributions from each of the components, and the evaluation of the obtained pseudo-labels as intermediate results.
Revised: The focus here is on the evaluation of the contributions from each of the components, and the evaluation of the obtained pseudo-labels as intermediate results.

Original: Effect of each component.
Revised: Effect of each component.

Original: In Table 3, we conduct ablation study to investigate the contribution of each component.
Revised: In Table 2, we conduct ablation study to investigate the contribution of each component.

Original: To start with, we consider the backbone (a), where the saliency network is trained with initial pseudo-labels.
Revised: To start with, we consider the backbone (a), where the saliency network is trained with initial pseudo-labels.

Original: As our proposed ATS and DSU are gradually incorporated, increased performance has been observed on both datasets.
Revised: As our proposed ATS and DSU are gradually incorporated, increased performance has been observed on both datasets.

Original: Here we ﬁrst investigate the beneﬁts of ATS by applying it to the backbone and obtaining (b).
Revised: Here we ﬁrst investigate the beneﬁts of ATS by applying it to the backbone and obtaining (b).

Original: We observe increased F-measure scores of 3% and 5.8% on NJUD and NLPR benchmarks, respectively.
Revised: We observe increased F-measure scores of 3% and 5.8% on NJUD and NLPR benchmarks, respectively.

Original: This clearly shows that the proposed ATS can effectively improve the utilization of reliable pseudo-labels, by re-weighting ambiguous training data.
Revised: This clearly shows that the proposed ATS can effectively improve the utilization of reliable pseudo-labels, by re-weighting ambiguous training data.

Original: We then investigate in detail all components in our DSU strategy.
Revised: We then investigate in detail all components in our DSU strategy.

Original: The addition of the entire
Revised: The addition of the entire

Original: DSU leads to (f), which signiﬁcantly improves the F-measure metric by 11.3% and 23.5% on each of the benchmarks, while reducing the MAE by 22.4% and 41.9%, respectively.
Revised: DSU leads to (f), which signiﬁcantly improves the F-measure metric by 11.3% and 23.5% on each of the benchmarks, while reducing the MAE by 22.4% and 41.9%, respectively.

Original: This veriﬁes the effectiveness of the DSU strategy to reﬁne and update pseudo-labels.
Revised: This veriﬁes the effectiveness of the DSU strategy to reﬁne and update pseudo-labels.

Original: Moreover, as we gradually exclude the consistency loss l con (row (e)) and HA operation (row (d)), degraded performances are observed on both datasets.
Revised: Moreover, as we gradually exclude the consistency loss l con (row (e)) and HA operation (row (d)), degraded performances are observed on both datasets.

Original: For an extreme case where we remove the DLU and only maintain
Revised: For an extreme case where we remove the DLU and only maintain

Original: CRF to reﬁne pseudo-labels, it is observed that much worse performance is achieved.
Revised: CRF to reﬁne pseudo-labels, it is observed that much worse performance is achieved.

Original: These results consistently demonstrate that all components in the DSU strategy are beneﬁcial for generating more accurate pseudo-labels.
Revised: These results consistently demonstrate that all components in the DSU strategy are beneﬁcial for generating more accurate pseudo-labels.

Original: [Table]
Revised: [Table]

Original: We also display the visual evidence of the updated pseudolabels obtained from the DSU strategy in Fig.
Revised: We also display the visual evidence of the updated pseudolabels obtained from the DSU strategy in Fig.

Original: It is shown that the initial pseudo-labels unfortunately tend to miss important parts as well as ﬁne-grained details.
Revised: It is shown that the initial pseudo-labels unfortunately tend to miss important parts as well as ﬁne-grained details.

Original: The application of CRF helps to ﬁlter away background noises, while salient parts could still be missing out.
Revised: The application of CRF helps to ﬁlter away background noises, while salient parts could still be missing out.

Original: By adopting our DSU and attentive training strategy, the missing parts could be retrieved in the resulting pseudolabels, with the object silhouette also being reﬁned.
Revised: By adopting our DSU and attentive training strategy, the missing parts could be retrieved in the updated pseudo-labels, with the object silhouette also being reﬁned.

Original: These numerical and visual results consistently verify the effectiveness of our pipeline in deep unsupervised RGB-D saliency detection.
Revised: These numerical and visual results consistently verify the effectiveness of our pipeline in deep unsupervised RGB-D saliency detection.

Original: [Table]
Revised: [Table]

Original: Detailed analysis of the ATS.
Revised: Detailed analysis of the ATS.

Original: The inﬂuence of the attentive training strategy (ATS) is studied in detail here.
Revised: The inﬂuence of the attentive training strategy (ATS) is studied in detail here.

Original: In Fig.
Revised: The error reduction curves in Fig.

Original: 4, we have observed that the use of ATS could lead to greater test error reduction compared with the backbone that uses uniform weight or attentive weight for each instance.
Revised: 4 have shown that the use of ATS could lead to greater test error reduction compared with the backbone that uses uniform weight or attentive weight for each instance.

Original: When combining the ATS with the DSU strategy, as shown in‘Setting 1’, ‘Setting 2’ and ‘Ours’ in Table 4, the ATS also enables the network to learn more efﬁciently than using uniform weight or attentive weight alone.
Revised: When combining the ATS with the DSU strategy as shown in Table 3, the ATS also enables the network to learn more efﬁciently than using uniform weight or attentive weight alone, by comparing ‘Ours’ with ‘Setting 1’ and ‘Setting 2’.

Original: In addition, we discuss the effect of different alternation intervals τ in ATS.
Revised: In addition, we discuss the effect of different alternation intervals τ in ATS.

Original: As listed in Table 4, the larger or smaller interval leads to inferior performance due to the insufﬁcient or excessive learning of saliency models.
Revised: As listed in Table 3, the larger or smaller interval leads to inferior performance due to the insufﬁcient or excessive learning of saliency models

Original: [Table] [Table] 
Revised: [Table] [Table] [Table]

Original: [Figure]
Revised: [Figure]

Original: Analysis of pseudo-labels.
Revised: Analysis of pseudo-labels.

Original: We analyze the quality of pseudo-labels over the training process in Table 5, where the mean absolute error scores between the pseudo-labels at different update rounds and the ground-truth labels are reported.
Revised: We analyze the quality of pseudo-labels over the training process in Table 4, where the mean absolute error scores between the pseudo-labels at different update rounds and the ground-truth labels are reported.

Original: It is observed that the quality of pseudo-labels is signiﬁcantly improved during the ﬁrst two rounds, which then remains stable in the consecutive rounds.
Revised: It is observed that the quality of pseudo-labels is signiﬁcantly improved during the ﬁrst two rounds, which then remains stable in the consecutive rounds.

Original: Fig.also shows that the initial pseudo-label is effectively reﬁned, where the updated pseudo-label is close to the true label.
Revised: Fig.also shows that the initial pseudo-label is effectively reﬁned, where the updated pseudo-label is close to the true label.

Original: This provides more reliable guiding signals for training the saliency network.
Revised: This provides more reliable guiding signals for training the saliency network

Revised: [Table]

Original: Comparison of different pseudo-label variants.
Revised: Comparison of different pseudo-label variants.

Original: In Table 6, we investigate other possible pseudolabel generation variants, including, initial pseudo-label with CRF reﬁnement, raw depth map, and raw depth map together with OTSU thresholding (Otsu, 1979).
Revised: In Table 5, we investigate other possible pseudolabel generation variants, including, initial pseudo-label with CRF reﬁnement, raw depth map, and raw depth map together with OTSU thresholding (Otsu, 1979).

Original: It is shown that, compared with the direct use of CRF, our proposed DSU is able to provide more reliable pseudo-labels, by disentangling depth to promote saliency.
Revised: It is shown that, compared with the direct use of CRF, our proposed DSU is able to provide more reliable pseudo-labels, by disentangling depth to promote saliency.

Original: It is worth noting that a direct application of the raw depth map or together with an OTSU adaptive thresholding of the depth map, may nevertheless lead to awful results.
Revised: It is worth noting that a direct application of the raw depth map or together with an OTSU adaptive thresholding of the depth map, may nevertheless lead to awful results.

Original: We conjecture this is because of the large variations embedded in raw depth, and the fact that foreground objects may be affected by nearby background stuffs that are close in depth. 
Revised: We conjecture this is because of the large variations embedded in raw depth, and the fact that foreground objects may be affected by nearby background stuffs that are close in depth. [Table]

Revised: Is depth-disentanglement necessary?

Revised: In our DSU, we disentangle the raw depth map into valuable salient parts as well as redundant non-salient areas to simultaneously highlight the salient regions and suppress the non-salient negative responses.

Original: 4. A PPLICATION TO F ULLY - SUPERVISED S ETTING
Revised: To verify the necessity of depth disentanglement, we remove the D NonSal branch in the depth-disentangled network and only use the D Sal to update pseudo-labels in DLU. Empirical results in Table 6 reveal that removing D NonSal (row (a)) leads to signiﬁcant inferior performance when comparing with the original DSU (row (c)), which proves the superiority of our DSU design. How about using raw depth labels in DSU?

Revised: We also consider to directly utilize the raw disentangled depth labels, i.e., D LabelSal and D LabelNonSal , to update pseudo-labels in DSU.

Revised: However, a direct adoption of the raw depth labels does not lead to good performance, as empirically exhibited in

Revised: Table 6 (b).

Revised: The DSU using raw labels performs worse compared to our original DSU design in row (c).

Revised: This is partly due to the large value variations and intrinsic label noises in raw depth map as discussed before.

Revised: On the other hand, beneﬁting from the capability of deep-learning-based networks to learn general representations from large-scale data collections, using D Sal and D NonSal in our

Revised: DSU strategy is able to eliminate the potential biases in a single raw depth label.

Revised: Visual evidences as exhibited in the 4 th -7 th columns of Fig.

Revised: 3 also support our claim.

Revised: 4. A PPLICATION TO F ULLY - SUPERVISED S ETTING

Original: [Table]
Revised: [Table]

Original: To show the generic applicability of our approach, a variant of our DSU is applied to several cuttingedge fully-supervised RGB-D models to improve their performance.
Revised: To show the generic applicability of our approach, a variant of our DSU is applied to several cuttingedge fully-supervised RGB-D models to improve their performance.

Original: This is made possible by redeﬁning the quantities D LabelSal = S GT ⊗ D map and
Revised: This is made possible by redeﬁning the quantities D LabelSal = S GT ⊗ D map and

Original: D LabelNonSal =
Revised: D LabelNonSal =

Original: (1 − S GT ) ⊗ D map , with S GT being the ground-truth saliency.
Revised: (1 − S GT ) ⊗ D map , with S GT being the ground-truth saliency.

Original: Then the saliency network ( i.e., existing RGB-D models) and the depthdisentangled network are trained by S GT and the new D LabelSal and D LabelNonSal , respectively.
Revised: Then the saliency network ( i.e., existing RGB-D models) and the depthdisentangled network are trained by S GT and the new D Label Sal and D Label NonSal , respectively.

Original: After training, the proposed DSU is engaged to obtain the ﬁnal improved saliency.
Revised: After training, the proposed DLU is engaged to obtain the ﬁnal improved saliency.

Original: In Table 7, we report the original results of four RGB-D methods and the new results of incorporating our DSU strategy on two popular benchmarks.
Revised: In Table 7, we report the original results of four RGB-D methods and the new results of incorporating our DSU strategy on two popular benchmarks.

Original: It is observed that our supervised variants have consistent performance improvement comparing to each of existing models.
Revised: It is observed that our supervised variants have consistent performance improvement comparing to each of existing models.

Original: For example, the average MAE scores of four
Revised: For example, the average MAE scores of four

Original: RGB-D methods on NJUD and NLPR benchmarks are reduced by 13.9% and 16.6%, respectively.
Revised: RGB-D methods on NJUD and NLPR benchmarks are reduced by 13.9% and 16.6%, respectively.

Original: We attribute the performance improvement to our DSU strategy that can exploit the learned D Sal to facilitate the localization of salient object regions in a scene, as well as suppress the redundant background noises by subtracting D NonSal .
Revised: We attribute the performance improvement to our DSU strategy that can exploit the learned D Sal to facilitate the localization of salient object regions in a scene, as well as suppress the redundant background noises by subtracting D NonSal .

Original: More experiments are presented in the appendix.
Revised: More experiments are presented in the Appx A.2.

Original:  C ONCLUSION AND O UTLOOK
Revised:  C ONCLUSION AND O UTLOOK

Original: This paper tackles the problem of deep unsupervised RGB-D saliency detection, which is the ﬁrst such effort to our knowledge.
Revised: This paper tackles the problem of deep unsupervised RGB-D saliency detection, which is the ﬁrst such effort to our knowledge.

Original: Our key insight is to internally engage the pseudo-labels, consolidated by two critical ingredients, namely depth-disentangled saliency update in iteratively ﬁne-tuning the pseudo-labels & attentive training strategy in alleviating the issue of noisy pseudo-labels.
Revised: Our key insight is to internally engage the pseudo-labels, consolidated by two critical ingredients, namely depth-disentangled saliency update in iteratively ﬁne-tuning the pseudo-labels & attentive training strategy in alleviating the issue of noisy pseudo-labels.

Original: Extensive empirical experiments demonstrate the superior performance and realtime efﬁciency of our approach.
Revised: Extensive empirical experiments demonstrate the superior performance and realtime efﬁciency of our approach.

Original: For future work, our plan is to explore the scenarios involving partial labels.
Revised: For future work, our plan is to explore the scenarios involving partial labels.

Revised: Ethics Statement.

Revised: This work does not involve any negative societal and ethical impacts, and we believe the most proximate impacts of this work will be positive.

Original: A A PPENDIX
Revised: In particular, class-agnostic unsupervised segmentation problems like RGB-D salient object detection problem present a major obstacle to the requirement of a signiﬁcant amount of laborious pixel-level human annotations. The proposed Depth-disentangled Saliency Update framework can produce more reliable supervision signal for training saliency network, which exhibits an appealing prospect and tradeoff between model accuracy and human consumption. Furthermore, our method achieves fast inference speed and good generic applicability, which is useful and appealing for light-weight deployment in practice. Reproducibility Statement.

Revised: As for social responsibility, the project team member will responsible for the quality of the project, like no cheating, being honest and academic integrity.

Revised: We also promise that our source code with easy-to-use instruction will be publicly available upon paper acceptance

Original: In this appendix, we ﬁrst elaborate on the evaluation metrics used in this paper, in Appx A.1.
Revised: In this appendix, we ﬁrst elaborate on the evaluation metrics used in this paper, in Appx A.1.

Original: Then, more details and experiments of our DSU are presented in Appx A.2, including the detailed structures of D-Sal and D-NonSal CNNs, more experiments on unsupervised and fully-supervised scenarios, and other pseudo- label initialization.
Revised: Then, more details and experiments of our DSU are presented in Appx A.2, including the detailed structures of D-Sal and D-NonSal CNNs, more experiments on unsupervised and fully-supervised scenarios, and other pseudo- label initialization.

Original: These empirical results consistently demonstrate the effectiveness and scalability of our approach.
Revised: These empirical results consistently demonstrate the effectiveness and scalability of our approach.

Original: In Appx A.3, we illustrate on the implementation details of our unsupervised pipeline.
Revised: In Appx A.3, we illustrate on the implementation details of our unsupervised pipeline.

Original: Finally, in Appx A.4, we discuss the potential limitations which can be addressed in the near future.
Revised: Finally, in Appx A.4, we discuss the potential limitations which can be addressed in the near future.

Original: A. E VALUATION M ETRICS
Revised: A. E VALUATION M ETRICS

Original: We adopt four widely-used metrics in SOD to evaluate the performance of saliency models, i.e. , E-measure, F-measure, weighted F-measure and MAE.
Revised: We adopt four widely-used metrics in SOD to evaluate the performance of saliency models, i.e. , E-measure, F-measure, weighted F-measure and MAE.

Original: The lower the MAE, the better.
Revised: The lower the MAE, the better.

Original: For other metrics, the higher score is better.
Revised: For other metrics, the higher score is better.

Original: Concretely, F-measure is an overall performance measurement and is computed by the weighted harmonic mean of the precision and recall:
Revised: Concretely, F-measure is an overall performance measurement and is computed by the weighted harmonic mean of the precision and recall:

Original: [Equation]
Revised: [Equation]

Original: where β 2 is set to 0.3 to emphasize the precision (Achanta et al., 2009).
Revised: where β 2 is set to 0.3 to emphasize the precision (Achanta et al., 2009).

Original: We use different ﬁxed [0, 255] thresholds to compute the F-measure metric.
Revised: We use different ﬁxed [0, 255] thresholds to compute the F-measure metric.

Original: Weighted F-measure ( F w β ) (Margolin et al., 2014) is its weighted measurement.
Revised: Weighted F-measure ( F w β ) (Margolin et al., 2014) is its weighted measurement.

Original: MAE ( M ) (Borji et al., 2015) represents the average absolute difference between the predicted saliency map and ground truth.
Revised: MAE ( M ) (Borji et al., 2015) represents the average absolute difference between the predicted saliency map and ground truth.

Original: It is used to calculate how similar a normalized saliency maps S ∈ [0 , 1] W × H is compared to the ground truth G ∈ [0 , 1] W × H :
Revised: It is used to calculate how similar a normalized saliency maps S ∈ [0 , 1] W × H is compared to the ground truth G ∈ [0 , 1] W × H :

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: where W and H denote the width and height of S , respectively.
Revised: where W and H denote the width and height of S , respectively.

Original: Enhanced-alignment measure ( E ξ )
Revised: Enhanced-alignment measure ( E ξ )

Original: (Fan et al., 2018) is proposed based on cognitive vision studies to capture image-level statistics and their local pixel matching information, as in
Revised: (Fan et al., 2018) is proposed based on cognitive vision studies to capture image-level statistics and their local pixel matching information, as in

Original: E [Equation] =
Revised: E ξ =

Original: W [Equation]
Revised: W × H

Original:  (cid:88) i 
Revised: W (cid:88) i =

Original: [Equation]

Original: H (cid:88)
Revised: H (cid:88)

Original: j

Original: [Equation]

Original:  φ [Equation] [Equation] , (11) where φ s ( · ) is the enhanced-alignment matrix (Fan et al., 2018), which reﬂects the correlation between S and G after subtracting their global means.
Revised: j = φ s ( i, j ) , (11) where φ s ( · ) is the enhanced-alignment matrix (Fan et al., 2018), which reﬂects the correlation between S and G after subtracting their global means.

Original: A. M ORE A NALYSES ON THE PROPOSED DSU
Revised: A. M ORE A NALYSES ON THE PROPOSED DSU

Original: Detailed structure on DSU.
Revised: Detailed structures.

Original: The detailed structures of the D-Sal CNN and D-NonSal CNN are illustrated in Fig.
Revised: The detailed structures of the D-Sal CNN and D-NonSal CNN are illustrated in Fig.

Revised: The ‘Conv 3 × 3’ represents a convolutional layer with the kernel size of 3.

Original: Visual results for unsupervised RGB-D SOD models.
Revised: Visual results for unsupervised RGB-D SOD models.

Original: More visual comparisons with unsupervised RGB-D saliency models are shown in Fig.
Revised: More visual comparisons with unsupervised RGB-D saliency models are shown in Fig.

Original: 8, where our unsupervised pipeline generates promising saliency prediction that is close to true label.
Revised: 8, where our unsupervised pipeline generates promising saliency prediction that is close to true label.

Original: 2021.01.15 for overall
Revised: 2021.01.15 for overall

Original: NJUD:
Revised: NJUD:

Original: Saliency Network
Revised: Saliency Network

Original: Depth Network
Revised: Depth Network

Original: Depth Map RGB Image !"
Revised: Depth Map RGB Image !"

Original: #$ %×%
Revised: #$ %×%

Original: Consistency Loss
Revised: Consistency Loss

Original: Updated
Revised: Updated

Original: Pseudo Label ! !
Revised: Pseudo Label ! !

Original: ⨁ Element-wise addition Element-wise subtraction ⊝ ⨁
Revised: ⨁ Element-wise addition Element-wise subtraction ⊝ ⨁

Original: Depth-disentangled Label Update
Revised: Depth-disentangled Label Update

Original: T h re s h
Revised: T h re s h

Original: o l d
Revised: o l d

Original: CR F
Revised: CR F

Original: Initial
Revised: Initial

Original: Update
Revised: Update

Original: Update · · · ⨁ ⊝ " "#$ !"
Revised: Update · · · ⨁ ⊝ " "#$ !"

Original: D-Sal
Revised: D-Sal

Original: CNN
Revised: CNN

Original: D-NonSal CNN $ +,-.)'/ $ 123+,-.)'/ " %&'()
Revised: D-NonSal CNN $ +,-.)'/ $ 123+,-.)'/ " %&'()

Original: ~ # *+, # -./*+, ! !
Revised: ~ # *+, # -./*+, ! !

Original: # ! $%&!"
Revised: # ! $%&!"

Original: # "#$ '()*
Revised: # "#$ '()*

Original: [Table]
Revised: [Table]

Original: 4 ×6+ 9 : ; < 0 1 24 ×6+ 9 : ; < 0 1 24 ×6+ 9 : ; < 0 1 24 ×6+ 9 : ; < 0 1 24 ×6+ 9 : ; < 0 1 24 ×6+ 9 : ; < !!!"

Original: #$ %×%
Revised: #$ %×%

Original: Depth Map !"
Revised: Depth Map !"

Original: HA + _ ⨂ ⨂ # *+,=+>&, # -./*+, =+>&, !
Revised: HA + _ ⨂ ⨂ # *+,=+>&, # -./*+, =+>&, !

Original: :,< ! :,= ! >
Revised: :,< ! :,= ! >

Original: Comparison with the state-of-the-art methods under fully-supervised scenario.

Original: As discussed in Sec.
Revised: As discussed in Sec.

Original: 4.4 of the main text, our method can boost the performance of existing SOD models greatly, by applying DSU to existing methods.
Revised: 4.4 of the main text, our method can boost the performance of existing SOD models greatly, by applying DSU to existing meth- ods.

Original: In this sec- tion, we further compare our fully-supervised variant of DSU with 25 existing state-of-the-art RGB- D saliency models.
Revised: In this section, we further compare our fullysupervised variant of DSU with 25 existing state-of- the-art RGB-D saliency models.

Original: We adopt the same featureextraction baseline as in BBSNet (Fan et al., 2020b) and DCF (Ji et al., 2021) to build the saliency network in the DSU framework.
Revised: We adopt the same feature-extraction baseline as in BBSNet (Fan et al., 2020b) and DCF (Ji et al., 2021) to build the saliency network in the DSU framework.

Original: Numerical results in Table 8 and visual results in Fig.
Revised: Numerical results in Table 8 and visual results in Fig.

Original: 9 consistently show the superiority and scalability of our method.
Revised: 9 consistently show the superiority and scalability of our method.

Original: Pseudo-label Initialization.
Revised: Pseudo-label Initialization.

Original: We investigate other initial pseudo-labels ( e.g. , DCMC (Cong et al., 2016)), to further verify the effectiveness of our pro- posed method.
Revised: We investigate other initial pseudo-labels ( e.g. , DCMC (Cong et al., 2016)), to further verify the effectiveness of our pro- posed method.

Original: The superior performance is also observed, by comparing backbone with ours: 0.167 vs. 0.
Revised: The superior performance is also observed, by comparing backbone with ours: 0.167 vs. 0.

Original: on NJUD, 0.119 vs. 0.084 on NLPR, 0.142 vs. 0.119 on STERE and 0.146 vs. 0.128 on DUTLF-Depth, using MAE metric.
Revised: on NJUD, 0.119 vs. 0.084 on NLPR, 0.142 vs. 0.119 on STERE and 0.146 vs

Original: A. I MPLEMENTATION D ETAILS
Revised: A. I MPLEMENTATION D ETAILS

Original: The proposed deep unsupervised pipeline is implemented with PyTorch and trained using a single Tesla P40 GPU.
Revised: The proposed deep unsupervised pipeline is implemented with PyTorch and trained using a single Tesla P40 GPU.

Original: Both Saliency and Depth Networks employ the same feature extraction backbone (DeepLabv3+)
Revised: Both Saliency and Depth Networks employ the same feature extraction backbone (DeepLabv3+)

Original: (Chen et al., 2018), which is equipped with ResNet-50 (He et al., 2016), with initial parameters pre-trained in Ima- geNet (Krizhevsky et al., 2012).
Revised: (Chen et al., 2018), which is equipped with ResNet-50 (He et al., 2016), with initial parameters pre-trained in Ima- geNet (Krizhevsky et al., 2012).

Original: All training & testing images are uniformly resized to the size of 352 × 352 .
Revised: All training & testing images are uniformly resized to the size of 352 × 352 .

Original: Throughout training, the learning rate is set to 1 × 10 − 4 , and the Adam optimizer is used with a mini-batch size of 10.
Revised: Throughout training, the learning rate is set to 1 × 10 − 4 , and the Adam optimizer is used with a mini-batch size of 10.

Original: Our approach is trained in an unsupervised manner, i.e. , without any human annotations, where initial pseudo-labels are the outputs of handcrafted RGB-D saliency method CDCP (Zhu et al., 2017b).
Revised: Our approach is trained in an unsupervised manner, i.e. , without any human annotations, where initial pseudo-labels are the outputs of handcrafted RGB-D saliency method CDCP (Zhu et al., 2017b).

Original: As for the proposed attentive training strategy, its alternation interval is set to 3, amounting to 2 τ = 6 epochs in a training round.
Revised: As for the proposed attentive training strategy, its alternation interval is set to 3, amounting to 2 τ = 6 epochs in a training round.

Original: During inference, our approach directly predicts saliency maps based on an RGB image, without access to any depth map.
Revised: During inference, our approach directly predicts saliency maps based on an RGB image, without access to any depth map.

Original: A. L IMITATION AND D ISCUSSION
Revised: A. L IMITATION AND D ISCUSSION

Original: As demonstrated in this paper, our deep unsupervised RGB-D saliency detection approach achieves an appealing trade-off between detection accuracy and human consumption.
Revised: As demonstrated in this paper, our deep unsupervised RGB-D saliency detection approach achieves an appealing trade-off between detection accuracy and human consumption.

Original: However, due to the lack of accurate pixel-level annotations, the model still fails to comprehensively detect the ﬁne-grained details, i.e. , the edges of salient objects.
Revised: However, due to the lack of accurate pixel-level annotations, the model still fails to comprehensively detect the ﬁne-grained details, i.e. , the edges of salient objects.

Original: To tackle this challenge, a doable solution is to introduce auxiliary edge constraint.
Revised: To tackle this challenge, a doable solution is to introduce auxiliary edge constraint.

Original: For example, the edge detection loss can be employed to low-level features of the model, which forces  model to produce discriminative features highlighting object details (Zhang et al., 2020b; Liu et al., 2019).
Revised: For example, the edge detection loss can be employed to low-level features of the model, which forces the model to produce discriminative features highlighting object details (Zhang et al., 2020b; Liu et al., 2019).

Original: The edge maps can be generated by classical Canny operator (Canny, 1986) in an unsupervised manner.
Revised: The edge maps can be generated by classical Canny operator (Canny, 1986) in an unsupervised manner.

Original: Hopefully this could encourage more inspirations and contributions to this community and further pave the way for its booming future.
Revised: Hopefully this could encourage more inspirations and contributions to this community and further pave the way for its booming future.

Original: [Table]
Revised: [Table]

Original: Ours

Original: [Figure]
Revised: [Figure]

Original: [Table][Figure] [Table] [Figure]
Revised: [Table]

Original: [Table] [Figure] [Table] [Figure]
Revised: [Figure]