Original: Semi-Supervised Video Salient Object Detection Based on Uncertainty-Guided Pseudo Labels
Revised: Semi-Supervised Video Salient Object Detection Based on Uncertainty-Guided Pseudo Labels

Original: Abstract
Revised: Abstract

Original: Semi-Supervised Video Salient Object Detection (SS-VSOD) is challenging because of the lack of temporal information caused by sparse annotations in video sequences.
Revised: Semi-Supervised Video Salient Object Detection (SS-VSOD) is challenging because of the lack of temporal information caused by sparse annotations in video sequences.

Original: Most works address this problem by generating pseudo labels for unlabeled data.
Revised: Most works address this problem by generating pseudo labels for unlabeled data.

Original: However, error-prone pseudo labels negatively affect the VOSD model.
Revised: However, error-prone pseudo labels negatively affect the VOSD model.

Original: Therefore, a deeper insight into pseudo labels should be developed.
Revised: Therefore, a deeper insight into pseudo labels should be developed.

Original: In this work, we aim to explore 1) how to utilize the incorrect predictions in pseudo labels to guide the network to generate more robust pseudo labels and 2) how to further screen out the noise that still exists in the improved pseudo labels.
Revised: In this work, we aim to explore 1) how to utilize the incorrect predictions in pseudo labels to guide the network to generate more robust pseudo labels and 2) how to further screen out the noise that still exists in the improved pseudo labels.

Original: To this end, we propose an
Revised: To this end, we propose an

Original: Uncertainty-Guided Pseudo Label Generator (UGPLG), which makes full use of inter-frame information to ensure the temporal consistency of the pseudo-labels and improves the robustness of the pseudo labels by strengthening the learning of difficult scenarios.
Revised: Uncertainty-Guided Pseudo Label Generator (UGPLG), which makes full use of inter-frame information to ensure the temporal consistency of the pseudo-labels and improves the robustness of the pseudo labels by strengthening the learning of difficult scenarios.

Original: Furthermore, we also introduce adversarial learning to address the noise problems in pseudo labels, guaranteeing the positive guidance of pseudo labels during model training.
Revised: Furthermore, we also introduce adversarial learning to address the noise problems in pseudo labels, guaranteeing the positive guidance of pseudo labels during model training.

Original: Experimental results demonstrate that our methods outperform existing semi-supervised method and partial fully-supervised methods across five public benchmarks of DAVIS, FBMS, MCL, ViSal, and SegTrack-V2.
Revised: Experimental results demonstrate that our methods outperform existing semi-supervised method and partial fully-supervised methods across five public benchmarks of DAVIS, FBMS, MCL, ViSal, and SegTrack-V2.

Original: Code and dataset are available at https://github.com/Lanezzz/UGPL .
Revised: Code and dataset are available at https://github.com/Lanezzz/UGPL .

Original:  Introduction
Revised:  Introduction

Original: Video Salient Object Detection (VSOD) aims to locate and segment the objects people are most interested in in the video consequence.
Revised: Video Salient Object Detection (VSOD) aims to locate and segment the objects people are most interested in in the video consequence.

Original: With the increasing demand for video data processing, research on VSOD has received increased attention.
Revised: With the increasing demand for video data processing, research on VSOD has received increased attention.

Original: As a fundamental technique in computer vision, many video-related applications adopt it as preprocessing to allocate more attention to salient regions, such as video tracking [55] , video object segmentation [37], video action recognition [31], video captioning [41].
Revised: As a fundamental technique in computer vision, many video-related applications adopt it as preprocessing to allocate more attention to salient regions, such as video tracking [55] , video object segmentation [37], video action recognition [31], video captioning [41].

Original: Compared with still-image-based SOD tasks, VSOD is more challenging because the prediction of salient objects in video is heavily dependent on temporal dynamics.
Revised: Compared with still-image-based SOD tasks, VSOD is more challenging because the prediction of salient objects in video is heavily dependent on temporal dynamics.

Original: In recent years, deep-learningbased VSOD has achieved significant progress with the development of CNN.
Revised: In recent years, deep-learningbased VSOD has achieved significant progress with the development of CNN.

Original: However, it is usually expensive and difficult to label temporally consistent pixel-level annotations for videos.
Revised: However, it is usually expensive and difficult to label temporally consistent pixel-level annotations for videos.

Original: To reduce the heavy efforts of labeling segmentation ground truth, numerous pseudo-label based semi-supervised video methods [58, 3, 12] are proposed.
Revised: To reduce the heavy efforts of labeling segmentation ground truth, numerous pseudo-label based semi-supervised video methods [58, 3, 12] are proposed.

Original: These works try to use limited, sparsely annotated groundtruth labels to generate labels for unlabeled data through self-training or label propagation, to make up for the lack of temporal information between sparsely annotated frames.
Revised: These works try to use limited, sparsely annotated groundtruth labels to generate labels for unlabeled data through self-training or label propagation, to make up for the lack of temporal information between sparsely annotated frames.

Original: Because video data has much redundant information ( et al. , 24 fps in the DAVIS [43] dataset), we believe using a small amount of ground truth and large amounts of pseudo labels to supervise the model’s training tends to be more efficient for VSOD.
Revised: Because video data has much redundant information ( et al. , 24 fps in the DAVIS [43] dataset), we believe using a small amount of ground truth and large amounts of pseudo labels to supervise the model’s training tends to be more efficient for VSOD.

Original: [Figure]
Revised: [Figure]

Original: Yan et al.
Revised: Yan et al.

Original: [58] try to address semi-supervised VSOD by proposing a teacher network trained with the sparsely sampled annotations (sample every n frames, n > 1) to generate pseudo labels for the unlabeled data, which are involved in the subsequent training of the video saliency model with manually annotated labels.
Revised: [58] try to address semi-supervised VSOD by proposing a teacher network trained with the sparsely sampled annotations (sample every n frames, n > 1) to generate pseudo labels for the unlabeled data, which are involved in the subsequent training of the video saliency model with manually annotated labels.

Original: Despite their great performance, further refinements for pseudo labels should be concerned.
Revised: Despite their great performance, further refinements for pseudo labels should be concerned.

Original: We observe that pseudo labels tend to have obvious errors regarding difficult scenarios, such as deformed objects, interference from similar objects, and complex contours(shown in Fig. 1).
Revised: We observe that pseudo labels tend to have obvious errors regarding difficult scenarios, such as deformed objects, interference from similar objects, and complex contours(shown in Fig. 1).

Original: Error-prone pseudo labels are usually detrimental to final detection.
Revised: Error-prone pseudo labels are usually detrimental to final detection.

Original: Inspired by these observations, we think two key issues need to be considered: 1) How do we use the incorrect information in pseudo labels to guide the pseudo labels generation network to generate more reliable predictions in difficult scenarios.
Revised: Inspired by these observations, we think two key issues need to be considered: 1) How do we use the incorrect information in pseudo labels to guide the pseudo labels generation network to generate more reliable predictions in difficult scenarios.

Original: How do we suppress the noise that still exists in pseudo labels for further improving the video saliency model.
Revised: How do we suppress the noise that still exists in pseudo labels for further improving the video saliency model.

Original: In this paper, we strive to overcome difficulties toward accurate semi-supervised VSOD.
Revised: In this paper, we strive to overcome difficulties toward accurate semi-supervised VSOD.

Original: The primary challenge toward this goal is to generate high-quality pseudo labels and avoid the interference of the noise that still exists in the pseudo labels.
Revised: The primary challenge toward this goal is to generate high-quality pseudo labels and avoid the interference of the noise that still exists in the pseudo labels.

Original: The key aspect in the success of our method is to utilize the erroneous prediction in pseudo labels to strengthen the robustness of the pseudo label generator and formulate noise suppression as an adversarial learning problem.
Revised: The key aspect in the success of our method is to utilize the erroneous prediction in pseudo labels to strengthen the robustness of the pseudo label generator and formulate noise suppression as an adversarial learning problem.

Original: Concretely, our contributions are threefold:
Revised: Concretely, our contributions are threefold:

Original: • We propose a pseudo label generator that is equipped with an Uncertainty-Aware Dual Decoder Module(UADDM).
Revised: • We propose a pseudo label generator that is equipped with an Uncertainty-Aware Dual Decoder Module(UADDM).

Original: It makes full use of the inter-frame information to locate the salient objects in unlabeled frames, ensuring the temporal consistency of pseudo labels.
Revised: It makes full use of the inter-frame information to locate the salient objects in unlabeled frames, ensuring the temporal consistency of pseudo labels.

Original: To our best knowledge, we first utilize the erroneous prediction in pseudo-labels to strengthen the robustness of the pseudo-label generator by proposing UADDM in semi-supervised VSOD.
Revised: To our best knowledge, we first utilize the erroneous prediction in pseudo-labels to strengthen the robustness of the pseudo-label generator by proposing UADDM in semi-supervised VSOD.

Original: • We introduce adversarial learning to learn the distribution of the sparsely labeled training set, and the regions that do not conform to the ground truth distribution in pseudo labels can be filtered out by the generative adversarial network.
Revised: • We introduce adversarial learning to learn the distribution of the sparsely labeled training set, and the regions that do not conform to the ground truth distribution in pseudo labels can be filtered out by the generative adversarial network.

Original: • We conduct extensive experiments on 5 widely-used datasets and demonstrate that our method outperforms existing semi-supervised VSOD method and partial fully-supervised methods.
Revised: • We conduct extensive experiments on 5 widely-used datasets and demonstrate that our method outperforms existing semi-supervised VSOD method and partial fully-supervised methods.

Original:  Related Work 2.1 Salient Object Detection
Revised:  Related Work 2.1 Salient Object Detection

Original: During the past decades, large amounts of conventional methods have been developed for SOD.
Revised: During the past decades, large amounts of conventional methods have been developed for SOD.

Original: Early works [24, 2, 59] mainly rely on intrinsic cues ( e.g. , color and texture) to extract saliency features.
Revised: Early works [24, 2, 59] mainly rely on intrinsic cues ( e.g. , color and texture) to extract saliency features.

Original: However, because low-level features cannot capture rich contextual semantic information, the application scenarios of these traditional methods are very limited.
Revised: However, because low-level features cannot capture rich contextual semantic information, the application scenarios of these traditional methods are very limited.

Original: Later on, with the development of deep learning techniques, deep-learning-based methods ( et al. , [15, 6, 56, 45, 9, 22, 63, 23, 21]) are dominant in this field, which can be divided into integration-based models and edge-based models.
Revised: Later on, with the development of deep learning techniques, deep-learning-based methods ( et al. , [15, 6, 56, 45, 9, 22, 63, 23, 21]) are dominant in this field, which can be divided into integration-based models and edge-based models.

Original: Integration-based models aim to aggregate multi-scale features to leverage context information of different levels.
Revised: Integration-based models aim to aggregate multi-scale features to leverage context information of different levels.

Original: Hou et al.
Revised: Hou et al.

Original: [15] introduce short connections by linking the deep layers towards shallower ones to integrate features of different layers.
Revised: [15] introduce short connections by linking the deep layers towards shallower ones to integrate features of different layers.

Original: Wu et al.
Revised: Wu et al.

Original: [56] aggregate partial high-level features to generate a coarse attention map to guide the network to output more accurate maps.
Revised: [56] aggregate partial high-level features to generate a coarse attention map to guide the network to output more accurate maps.

Original: Edge-based models attempt to generate prediction maps with clear boundaries by making better use of edge information.
Revised: Edge-based models attempt to generate prediction maps with clear boundaries by making better use of edge information.

Original: Qin et al.
Revised: Qin et al.

Original: [45] Propose a hybrid loss that fuses BCE, SSIM, and IoU to supervise the training process on pixel-level, patch-level, and map-level.
Revised: [45] Propose a hybrid loss that fuses BCE, SSIM, and IoU to supervise the training process on pixel-level, patch-level, and map-level.

Original: Feng et al.
Revised: Feng et al.

Original: [9] introduces a boundary-enhanced loss as an assistant to learning exquisite object contours which saves the post-processing operations to refine the boundaries.
Revised: [9] introduces a boundary-enhanced loss as an assistant to learning exquisite object contours which saves the post-processing operations to refine the boundaries.

Original: Wei et al.
Revised: Wei et al.

Original: [54] design pixel position aware loss to assign higher weights to edge positions which can help the network focus more on boundary regions.
Revised: [54] design pixel position aware loss to assign higher weights to edge positions which can help the network focus more on boundary regions.

Original: More detailed descriptions of research in SOD field can be approached in related literatures [50, 65, 19, 30, 60, 44, 61].
Revised: More detailed descriptions of research in SOD field can be approached in related literatures [50, 65, 19, 30, 60, 44, 61].

Original: 2.2 Video Salient Object Detection
Revised: 2.2 Video Salient Object Detection

Original: Existing VSOD methods can be divided into two categories: conventional models and deep learning models.
Revised: Existing VSOD methods can be divided into two categories: conventional models and deep learning models.

Original: Traditional methods rely on people’s prior knowledge to extract hand-crafted features, such as color-contrast [34], background prior [57] and morphology cues [46].
Revised: Traditional methods rely on people’s prior knowledge to extract hand-crafted features, such as color-contrast [34], background prior [57] and morphology cues [46].

Original: Because low-level features have limited representational ability, these conventional methods are usually only suitable for specific scenarios.
Revised: Because low-level features have limited representational ability, these conventional methods are usually only suitable for specific scenarios.

Original: With the development of CNN, deep-learning-based VOSD models have recently achieved great success.
Revised: With the development of CNN, deep-learning-based VOSD models have recently achieved great success.

Original: Fan et al.
Revised: Fan et al.

Original: [8] present a baseline model with a saliency-shift-aware convLSTM module to progressively integrate temporal information.
Revised: [8] present a baseline model with a saliency-shift-aware convLSTM module to progressively integrate temporal information.

Original: Li et al.
Revised: Li et al.

Original: [29] propose a dual-stream network to enhance appearance features with motion features or motion saliency.
Revised: [29] propose a dual-stream network to enhance appearance features with motion features or motion saliency.

Original: Gu et al.
Revised: Gu et al.

Original: [13] design a 3D pyramid constrained self-attention module to capture local motion cues of salient objects.
Revised: [13] design a 3D pyramid constrained self-attention module to capture local motion cues of salient objects.

Original: Liu et al.
Revised: Liu et al.

Original: [62] proposes a dynamic context-sensitive filtering module to dynamically generate context-sensitive convolution kernels and introduce a bidirectional fusion strategy to fuse spatial and temporal features.
Revised: [62] proposes a dynamic context-sensitive filtering module to dynamically generate context-sensitive convolution kernels and introduce a bidirectional fusion strategy to fuse spatial and temporal features.

Original: However, the remarkable performance achieved by these methods relies on densely annotated video datasets which cost considerable expense and time.
Revised: However, the remarkable performance achieved by these methods relies on densely annotated video datasets which cost considerable expense and time.

Original: Researchers explore addressing VSOD by using weak supervision or semi-supervision to relieve the burden of handcrafted labeling.
Revised: Researchers explore addressing VSOD by using weak supervision or semi-supervision to relieve the burden of handcrafted labeling.

Original: Zhao et al.
Revised: Zhao et al.

Original: [64] designs multiple losses from the perspectives of boundary, structure, and front-background similarity to learn SOD by using fixation guided scribble annotations.
Revised: [64] designs multiple losses from the perspectives of boundary, structure, and front-background similarity to learn SOD by using fixation guided scribble annotations.

Original: Yan et al.
Revised: Yan et al.

Original: [58] proposes to utilize a self-training method to generate pseudo labels to ease the effort of acquiring high-quality manually annotated data.
Revised: [58] proposes to utilize a self-training method to generate pseudo labels to ease the effort of acquiring high-quality manually annotated data.

Original: 2.3 Semi-supervised Semantic Segmentation
Revised: 2.3 Semi-supervised Semantic Segmentation

Original: Existing semantic segmentation methods are also being actively explored to reduce the utilization of large amounts of annotated data.
Revised: Existing semantic segmentation methods are also being actively explored to reduce the utilization of large amounts of annotated data.

Original: There are two mainstream methods in Semi-supervised Image
Revised: There are two mainstream methods in Semi-supervised Image

Original: Semantic Segmentation.
Revised: Semantic Segmentation.

Original: The first is consistency regularization which applies different perturbation to the same image and requires their predictions or intermediate features to be consistent [10, 40, 25].
Revised: The first is consistency regularization which applies different perturbation to the same image and requires their predictions or intermediate features to be consistent [10, 40, 25].

Original: The second method is based on self-training [32, 38, 16].
Revised: The second method is based on self-training [32, 38, 16].

Original: A teacher network is firstly trained with a small number of ground truth labels and then the network is used to generate pseudo-labels for unlabeled data.
Revised: A teacher network is firstly trained with a small number of ground truth labels and then the network is used to generate pseudo-labels for unlabeled data.

Original: Finally, a student model is jointly trained with ground truth labels and pseudo-labels.
Revised: Finally, a student model is jointly trained with ground truth labels and pseudo-labels.

Original: Due to the lack of strong data augmentation for video data, it is difficult to apply the consistency regularization to video-related tasks.
Revised: Due to the lack of strong data augmentation for video data, it is difficult to apply the consistency regularization to video-related tasks.

Original: Current Semi-supervised Video Semantic Segmentation mainly relys on self-training.
Revised: Current Semi-supervised Video Semantic Segmentation mainly relys on self-training.

Original: Chen et al.
Revised: Chen et al.

Original: [3] train a teacher network to generate pseudo-labels for unlabeled data and then train the student network with human-annotated labels and pseudo-labels iteratively.
Revised: [3] train a teacher network to generate pseudo-labels for unlabeled data and then train the student network with human-annotated labels and pseudo-labels iteratively.

Original: Ganeshan et al.
Revised: Ganeshan et al.

Original: [12] combines optical flow and the prediction of models to generate a coarse pseudolabel, followed by a refine network to achieve better pseudo labels.
Revised: [12] combines optical flow and the prediction of models to generate a coarse pseudolabel, followed by a refine network to achieve better pseudo labels.

Original:  Our methods 3.1 Overview
Revised:  Our methods 3.1 Overview

Original: To avoid low-quality pseudo labels that negatively affect VSOD, our method aims to progressively enhance the quality of pseudo labels and strictly filter out the noise in pseudo labels: (shown in
Revised: To avoid low-quality pseudo labels that negatively affect VSOD, our method aims to progressively enhance the quality of pseudo labels and strictly filter out the noise in pseudo labels: (shown in

Original: Fig. 2))
Revised: Fig. 2))

Original: We propose an Uncertainty-Guided Pseudo Label Generator (UGPLG) which uses interframe information to generate consecutive pseudo labels and enhances the learning of difficult scenarios to further improve the robustness of pseudo labels.
Revised: We propose an Uncertainty-Guided Pseudo Label Generator (UGPLG) which uses interframe information to generate consecutive pseudo labels and enhances the learning of difficult scenarios to further improve the robustness of pseudo labels.

Original: We introduce adversarial learning for noise suppression (NS-GAN) to screen out the regions in pseudo labels that do not conform to the distribution of ground truth, making pseudo labels more reliable.
Revised: We introduce adversarial learning for noise suppression (NS-GAN) to screen out the regions in pseudo labels that do not conform to the distribution of ground truth, making pseudo labels more reliable.

Original: CFGM
Revised: CFGM

Original: [Figure]
Revised: [Figure]

Original: Encoder
Revised: Encoder

Original: AL
Revised: AL

Original: ML
Revised: ML

Original: GT t+n Uncertainty map
Revised: GT t+n Uncertainty map

Original: MainDecoder
Revised: MainDecoder

Original: AuxiliaryDecoder
Revised: AuxiliaryDecoder

Original: I t I t+n
Revised: I t I t+n

Original: Ground truth
Revised: Ground truth

Original: Pseudo label
Revised: Pseudo label

Original: Generator
Revised: Generator

Original: Discriminator
Revised: Discriminator

Original: Confidence map
Revised: Confidence map

Original: Supervision Supervision
Revised: Supervision Supervision

Original: Unlabeled data
Revised: Unlabeled data

Original: Labeled data
Revised: Labeled data

Original: Similarity Matrix
Revised: Similarity Matrix

Original: M
Revised: M

Original: C
Revised: C

Original: CFGM GT guidance matmul product M hadamard product
Revised: CFGM GT guidance matmul product M hadamard product

Original: C concatenate
Revised: C concatenate

Original: AL auxiliary loss
Revised: AL auxiliary loss

Original: [Figure]
Revised: [Figure]

Original: ML
Revised: ML

Original: [Figure]
Revised: [Figure]

Original: labeled
Revised: labeled

Original: [Figure]
Revised: [Figure]

Original: flow unlabeled
Revised: flow unlabeled

Original: [Figure]
Revised: [Figure]

Original: flow supervision
Revised: flow supervision

Original: UADD
Revised: UADD

Original: [Figure]
Revised: [Figure]

Original: optional
Revised: optional

Original: [Figure]
Revised: [Figure]

Original: The proposed UGPLG (shown in Stage 1 of Fig. 2) follows the encoder-decoder architecture.
Revised: The proposed UGPLG (shown in Stage 1 of Fig. 2) follows the encoder-decoder architecture.

Original: The sharing encoder is based on ResNet50 [14] with ASPP [4].
Revised: The sharing encoder is based on ResNet50 [14] with ASPP [4].

Original: It consists of a Cross-Frame Global
Revised: It consists of a Cross-Frame Global

Original: Matching Module (CFGMM) and an Uncertainty-Aware Dual Decoder Module (UADDM).
Revised: Matching Module (CFGMM) and an Uncertainty-Aware Dual Decoder Module (UADDM).

Original: CFGMM aims to refer to the saliency cues in the same video to guarantee the temporal consistency of pseudo labels.
Revised: CFGMM aims to refer to the saliency cues in the same video to guarantee the temporal consistency of pseudo labels.

Original: And UADDM aims to enhance the learning of uncertain regions in pseudo labels to improve the robustness of pseudo labels.
Revised: And UADDM aims to enhance the learning of uncertain regions in pseudo labels to improve the robustness of pseudo labels.

Original: Equipped with the two modules, UGPLG can generate high-quality pseudo labels for complex scenerios.
Revised: Equipped with the two modules, UGPLG can generate high-quality pseudo labels for complex scenerios.

Original: The proposed NS-GAN (shown in Stage 2 of Fig. 2) uses a video saliency model as the generator.
Revised: The proposed NS-GAN (shown in Stage 2 of Fig. 2) uses a video saliency model as the generator.

Original: In the process of adversarial learning, the discriminator can accurately distinguish between the ground truths and the predictions of the generator.
Revised: In the process of adversarial learning, the discriminator can accurately distinguish between the ground truths and the predictions of the generator.

Original: Thus by using the discriminator to filter the noise for the pseudo labels, the training process of the generator is free from the negative effects of noise.
Revised: Thus by using the discriminator to filter the noise for the pseudo labels, the training process of the generator is free from the negative effects of noise.

Original: 3.2 Uncertainty-Guided Pseudo Label Generator
Revised: 3.2 Uncertainty-Guided Pseudo Label Generator

Original: Cross-Frame Global Matching.
Revised: Cross-Frame Global Matching.

Original: Generating pseudo labels for unlabeled video data is very dependent on temporal information because it contains motion cues and location information of the salient objects.
Revised: Generating pseudo labels for unlabeled video data is very dependent on temporal information because it contains motion cues and location information of the salient objects.

Original: The generated pseudo labels are temporally inconsistent if temporal information is not fully utilized.
Revised: The generated pseudo labels are temporally inconsistent if temporal information is not fully utilized.

Original: A straightforward utilization of temporal information [11] is computing the optical flow to warp labels to the unlabeled frames.
Revised: A straightforward utilization of temporal information [11] is computing the optical flow to warp labels to the unlabeled frames.

Original: However, optical flow generated from [17] [48] usually cannot accurately predict the geometric relationship between two frames within a long time interval.
Revised: However, optical flow generated from [17] [48] usually cannot accurately predict the geometric relationship between two frames within a long time interval.

Original: Therefore, to model the long-term temporal relationship, we select inter-frame global matching which can ignore time intervals to build a bridge for the saliency information propagation between labeled frames and unlabeled frames.
Revised: Therefore, to model the long-term temporal relationship, we select inter-frame global matching which can ignore time intervals to build a bridge for the saliency information propagation between labeled frames and unlabeled frames.

Original: The whole propagation process is divided into two steps.
Revised: The whole propagation process is divided into two steps.

Original: In the first step, shown as Stage 1 in Fig. 2, given two video frames I t and frame
Revised: In the first step, shown as Stage 1 in Fig. 2, given two video frames I t and frame

Original: I t + n in the same video, F t ∈ R C × ( HW ) and F t + n ∈ R C
Revised: I t + n in the same video, F t ∈ R C × ( HW ) and F t + n ∈ R C

Original: × ( HW ) represent the corresponding features from the sharing encoder.
Revised: × ( HW ) represent the corresponding features from the sharing encoder.

Original: We aim to compute the similarity matrix S between F t and F t + n by leveraging the non-local [53] mechanism:
Revised: We aim to compute the similarity matrix S between F t and F t + n by leveraging the non-local [53] mechanism:

Original: [Equation]
Revised: [Equation]

Original: where W ∈ R C × C means a weight matrix and flat means the operation of flatten.
Revised: where W ∈ R C × C means a weight matrix and flat means the operation of flatten.

Original: Then we normalize S in a column-wise way with a softmax function:
Revised: Then we normalize S in a column-wise way with a softmax function:

Original: [Equation]
Revised: [Equation]

Original: Each column of the matrix S represents the relevance between a position on the unlabeled frame and each position on the salient regions of the labeled frame.
Revised: Each column of the matrix S represents the relevance between a position on the unlabeled frame and each position on the salient regions of the labeled frame.

Original: In the second step, we aim to find out the features in F t + n that are most correlated to salient objects in F t .
Revised: In the second step, we aim to find out the features in F t + n that are most correlated to salient objects in F t .

Original: The transposed F t is multiplied by the similarity matrix S , and we can get the warped feature (cid:101) F t + n propagated from F t ,
Revised: The transposed F t is multiplied by the similarity matrix S , and we can get the warped feature (cid:101) F t + n propagated from F t ,

Original: [Equation]
Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: Each element of (cid:101) F t + n represents the sum of the similarity of all positions on F t and current position on F t + n .
Revised: Each element of (cid:101) F t + n represents the sum of the similarity of all positions on F t and current position on F t + n .

Original: By concatenating (cid:101) F t + n and F t + n , we can achieve F fuse which not only retains the spatial information of current frame but also integrates the prior information about salient objects of other frames in the same video.
Revised: By concatenating (cid:101) F t + n and F t + n , we can achieve F fuse which not only retains the spatial information of current frame but also integrates the prior information about salient objects of other frames in the same video.

Original: In this way, our UGPLG can generate temporally consistent pseudo labels.
Revised: In this way, our UGPLG can generate temporally consistent pseudo labels.

Original: [Equation]
Revised: [Equation]

Original: where [ · , · ] means concatenation operation.
Revised: where [ · , · ] means concatenation operation.

Original: Uncertainty-aware Dual Decoder.
Revised: Uncertainty-aware Dual Decoder.

Original: Since conventional pseudo label generators are difficult to make accurate predictions in difficult scenarios, to achieve robust pseudo labels, we design an
Revised: Since conventional pseudo label generators are difficult to make accurate predictions in difficult scenarios, to achieve robust pseudo labels, we design an

Original: Uncertainty-Aware Dual Decoder Module (UADDM) to detect and utilize the uncertain regions in pseudo labels to improve the performance of UGPLG.
Revised: Uncertainty-Aware Dual Decoder Module (UADDM) to detect and utilize the uncertain regions in pseudo labels to improve the performance of UGPLG.

Original: During training, when we feed the temporally enhanced feature F fuse to the UADDM, the auxiliary decoder makes a coarse prediction for I t + n
Revised: During training, when we feed the temporally enhanced feature F fuse to the UADDM, the auxiliary decoder makes a coarse prediction for I t + n

Original: and it is supervised by L t + n :
Revised: and it is supervised by L t + n :

Original: [Equation]
Revised: [Equation]

Original: (5) where Aux ( · ) means the auxiliary decoder and L t + n means the label of I t + n .
Revised: (5) where Aux ( · ) means the auxiliary decoder and L t + n means the label of I t + n .

Original: Due to the lack of a superior training strategy, the prediction map of the auxiliary decoder often has obvious errors regarding difficult scenarios.
Revised: Due to the lack of a superior training strategy, the prediction map of the auxiliary decoder often has obvious errors regarding difficult scenarios.

Original: When a certain position of l aux is greater than a fixed value, we consider the auxiliary decoder’s prediction for that position to be uncertain.
Revised: When a certain position of l aux is greater than a fixed value, we consider the auxiliary decoder’s prediction for that position to be uncertain.

Original: Since the structure of the main decoder is exactly the same as that of the auxiliary decoder, the prediction error of the auxiliary decoder in difficult scenarios has a great probability to occur on the main decoder.
Revised: Since the structure of the main decoder is exactly the same as that of the auxiliary decoder, the prediction error of the auxiliary decoder in difficult scenarios has a great probability to occur on the main decoder.

Original: We assign a higher weight to the uncertain regions the auxiliary decoder predicts to guide the main decoder to train the network to generate more robust pseudo-labels.
Revised: We assign a higher weight to the uncertain regions the auxiliary decoder predicts to guide the main decoder to train the network to generate more robust pseudo-labels.

Original: [Equation]
Revised: [Equation]

Original: where I ( · ) is the indicator function, and Main ( · ) means main decoder.
Revised: where I ( · ) is the indicator function, and Main ( · ) means main decoder.

Original: T loss is a fixed threshold which is empirically set as 0.3, and α is empirically set as 5.
Revised: T loss is a fixed threshold which is empirically set as 0.3, and α is empirically set as 5.

Original: Thus, the pseudo labels generated by
Revised: Thus, the pseudo labels generated by

Original: UGPLG can be more robust.
Revised: UGPLG can be more robust.

Original: As training goes on, the ability of the auxiliary decoder is gradually improved, the number of erroneous predictions is gradually reduced, and the uncertain areas also gradually shrink.
Revised: As training goes on, the ability of the auxiliary decoder is gradually improved, the number of erroneous predictions is gradually reduced, and the uncertain areas also gradually shrink.

Original: Therefore the auxiliary decoder can better guide the main decoder to pay more attention to the real complex scenarios.
Revised: Therefore the auxiliary decoder can better guide the main decoder to pay more attention to the real complex scenarios.

Original: It is noted that the auxiliary decoder is used only in the training stage.
Revised: It is noted that the auxiliary decoder is used only in the training stage.

Original: Apart from the standard BCELoss, we also introduce IoU loss to enable the model to focus on global structure.
Revised: Apart from the standard BCELoss, we also introduce IoU loss to enable the model to focus on global structure.

Original: l bce and l iou are defined as:
Revised: l bce and l iou are defined as:

Original: [Equation]
Revised: [Equation]

Original: where ( x, y ) means the location of the pixel, H , W means the height and width, P ( · ) means the prediction of the model, and L ( · ) means ground truth.
Revised: where ( x, y ) means the location of the pixel, H , W means the height and width, P ( · ) means the prediction of the model, and L ( · ) means ground truth.

Original: 3.3 Adversarial Learning for noise suppression
Revised: 3.3 Adversarial Learning for noise suppression

Original: Generative Adversarial Network.
Revised: Generative Adversarial Network.

Original: To further improve the reliability of pseudo labels, we propose a GAN-based noise suppression mechanism to filter out the regions that do not conform to the distribution of ground truth.
Revised: To further improve the reliability of pseudo labels, we propose a GAN-based noise suppression mechanism to filter out the regions that do not conform to the distribution of ground truth.

Original: The generator is trained to confuse the discriminator so that the distribution of the generator can be closer to the ground truth distribution.
Revised: The generator is trained to confuse the discriminator so that the distribution of the generator can be closer to the ground truth distribution.

Original: Correspondingly, in the process of adversarial learning, the ability of the discriminator also becomes stronger.
Revised: Correspondingly, in the process of adversarial learning, the ability of the discriminator also becomes stronger.

Original: In that case, the discriminator can accurately identify the noise that still exists in the pseudo labels.
Revised: In that case, the discriminator can accurately identify the noise that still exists in the pseudo labels.

Original: By suppressing the noise, we can ensure that pseudo labels are always providing positive guidance to the generator.
Revised: By suppressing the noise, we can ensure that pseudo labels are always providing positive guidance to the generator.

Original: CRM
Revised: CRM

Original: E E
Revised: E E

Original: D D
Revised: D D

Original: D
Revised: D

Original: RGB pred
Revised: RGB pred

Original: Fuse pred
Revised: Fuse pred

Original: OPT pred
Revised: OPT pred

Original: I k
Revised: I k

Original: I t
Revised: I t

Original: O t O k
Revised: O t O k

Original: The detailed structure of the generator is shown in Fig.
Revised: The detailed structure of the generator is shown in Fig.

Original: 3, which is a classic twostream network.
Revised: 3, which is a classic twostream network.

Original: The upper symmetrical branch is the RGB branch which learns information such as color and outline, and the lower branch is the optical flow branch which learns information such as position and semantics.
Revised: The upper symmetrical branch is the RGB branch which learns information such as color and outline, and the lower branch is the optical flow branch which learns information such as position and semantics.

Original: The two symmetrical branches perform information fusion through a well-designed cross-modality information integration module CRM [20].
Revised: The two symmetrical branches perform information fusion through a well-designed cross-modality information integration module CRM [20].

Original: Since the information of the two branches is complementary, the fused features of the aggregation branch retain both local detailed information and global structural information.
Revised: Since the information of the two branches is complementary, the fused features of the aggregation branch retain both local detailed information and global structural information.

Original: The generator takes both labeled and unlabeled data as input and outputs the predictions of the three branches.
Revised: The generator takes both labeled and unlabeled data as input and outputs the predictions of the three branches.

Original: The encoder of each symmetrical branch is based on
Revised: The encoder of each symmetrical branch is based on

Original: ResNet50 [14] with ASPP [4].
Revised: ResNet50 [14] with ASPP [4].

Original: The decoder of the symmetrical branch consists of three convolutional layers, and the decoder of the aggregation branch has one more than that of the symmetrical branch.
Revised: The decoder of the symmetrical branch consists of three convolutional layers, and the decoder of the aggregation branch has one more than that of the symmetrical branch.

Original: Similar to [16], our discriminator network is based on FCN [35].
Revised: Similar to [16], our discriminator network is based on FCN [35].

Original: It takes the prediction maps of the saliency model or ground truth labels as input and then outputs a probability map of size H × W × 1 .
Revised: It takes the prediction maps of the saliency model or ground truth labels as input and then outputs a probability map of size H × W × 1 .

Original: Training with labeled data.
Revised: Training with labeled data.

Original: The generator and the discriminator are alternately trained on labeled data.
Revised: The generator and the discriminator are alternately trained on labeled data.

Original: For the training of the discriminator.
Revised: For the training of the discriminator.

Original: We minimize the loss using:
Revised: We minimize the loss using:

Original: [Equation]
Revised: [Equation]

Original: where G fuse ( · ) is the output of the aggregation branch in the saliency model, M 0 is an all-zero matrix and M 1 is an all-one matrix.
Revised: where G fuse ( · ) is the output of the aggregation branch in the saliency model, M 0 is an all-zero matrix and M 1 is an all-one matrix.

Original: I l , O l , and Y l are the image, optical flow, and ground truth of the labeled frame, respectively.
Revised: I l , O l , and Y l are the image, optical flow, and ground truth of the labeled frame, respectively.

Original: When the input is from ground truth, the discriminator output an all-one map, and when the input comes from the prediction map of the generator, the discriminator output an all-zero map.
Revised: When the input is from ground truth, the discriminator output an all-one map, and when the input comes from the prediction map of the generator, the discriminator output an all-zero map.

Original: To enable the generator to generate prediction maps with the similar distribution as ground truth,
Revised: To enable the generator to generate prediction maps with the similar distribution as ground truth,

Original: G rgb ( · ) , G opt ( · ) , G fuse ( · ) are all supervised by standard BCEloss, and G fuse ( · ) is additionally supervised by the adversarial loss l adv .
Revised: G rgb ( · ) , G opt ( · ) , G fuse ( · ) are all supervised by standard BCEloss, and G fuse ( · ) is additionally supervised by the adversarial loss l adv .

Original: The total loss of the generator supervised by labeled data l GL is defined as:
Revised: The total loss of the generator supervised by labeled data l GL is defined as:

Original: [Equation]
Revised: [Equation]

Original: Where δ is a hyperparameter with its value of 0.2, and G rgb ( · ) , G opt ( · ) mean the prediction map of
Revised: Where δ is a hyperparameter with its value of 0.2, and G rgb ( · ) , G opt ( · ) mean the prediction map of

Original: RGB branch, Optical Flow branch, respectively.
Revised: RGB branch, Optical Flow branch, respectively.

Original: l adv is defined as:
Revised: l adv is defined as:

Original: [Equation]
Revised: [Equation]

Original: With l adv , the generator is trained to confuse the discriminator by maximizing the probability of the confidence map.
Revised: With l adv , the generator is trained to confuse the discriminator by maximizing the probability of the confidence map.

Original: Then the distribution of the prediction output by the generator gets closer to the distribution of ground truth.
Revised: Then the distribution of the prediction output by the generator gets closer to the distribution of ground truth.

Original: Training with unlabeled data.
Revised: Training with unlabeled data.

Original: Before using pseudo labels for supervised training, to avoid noise providing wrong guidance to the saliency model, we need to filter out the noise in pseudo labels at first.
Revised: Before using pseudo labels for supervised training, to avoid noise providing wrong guidance to the saliency model, we need to filter out the noise in pseudo labels at first.

Original: We feed the pseudo labels to the discriminator and obtain the corresponding confidence maps.
Revised: We feed the pseudo labels to the discriminator and obtain the corresponding confidence maps.

Original: Then the confidence map D ( PL u ) is directly used as the weight of l GU which is the total loss of the generator supervised by unlabeled data:
Revised: Then the confidence map D ( PL u ) is directly used as the weight of l GU which is the total loss of the generator supervised by unlabeled data:

Original: [Equation]
Revised: [Equation]

Original: Where the δ is the same as in Eq.
Revised: Where the δ is the same as in Eq.

Original: I u , O u , and PL u are the image, optical flow, and pseudo label of the unlabeled frame, respectively.
Revised: I u , O u , and PL u are the image, optical flow, and pseudo label of the unlabeled frame, respectively.

Original: Because the weights of the noisy regions are very small, the noisy regions have little effect on the loss l GU , thus avoiding the model learning from the noise in the pseudo-labels, ensuring the stability of saliency model training and helping the model to converge better.
Revised: Because the weights of the noisy regions are very small, the noisy regions have little effect on the loss l GU , thus avoiding the model learning from the noise in the pseudo-labels, ensuring the stability of saliency model training and helping the model to converge better.

Original: At last, the total loss of the generator is composed of the three types of loss:
Revised: At last, the total loss of the generator is composed of the three types of loss:

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

Original:  Experiments 4.1 Datasets
Revised:  Experiments 4.1 Datasets

Original: To evaluate the performance of our method, we conduct experiments on five widely-used VSOD datasets for fair comparisons.
Revised: To evaluate the performance of our method, we conduct experiments on five widely-used VSOD datasets for fair comparisons.

Original: DAVIS [43] is the most popular VSOD dataset, with 50 high-quality fully annotated video sequences.
Revised: DAVIS [43] is the most popular VSOD dataset, with 50 high-quality fully annotated video sequences.

Original: The whole dataset is split into 30 sequences (2079 frames) for training and 20 sequences (1376 frames) for testing.
Revised: The whole dataset is split into 30 sequences (2079 frames) for training and 20 sequences (1376 frames) for testing.

Original: FBMS [39] includes 59 natural video sequences which contains 13860 frames while only 780 of them are sparsely annotated.
Revised: FBMS [39] includes 59 natural video sequences which contains 13860 frames while only 780 of them are sparsely annotated.

Original: The whole dataset is split into 29 sequences (353 frames) for training and 30 sequences (720 frames) for testing.
Revised: The whole dataset is split into 29 sequences (353 frames) for training and 30 sequences (720 frames) for testing.

Original: ViSal [51] is the first specially collected dataset for VSOD.
Revised: ViSal [51] is the first specially collected dataset for VSOD.

Original: It contains 17 video sequences with 193 sparsely annotated frames.
Revised: It contains 17 video sequences with 193 sparsely annotated frames.

Original: MCL [26] includes 9 video sequences about objects with fast movement.
Revised: MCL [26] includes 9 video sequences about objects with fast movement.

Original: There are total 463 sparsely annotated frames.
Revised: There are total 463 sparsely annotated frames.

Original: SegTrack-V2 [27] is the earliest VOS dataset which contains 13 videos, with a total of 1025 annotated frames.
Revised: SegTrack-V2 [27] is the earliest VOS dataset which contains 13 videos, with a total of 1025 annotated frames.

Original: 4.2 Experimental Setup
Revised: 4.2 Experimental Setup

Original: Evaluation Metrics.
Revised: Evaluation Metrics.

Original: We use three universally-agreed criterions to evaluate our results, i.e. , mean absolute error (MAE) [42], max F-measure ( F maxβ ) [1] and structure-measure ( S α ) [7].
Revised: We use three universally-agreed criterions to evaluate our results, i.e. , mean absolute error (MAE) [42], max F-measure ( F maxβ ) [1] and structure-measure ( S α ) [7].

Original: Implementation Details.
Revised: Implementation Details.

Original: Our network is implemented on the Pytorch framework with 4 × GTX 1080Ti
Revised: Our network is implemented on the Pytorch framework with 4 × GTX 1080Ti

Original: GPU and it is also adapted to the Mindspore framework of Huawei with an Ascend-910.
Revised: GPU and it is also adapted to the MindSpore framework of Huawei with an Ascend-910.

Original: We choose the train set of DAVIS and FBMS as our training set to train both Uncertainty-Guided Pseudo
Revised: We choose the train set of DAVIS and FBMS as our training set to train both Uncertainty-Guided Pseudo

Original: Label Generator (UGPLG) and Noise-Suppressed Generative Adversarial Network (NS-GAN).
Revised: Label Generator (UGPLG) and Noise-Suppressed Generative Adversarial Network (NS-GAN).

Original: For the training of UGPLG , the initial learning rate is set as 0.005 and decays
Revised: For the training of UGPLG , the initial learning rate is set as 0.005 and decays

Original: 0.1 times every 25 epochs with a batch size of 8.
Revised: 0.1 times every 25 epochs with a batch size of 8.

Original: For the training of NS-GAN , the initial learning rate is set as 0.015 and decays 0.1 times every 20 epochs.
Revised: For the training of NS-GAN , the initial learning rate is set as 0.015 and decays 0.1 times every 20 epochs.

Original: Images are uniformly resized to 448 × 448.
Revised: Images are uniformly resized to 448 × 448.

Original: We adopt an SGD optimizer in which the momentum and weight decay are set to 0.9, 5e-4.
Revised: We adopt an SGD optimizer in which the momentum and weight decay are set to 0.9, 5e-4.

Original: In the pre-train phase for
Revised: In the pre-train phase for

Original: NS-GAN , we pretrain the RGB branch on DUTS [49] which is a commonly used static-image SOD dataset.
Revised: NS-GAN , we pretrain the RGB branch on DUTS [49] which is a commonly used static-image SOD dataset.

Original: The initial learning rate is set as 0.01 and decays
Revised: The initial learning rate is set as 0.01 and decays

Original: 0.1 times every 30 epochs.
Revised: 0.1 times every 30 epochs.

Original: [Table]
Revised: [Table]

Original: 4.3 Comparison with State-of-the-Art
Revised: 4.3 Comparison with State-of-the-Art

Original: The colummn Total calculates the sum of training
Revised: The colummn Total calculates the sum of training

Original: [Table]
Revised: [Table]

Original: DCF
Revised: DCF

Original: DAVSOD + DAVIS + VOS
Revised: DAVSOD + DAVIS + VOS

Original: [Table]
Revised: [Table]

Original: 5K
Revised: 5K

Original: RCR † 20%( DAVIS + FBMS + VOS )
Revised: RCR † 20%( DAVIS + FBMS + VOS )

Original: Ours † 10%( DAVIVS ) + FBMS
Revised: Ours † 10%( DAVIVS ) + FBMS

Original: As shown in Table.
Revised: As shown in Table.

Original: [Table]
Revised: [Table]

Original: we compare our methods with two conventional video salient object methods (remarked with ∗ ): SGSP [33] ∗ , SCOM [5] ∗ , and eight deep-learning based methods: SCNN [47],
Revised: we compare our methods with two conventional video salient object methods (remarked with ∗ ): SGSP [33] ∗ , SCOM [5] ∗ , and eight deep-learning based methods: SCNN [47],

Original: DLVS [52],
Revised: DLVS [52],

Original: FGRN [28],
Revised: FGRN [28],

Original: SSAV [8],
Revised: SSAV [8],

Original: PCSA [13], FDS [18], DCF [62], RCR [58].
Revised: PCSA [13], FDS [18], DCF [62], RCR [58].

Original: To guarantee fair comparisons, we utilize the widely-used evaluation code provided by [8].
Revised: To guarantee fair comparisons, we utilize the widely-used evaluation code provided by [8].

Original: First , compared to the existing SemiSupervised VSOD method RCR, our method
Revised: First , compared to the existing SemiSupervised VSOD method RCR, our method

Original: [Figure]
Revised: [Figure]

Original: significantly outperforms them on all datasets.
Revised: significantly outperforms them on all datasets.

Original: Specifically, our method improves the MAE by 29.6%, 49.1%, 26.9%, 35.7% on DAVIS, FBMS,
Revised: Specifically, our method improves the MAE by 29.6%, 49.1%, 26.9%, 35.7% on DAVIS, FBMS,

Original: ViSal, and SegTrack-V2, respectively, using only 40% of their labeled data (568 versus 1406, shown in Table. 2).
Revised: ViSal, and SegTrack-V2, respectively, using only 40% of their labeled data (568 versus 1406, shown in Table. 2).

Original: Second , compared to the fully supervised methods, our method achieves state-of-the-art on FBMS dataset, and achieves 98.7%, 97.2%, 99.9% and 92.7% of the SOTA accuracy on F max β on DAVIS, ViSal, MCL and SegTrack-V2, respectively.
Revised: Second , compared to the fully supervised methods, our method achieves state-of-the-art on FBMS dataset, and achieves 98.7%, 97.2%, 99.9% and 92.7% of the SOTA accuracy on F max β on DAVIS, ViSal, MCL and SegTrack-V2, respectively.

Original: Despite our great performance, the number of ground truths we use is only 5% of current state-of-the-art fully-supervised methods.
Revised: Despite our great performance, the number of ground truths we use is only 5% of current state-of-the-art fully-supervised methods.

Original: Finally ,
Revised: Finally ,

Original: Fig.
Revised: Fig.

Original: 4 shows visual comparisons to demonstrate the effectiveness of our proposed method to deal with complex scenarios, such as interference of complex background objects ( 1 st and 2 nd rows), similar foreground and background ( 3 rd and 4 th rows) and multiple objects ( 5 rd and 6 th rows).
Revised: 4 shows visual comparisons to demonstrate the effectiveness of our proposed method to deal with complex scenarios, such as interference of complex background objects ( 1 st and 2 nd rows), similar foreground and background ( 3 rd and 4 th rows) and multiple objects ( 5 rd and 6 th rows).

Original: In these complex cases, it can be observed that our saliency model generalizes better than most VSOD models, which proves that our method can help the network to generate more robust predictions in a variety of complex scenarios.
Revised: In these complex cases, it can be observed that our saliency model generalizes better than most VSOD models, which proves that our method can help the network to generate more robust predictions in a variety of complex scenarios.

Original: 4.4 Ablation Studies
Revised: 4.4 Ablation Studies

Original: The Effectiveness of UGPLG.
Revised: The Effectiveness of UGPLG.

Original: Uncertainty-Guided Pseudo Label Generator (UGPLG) consists of Cross-Frame Global Matching Module(CFGMM) and Uncertainty-Aware Dual Decoder Module (UADDM), aiming to generate high-quality pseudo labels to guide the training of the saliency model.
Revised: Uncertainty-Guided Pseudo Label Generator (UGPLG) consists of Cross-Frame Global Matching Module(CFGMM) and Uncertainty-Aware Dual Decoder Module (UADDM), aiming to generate high-quality pseudo labels to guide the training of the saliency model.

Original: We perform abundant experiments to validate the effectiveness of each component of UGPLG.
Revised: We perform abundant experiments to validate the effectiveness of each component of UGPLG.

Original: As shown in Table.
Revised: As shown in Table.

Original: 4, case (1) refers to our baseline model which only retrains the encoder and the main decoder in UGPLG, and the generator in NS-GAN.
Revised: 4, case (1) refers to our baseline model which only retrains the encoder and the main decoder in UGPLG, and the generator in NS-GAN.

Original: [Table]
Revised: [Table]

Original: First , compared to the baseline, case (3) equips with CFGMM additionally.
Revised: First , compared to the baseline, case (3) equips with CFGMM additionally.

Original: The great performance improvement achieved in case (3) powerfully demonstrates the effectiveness of CFGMM.
Revised: The great performance improvement achieved in case (3) powerfully demonstrates the effectiveness of CFGMM.

Original: To further prove the effectivity of CFGMM on introducing temporal information, we propose case (2) by removing the reference to the adjacent ground truth in
Revised: To further prove the effectivity of CFGMM on introducing temporal information, we propose case (2) by removing the reference to the adjacent ground truth in

Original: CFGMM.
Revised: CFGMM.

Original: Compared to case (3), case (2) encounters obvious performance degradation which demonstrates that our CFGMM can effectively utilize inter-frame information to generate high-quality pseudo labels.
Revised: Compared to case (3), case (2) encounters obvious performance degradation which demonstrates that our CFGMM can effectively utilize inter-frame information to generate high-quality pseudo labels.

Original: Second , to prove the effect of UADDM on strengthening the learning of difficult scenarios, we add UADDM (denoted as case (4)) based on case (3).
Revised: Second , to prove the effect of UADDM on strengthening the learning of difficult scenarios, we add UADDM (denoted as case (4)) based on case (3).

Original: Compared to case (3), the performance improves by 14% and 15% on DAVIS and MCL respectively in terms of MAE.
Revised: Compared to case (3), the performance improves by 14% and 15% on DAVIS and MCL respectively in terms of MAE.

Original: Moreover , we directly test the performance of pseudo labels on the DAVIS dataset, shown in Table.
Revised: Moreover , we directly test the performance of pseudo labels on the DAVIS dataset, shown in Table.

Original: Specifically, we train case (1) to case (4) with 10% of the ground truth in DAVIS and then generate pseudo-labels for the remaining 90% of the dataset.
Revised: Specifically, we train case (1) to case (4) with 10% of the ground truth in DAVIS and then generate pseudo-labels for the remaining 90% of the dataset.

Original: The experimental results show that the modules in UGPLG can gradually improve the performance of pseudo labels, finally reaching an impressive performance of 0.007 on MAE.
Revised: The experimental results show that the modules in UGPLG can gradually improve the performance of pseudo labels, finally reaching an impressive performance of 0.007 on MAE.

Original: At last , we provide the corresponding visualization results in Fig.
Revised: At last , we provide the corresponding visualization results in Fig.

Original: It can be found that the pseudo labels are getting more consistent temporally.
Revised: It can be found that the pseudo labels are getting more consistent temporally.

Original: The boundaries of the pseudo labels are getting clearer and the content of salient objects is getting more complete.
Revised: The boundaries of the pseudo labels are getting clearer and the content of salient objects is getting more complete.

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: The Effectiveness of NS-GAN.
Revised: The Effectiveness of NS-GAN.

Original: To highlight the importance of the noise suppression mechanism, we conduct a comparative experiment between an ordinary GAN [36] (denoted as case (5)) and NSGAN (denoted as case (6)), shown in Table.
Revised: To highlight the importance of the noise suppression mechanism, we conduct a comparative experiment between an ordinary GAN [36] (denoted as case (5)) and NSGAN (denoted as case (6)), shown in Table.

Original: It can be observed that under the condition that case (5) has achieved a great performance, our NS-GAN still creates a further performance improvement which demonstrates the effectiveness of further suppressing the noise of high-quality pseudo labels.
Revised: It can be observed that under the condition that case (5) has achieved a great performance, our NS-GAN still creates a further performance improvement which demonstrates the effectiveness of further suppressing the noise of high-quality pseudo labels.

Original: Case (6a) and case (6b) in Fig.
Revised: Case (6a) and case (6b) in Fig.

Original: 5 show the confidence maps generated by the discriminator at different training stages for case (4).
Revised: 5 show the confidence maps generated by the discriminator at different training stages for case (4).

Original: As the training process goes on, the discriminator can accurately identify noise areas, ensuring that pseudo labels always provide positive guidance to the saliency model.
Revised: As the training process goes on, the discriminator can accurately identify noise areas, ensuring that pseudo labels always provide positive guidance to the saliency model.

Original: [Table]
Revised: [Table]

Original: The Effectiveness of Multiple Output
Revised: The Effectiveness of Multiple Output

Original: Streams.
Revised: Streams.

Original: To explore the importance of all output streams in the generator, we compare the performance differences of supervised training under four output configurations, shown in Table.
Revised: To explore the importance of all output streams in the generator, we compare the performance differences of supervised training under four output configurations, shown in Table.

Original: F, R,
Revised: F, R,

Original: O are fused features branch, rgb features branch, and flow features branch, corresponding to Fuse pred, RGB pred, OPT pred in Fig.
Revised: O are fused features branch, rgb features branch, and flow features branch, corresponding to Fuse pred, RGB pred, OPT pred in Fig.

Original: 3 of the paper, respectively.
Revised: 3 of the paper, respectively.

Original: The training set is 20% of annotated frames.
Revised: The training set is 20% of annotated frames.

Original: As we can see that the outputs we employ for the generator help the model achieve the best performance.
Revised: As we can see that the outputs we employ for the generator help the model achieve the best performance.

Original: Different Ratios of Ground Truth to Pseudo Labels.
Revised: Different Ratios of Ground Truth to Pseudo Labels.

Original: We set up multiple comparative experiments to verify the effectiveness of our method on different ratios of ground truths (GT) to pseudo labels (PL).
Revised: We set up multiple comparative experiments to verify the effectiveness of our method on different ratios of ground truths (GT) to pseudo labels (PL).

Original: Table.
Revised: Table.

Original: 6 shows that in all three configurations, our method using 50% pseudo labels already achieves the results that are comparable to the fully-supervised VSOD methods, which fully demonstrates the superiority of our method.
Revised: 6 shows that in all three configurations, our method using 50% pseudo labels already achieves the results that are comparable to the fully-supervised VSOD methods, which fully demonstrates the superiority of our method.

Original: Finally, since the training time with 90% pseudo-labels is more than double that with 50% pseudo labels and considering the trade-off between performance and training time, we chose the configuration M using 50%
Revised: Finally, since the training time with 90% pseudo-labels is more than double that with 50% pseudo labels and considering the trade-off between performance and training time, we chose the configuration M using 50%

Original: [Table]
Revised: [Table]

Original:  Conclusion
Revised:  Conclusion

Original: In this paper, we strive to face the challenge of error-prone pseudo labels are usually detrimental to final detection.
Revised: In this paper, we strive to face the challenge of error-prone pseudo labels are usually detrimental to final detection.

Original: We propose an Uncertainty-Guided Pseudo Label Generator (UGPLG), which makes full use of temporal information to generate temporally consistent pseudo labels, and enhances the learning of uncertain regions to improve the robustness of pseudo labels.
Revised: We propose an Uncertainty-Guided Pseudo Label Generator (UGPLG), which makes full use of temporal information to generate temporally consistent pseudo labels, and enhances the learning of uncertain regions to improve the robustness of pseudo labels.

Original: To screen out the noise that still exists in the pseudo labels, we further propose an adversarial learning strategy for noise suppression to ensure that the pseudo labels can always guide the network positively.
Revised: To screen out the noise that still exists in the pseudo labels, we further propose an adversarial learning strategy for noise suppression to ensure that the pseudo labels can always guide the network positively.

Original: Experimental results demonstrate that our method outperforms the existing semi-supervised VSOD method and partial fully-supervised VSOD model while using only 5% labeled data used by the SOTA fullysupervised methods
Revised: Experimental results demonstrate that our method outperforms the existing semi-supervised VSOD method and partial fully-supervised VSOD model while using only 5% labeled data used by the SOTA fullysupervised methods

Original: Acknowledgements.
Revised: Acknowledgements.

Original: This work was supported by the National Natural Science Foundation of
Revised: This work was supported by the National Natural Science Foundation of

Original: China (62172070 and 61976035), the Central Government Guided Local Science and Technology
Revised: China (62172070 and 61976035), the Central Government Guided Local Science and Technology

Original: Development Funds of Liaoning Province (2022JH6/100100028), the Natural Science Foundation of
Revised: Development Funds of Liaoning Province (2022JH6/100100028), the Natural Science Foundation of

Original: Liaoning Province (2021-MS-123) and CAAI-Huawei MindSpore open fund (CAAIXSJLJJ-2022014C)
Revised: Liaoning Province (2021-MS-123) and CAAI-Huawei MindSpore open fund (CAAIXSJLJJ-2022014C)

Original: Checklist
Revised: Checklist

Original: The checklist follows the references.
Revised: The checklist follows the references.

Original: Please read the checklist guidelines carefully for information on how to answer these questions.
Revised: Please read the checklist guidelines carefully for information on how to answer these questions.

Original: For each question, change the default [TODO] to [Yes] , [No] , or [N/A] .
Revised: For each question, change the default [TODO] to [Yes] , [No] , or [N/A] .

Original: You are strongly encouraged to include a justification to your answer , either by referencing the appropriate section of your paper or providing a brief inline description.
Revised: You are strongly encouraged to include a justification to your answer , either by referencing the appropriate section of your paper or providing a brief inline description.

Original: For example:
Revised: For example:

Original: • Did you include the license to the code and datasets?
Revised: • Did you include the license to the code and datasets?

Original: [Yes] See Section ?? .
Revised: [Yes] See Section ?? .

Original: • Did you include the license to the code and datasets?
Revised: • Did you include the license to the code and datasets?

Original: [No] The code and the data are proprietary.
Revised: [No] The code and the data are proprietary.

Original: • Did you include the license to the code and datasets?
Revised: • Did you include the license to the code and datasets?

Original: [N/A]
Revised: [N/A]

Original: Please do not modify the questions and only use the provided macros for your answers.
Revised: Please do not modify the questions and only use the provided macros for your answers.

Original: Note that the
Revised: Note that the

Original: Checklist section does not count towards the page limit.
Revised: Checklist section does not count towards the page limit.

Original: In your paper, please delete this instructions block and only keep the Checklist section heading above along with the questions/answers below.
Revised: In your paper, please delete this instructions block and only keep the Checklist section heading above along with the questions/answers below.

Original: For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope?
Revised: For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope?

Original: [Yes] (b) Did you describe the limitations of your work?
Revised: [Yes] (b) Did you describe the limitations of your work?

Original: [No] (c) Did you discuss any potential negative societal impacts of your work?
Revised: [No] (c) Did you discuss any potential negative societal impacts of your work?

Original: [No] (d) Have you read the ethics review guidelines and ensured that your paper conforms to them?
Revised: [No] (d) Have you read the ethics review guidelines and ensured that your paper conforms to them?

Original: [Yes] 2.
Revised: [Yes] 2.

Original: If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results?
Revised: If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results?

Original: [Yes] (b) Did you include complete proofs of all theoretical results?
Revised: [Yes] (b) Did you include complete proofs of all theoretical results?

Original: [Yes] 3.
Revised: [Yes] 3.

Original: If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?
Revised: If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?

Original: [Yes] see Sec.
Revised: [Yes] see Sec.

Original: 4.1, Sec. 4.
Revised: 4.1, Sec. 4.

Original: (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)?
Revised: (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)?

Original: [Yes] (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?
Revised: [Yes] (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?

Original: [No] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)?
Revised: [No] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)?

Original: [Yes] 4.
Revised: [Yes] 4.

Original: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators?
Revised: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators?

Original: [Yes] (b) Did you mention the license of the assets?
Revised: [Yes] (b) Did you mention the license of the assets?

Original: [No] (c) Did you include any new assets either in the supplemental material or as a URL?
Revised: [No] (c) Did you include any new assets either in the supplemental material or as a URL?

Original: [No] (d) Did you discuss whether
Revised: [No] (d) Did you discuss whether

Original: and how consent was obtained from people whose data you’re using/curating?
Revised: and how consent was obtained from people whose data you’re using/curating?

Original: [No] (e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content?
Revised: [No] (e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content?

Original: [No] 5.
Revised: [No] 5.

Original: If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable?
Revised: If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable?

Original: [N/A] (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable?
Revised: [N/A] (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable?

Original: [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation?
Revised: [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation?

Original: [N/A]
Revised: [N/A]