Original: Cross-Image Context for Single Image Inpainting
Revised: Cross-Image Context for Single Image Inpainting

Original: Anonymous Author(s)
Revised: Anonymous Author(s)

Original: AfﬁliationAddress email
Revised: AfﬁliationAddress email

Original: Abstract
Revised: Abstract

Original: Visual context is of crucial importance for image inpainting.
Revised: Visual context is of crucial importance for image inpainting.

Original: The contextualinformation captures the appearance and semantic correlation between the imageregions, helping to propagate the information of the complete regions for reasoningthe content of the corrupted regions.
Revised: The contextualinformation captures the appearance and semantic correlation between the imageregions, helping to propagate the information of the complete regions for reasoningthe content of the corrupted regions.

Original: Many inpainting methods compute the visualcontext based on the regions within the single image.
Revised: Many inpainting methods compute the visualcontext based on the regions within the single image.

Original: In this paper, we proposethe Cross-Image Context Memory (CICM) for learning and using the cross-imagecontext to recover the corrupted regions.
Revised: In this paper, we proposethe Cross-Image Context Memory (CICM) for learning and using the cross-imagecontext to recover the corrupted regions.

Original: CICM consists of multiple sets of thecross-image features learned from the image regions with different visual patterns.
Revised: CICM consists of multiple sets of thecross-image features learned from the image regions with different visual patterns.

Original: The regional features are learned across different images, thus providing richercontext that beneﬁts the inpainting task.
Revised: The regional features are learned across different images, thus providing richercontext that beneﬁts the inpainting task.

Original: The experimental results demonstratethe effectiveness and generalization of CICM, which achieves state-of-the-artperformances on various datasets for single image inpainting.
Revised: The experimental results demonstratethe effectiveness and generalization of CICM, which achieves state-of-the-artperformances on various datasets for single image inpainting.

Original:  Introduction
Revised:  Introduction

Original: Image inpainting requires understanding the object appearances and semantic categories in the images.
Revised: Image inpainting requires understanding the object appearances and semantic categories in the images.

Original: The recent success of image inpainting lies in the end-to-end training of the deep networks, which aregood at learning the appearance and semantic features from the large-scale data.
Revised: The recent success of image inpainting lies in the end-to-end training of the deep networks, which aregood at learning the appearance and semantic features from the large-scale data.

Original: Generally, the inpainting networks [1, 2, 3, 4, 5, 6, 7, 8] yield the regional features that capture theappearance and semantic correlation between the image regions, thus providing the visual context.
Revised: Generally, the inpainting networks [1, 2, 3, 4, 5, 6, 7, 8] yield the regional features that capture theappearance and semantic correlation between the image regions, thus providing the visual context.

Original: Based on the visual context, the relevant information of the complete regions can be propagatedto the corrupted regions, whose contents are consequently reasoned.
Revised: Based on the visual context, the relevant information of the complete regions can be propagatedto the corrupted regions, whose contents are consequently reasoned.

Original: Conventionally, the contextpropagation takes place between the regions within the isolated image.
Revised: Conventionally, the contextpropagation takes place between the regions within the isolated image.

Original: However, the existing methods,which heavily rely on the visual context, face a dilemma, where the corrupted regions naturally lackthe visual information for computing the reliable context.
Revised: However, the existing methods,which heavily rely on the visual context, face a dilemma, where the corrupted regions naturally lackthe visual information for computing the reliable context.

Original: It lets the learned context lose the criticalrelationships between the image regions, eventually degrading the inpainting performance.
Revised: It lets the learned context lose the criticalrelationships between the image regions, eventually degrading the inpainting performance.

Original: In this paper, we advocate the idea of learning the cross-image context to recover the corrupted imageregions.
Revised: In this paper, we advocate the idea of learning the cross-image context to recover the corrupted imageregions.

Original: Alongside the inpainting network, we construct an external memory, where we store theregional features of the complete regions across different images.
Revised: Alongside the inpainting network, we construct an external memory, where we store theregional features of the complete regions across different images.

Original: For inpainting, we search CICM toachieve the regional features, which are potentially relevant to the corrupted regions.
Revised: For inpainting, we search CICM toachieve the regional features, which are potentially relevant to the corrupted regions.

Original: Rather thandepending on the regional features learned from a single image, we employ the external memory toprovide richer cross-image context.
Revised: Rather thandepending on the regional features learned from a single image, we employ the external memory toprovide richer cross-image context.

Original: It allows the regional information from different images to bepropagated to the corrupted regions in an image, where the lost contents are better restored.
Revised: It allows the regional information from different images to bepropagated to the corrupted regions in an image, where the lost contents are better restored.

Original: Speciﬁcally, we propose the external memory,  Cross-Image Context Memory (CICM), which canbe equipped with the popular inpainting networks.
Revised: Speciﬁcally, we propose the external memory, named Cross-Image Context Memory (CICM), whichcan be equipped with the popular inpainting networks.

Original: CICM consists of multiple feature sets.
Revised: CICM consists of multiple feature sets.

Original: Eachset contains the cross-image features. Context Generalization injects the regional features, whichare learned from different images, into the cross-image features.
Revised: We usethe Context Generalization to inject the regional features, which are learned from different images,into the feature sets of CICM.

Original: We let the cross-image features indifferent sets have diversity. Thus, we allow different sets  capture a richer cross-image context.
Revised: We let the feature sets have diversity, thus allowing the regionalfeatures in different sets to capture the cross-image context as rich as possible.

Original: Inthe same set, we encourage the cross-image features to contain the visual information of the similarimage regions. This facilitates Context Augmentation for ﬁnding the consistent cross-image featuresfrom CICM. Finally, we use the cross-image features to recover the corrupted regions.[Figure]
Revised: In the same set,we encourage the regional features to contain the visual information of the similar image regions,facilitating the Context Augmentation that helps to ﬁnd the consistent regional features from CICM,where the cross-image context augments the corrupted regions.

Original: feature set l … …… … …… … … …… … feature set l + feature set L setset l set l + set L … ……

Original: [Figure]

Original: We intensively evaluate CICM on the datasets (i.e., Places

Revised: We intensively evaluate CICM on the datasets (i.e., Places

Original: [9] and CelebA [10]) for image inpainting,where we achieve the state-of-the-art performances.
Revised: [9] and CelebA [10]) for image inpainting,where we achieve the state-of-the-art performances.

Original: Furthermore, we justify the generalization of
Revised: Furthermore, we justify the generalization of

Original: CICM, which can be applied to learn the cross-image context from not only the low-level appearancesbut also the high-level semantic information of the image regions.
Revised: CICM, which can be applied to learn the cross-image context from not only the low-level appearancesbut also the high-level semantic information of the image regions.

Original: With the help of CICM, we yieldbetter results than the recent inpainting methods, on the datasets (i.e., Cityscapes [11] and Outdoor
Revised: With the help of CICM, we yieldbetter results than the recent inpainting methods, on the datasets (i.e., Cityscapes [11] and Outdoor

Original: Scenes [12]) that provide the semantic information of object categories.
Revised: Scenes [12]) that provide the semantic information of object categories.

Original:  Related Work
Revised:  Related Work2.1 Visual Context for Image Inpainting

Original: 2.1 Visual Context for Image Inpainting

Original: Many inpainting methods have been proposed in recent years [13, 14, 15, 16, 17, 18, 19, 20, 21, 22].
Revised: Many inpainting methods have been proposed in recent years [13, 14, 15, 16, 17, 18, 19, 20, 21, 22].

Original: Pathak et al.
Revised: Pathak et al.

Original: [23] propose the deep adversarial learning that harnesses visual context for imageinpainting.
Revised: [23] propose the deep adversarial learning that harnesses visual context for imageinpainting.

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

Original: [24] and Yu et al.
Revised: [24] and Yu et al.

Original: [25] propose the partial convolution and gated convolutionto achieve visual context for recovering the corrupted image regions with irregular shapes.
Revised: [25] propose the partial convolution and gated convolutionto achieve visual context for recovering the corrupted image regions with irregular shapes.

Original: Liu etal.
Revised: Liu etal.

Original: [26] focus on repairing the texture and structure of the images.
Revised: [26] focus on repairing the texture and structure of the images.

Original: Zeng et al.
Revised: Zeng et al.

Original: [27] ﬁll the corruptedregions with the help of cross-layer attention.
Revised: [27] ﬁll the corruptedregions with the help of cross-layer attention.

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

Original: [28] propose the recurrent feature reasoningto improve the inpainting results.
Revised: [28] propose the recurrent feature reasoningto improve the inpainting results.

Original: In addition, semantic information has also been used to guide therecovery of corrupted regions.
Revised: In addition, semantic information has also been used to guide therecovery of corrupted regions.

Original: Song et al.
Revised: Song et al.

Original: [29] use the semantic segmentation maps for calibratingthe image restoration.
Revised: [29] use the semantic segmentation maps for calibratingthe image restoration.

Original: Liao et al.
Revised: Liao et al.

Original: [30] employ the segmentation results at each layer of the decoderfor constraining the inpainting results.
Revised: [30] employ the segmentation results at each layer of the decoderfor constraining the inpainting results.

Original: Most of the above methods rely on the single-image context.
Revised: Most of the above methods rely on the single-image context.

Original: In contrast, we propose the inpaintingmethod based on the cross-image context, which is more powerful in terms of capturing the underlyingrelationship between lost image contents and recovering the corrupted regions.
Revised: In contrast, we propose the inpaintingmethod based on the cross-image context, which is more powerful in terms of capturing the underlyingrelationship between lost image contents and recovering the corrupted regions.

Original: 2.2 Memorized Context for Visual Understanding
Revised: 2.2 Memorized Context for Visual Understanding

Original: Recent studies demonstrate the importance of the memorized context for the visual understandingtasks [31, 32, 33, 34, 35, 36, 37, 38].
Revised: Recent studies demonstrate the importance of the memorized context for the visual understandingtasks [31, 32, 33, 34, 35, 36, 37, 38].

Original: He et al.
Revised: He et al.

Original: [39] propose the momentum contrast to establish alarge and consistent memory for unsupervised pre-training.
Revised: [39] propose the momentum contrast to establish alarge and consistent memory for unsupervised pre-training.

Original: Jeong et al.
Revised: Jeong et al.

Original: [40] propose the unsupervisedimage-to-image translation framework that propagates the instance style information across images.
Revised: [40] propose the unsupervisedimage-to-image translation framework that propagates the instance style information across images.

Original: Wang et al.
Revised: Wang et al.

Original: [41] explore a large visual data space for memorizing the semantic object information.
Revised: [41] explore a large visual data space for memorizing the semantic object information.

Original: Xuet al.
Revised: Xuet al.

Original: [42] design the memory that stores the complete regions for guiding texture generation.
Revised: [42] design the memory that stores the complete regions for guiding texture generation.

Original: Feng etal.
Revised: Feng etal.

Original: [43] model the semantic prior shared across images to reasonably recover the corrupted regions.
Revised: [43] model the semantic prior shared across images to reasonably recover the corrupted regions.

Original: The memories of the above methods store the single-image context or the cross-image semanticinformation.
Revised: The memories of the above methods store the single-image context or the cross-image semanticinformation.

Original: They miss the appearance details shared across various images.
Revised: They miss the appearance details shared across various images.

Original: In this paper, we construct the memory that contains the cross-image context learned from different images, allowingricher context to improve the inpainting results.
Revised: In this paper, we construct the memory that contains the cross-image context learned from different images, allowingricher context to improve the inpainting results.

Original:  Method Overview
Revised:  Method Overview

Original: We illustrate the inpainting network with CICM in Figure 1.
Revised: We illustrate the inpainting network with CICM in Figure 1.

Original: Given the input image I ∈
Revised: Given the input image I ∈

Original: R H
Revised: R H

Original: × W × 3 ,the backbone network extracts the convolutional feature maps at different layers.
Revised: × W × 3 ,the backbone network extracts the convolutional feature maps at different layers.

Original: At a layer, thefeature map F ∈ R H × W × C is divided into N regions, where we achieve the regional features{ F n ∈ R C | n = 1 , ..., N } .
Revised: At a layer, thefeature map F ∈ R H × W × C is divided into N regions, where we achieve the regional features{ F n ∈ R C | n = 1 , ..., N } .

Original: H × W and C represent the spatial resolution and the feature channels.
Revised: H × W and C represent the spatial resolution and the feature channels.

Original: CICM has the feature sets { S l | l = 1 , ..., L } .
Revised: CICM has the feature sets { S l | l = 1 , ..., L } .

Original: S l = { C l,k ∈ R C | k
Revised: S l = { C l,k ∈ R C | k

Original: = 1 , ..., K } is the l th set thatcontains K cross-image features.
Revised: = 1 , ..., K } is the l th set thatcontains K cross-image features.

Original: C l,k is the k th cross-image features.
Revised: C l,k is the k th cross-image features.

Original: From { F n ∈ R C | n =1 , ..., N } , we choose the regional features of the complete regions and input them into the Context
Revised: From { F n ∈ R C | n =1 , ..., N } , we choose the regional features of the complete regions and input them into the Context

Original: Generalization , which constructs the cross-image feature sets.
Revised: Generalization , which constructs the cross-image feature sets.

Original: The cross-image context provided by
Revised: The cross-image context provided by

Original: CICM is used by the Context Augmentation to recover the corrupted regions.
Revised: CICM is used by the Context Augmentation to recover the corrupted regions.

Original: Context Generalization As illustrated in Figure 2(a), there is a set of anchor features { A l ∈ 83 R C | l = 1 , ..., L } in CICM.
Revised: Context Generalization As illustrated in Figure 2(a), there is a set of anchor features { A l ∈ 83 R C | l = 1 , ..., L } in CICM.

Original: A l is associated with the feature set S l .
Revised: A l is associated with the feature set S l .

Original: Given the regional feature F n ofthe complete region, we measure its similarity with each anchor feature.
Revised: Given the regional feature F n ofthe complete region, we measure its similarity with each anchor feature.

Original: Given the anchor feature withthe highest similarity, we choose the associated feature set, where we inject the new information ofthe regional feature F n into the cross-image features.
Revised: Given the anchor feature withthe highest similarity, we choose the associated feature set, where we inject the new information ofthe regional feature F n into the cross-image features.

Original: We also update the anchor feature to representthe entire feature set.
Revised: We also update the anchor feature to representthe entire feature set.

Original: During training, we minimize the intra-set similarity between the cross-imagefeatures in the same set.
Revised: During training, we minimize the intra-set similarity between the cross-imagefeatures in the same set.

Original: The inter-set similarity between the anchor features is maximized.
Revised: The inter-set similarity between the anchor features is maximized.

Original: Context Augmentation CICM provides the cross-image features for the context augmentation,as illustrated in Figure 2(b).
Revised: Context Augmentation CICM provides the cross-image features for the context augmentation,as illustrated in Figure 2(b).

Original: For the regional feature F m of the corrupted region, we measure itssimilarity with every anchor feature.
Revised: For the regional feature F m of the corrupted region, we measure itssimilarity with every anchor feature.

Original: Again, we choose the anchor feature with the highest similarity,and use the cross-image features inside the associated feature set to augment the regional feature
Revised: Again, we choose the anchor feature with the highest similarity,and use the cross-image features inside the associated feature set to augment the regional feature

Original: F m .
Revised: F m .

Original: This is done by injecting the context information of the cross-image features into F m .
Revised: This is done by injecting the context information of the cross-image features into F m .

Original: Theaugmented feature is used to predict the lost content of the region.
Revised: Theaugmented feature is used to predict the lost content of the region.

Original:  Cross-Image Context Memory
Revised:  Cross-Image Context Memory

Original: Below, we elaborate on the architecture of CICM.
Revised: Below, we elaborate on the architecture of CICM.

Original: We use the Context Generalization to learn the crossimage features, which are stored in different feature sets of CICM.
Revised: We use the Context Generalization to learn the crossimage features, which are stored in different feature sets of CICM.

Original: The Context Augmentation ﬁndsthe cross-image features from CICM, offering the useful cross-image context for image inpainting.
Revised: The Context Augmentation ﬁndsthe cross-image features from CICM, offering the useful cross-image context for image inpainting.

Original: 4.1 Context Generalization
Revised: 4.1 Context Generalization

Original: As illustrated in Figure 2(a), the context generalization extracts the regional features from thecomplete regions of different images.
Revised: As illustrated in Figure 2(a), the context generalization extracts the regional features from thecomplete regions of different images.

Original: It injects these regional features into CICM, where the regionalinformation is generalized as the cross-image features.
Revised: It injects these regional features into CICM, where the regionalinformation is generalized as the cross-image features.

Original: In Figure 2(c), we provide more details of the context generalization.
Revised: In Figure 2(c), we provide more details of the context generalization.

Original: We denote F n as the regionalfeature of the n th region, which is complete in the input image I .
Revised: We denote F n as the regionalfeature of the n th region, which is complete in the input image I .

Original: We follow the dot-productfashion to measure the similarity between the regional feature F n and each anchor feature in the set{ A l ∈ R C | l = 1 , ..., L } , and choose the most relevant feature set S l as:
Revised: We follow the dot-productfashion to measure the similarity between the regional feature F n and each anchor feature in the set{ A l ∈ R C | l = 1 , ..., L } , and choose the most relevant feature set S l as:

Original: [Equation]
Revised: [Equation]

Original: We inject the regional feature F n into the set S l , where the cross-image feature C l,k is updated as:
Revised: We inject the regional feature F n into the set S l , where the cross-image feature C l,k is updated as:

Original: [Equation]
Revised: [Equation]

Original: where ← means the update by overwriting.
Revised: where ← means the update by overwriting.

Original: We use the dot product to measure the similarityλ n,l,k ∈ [0 , 1] between the regional feature F n and every cross-image feature in S l .
Revised: We use the dot product to measure the similarityλ n,l,k ∈ [0 , 1] between the regional feature F n and every cross-image feature in S l .

Original: The update of 110 cross-image features take place during the network training.
Revised: The update of 110 cross-image features take place during the network training.

Original: Note that the network training allows theregional features of different images to be injected into the cross-image features, whose generalizationpower is strengthened.
Revised: Note that the network training allows theregional features of different images to be injected into the cross-image features, whose generalizationpower is strengthened.

Original: To improve the computational efﬁciency, we conduct the batch-wise update on the anchor features.
Revised: To improve the computational efﬁciency, we conduct the batch-wise update on the anchor features.

Original: After injecting the regional features of a mini-batch into the cross-image features in CICM, we update [Figure] … s s … CICM …
Revised: After injecting the regional features of a mini-batch into the cross-image features in CICM, we update the anchor feature A l as:

Original: [Figure]

Original: argmax … … …… set l …

Original: CICM … … … …… set l s argmax

Original: [Figure]

Original: ratio

Original: [Figure]

Original: [Figure]

Original: [Figure]

Original: Figure 2: (a) Given a regional feature of the complete region, the context generation measures the similarity with every anchor feature, and selects the most similar anchor feature and the associated feature set.

Original: As illustrated in (c), the selected anchor feature and the cross-image features are updated by the regional information.

Original: (b) Given a regional feature of the corrupted region, the context augmentation also measures the similarity with every anchor feature.

Original: It select the feature set, where the cross-image features augment the regional feature, as illustrated in (d).

Original: the anchor feature A l as:

Original: [Equation]
Revised: [Equation]

Original: where we use the momentum factor β= 0 . 5 to control the update of the anchor feature.
Revised: where we use the momentum factor β to control the update of the anchor feature.

Original: 4.2 Context Augmentation
Revised: 4.2 Context Augmentation

Original: As illustrated In Figure 2(b), the context augmentation takes input as the regional features of thecorrupted regions in the image.
Revised: As illustrated In Figure 2(b), the context augmentation takes input as the regional features of thecorrupted regions in the image.

Original: The regional features are used to measure the similarities with theanchor features.
Revised: The regional features are used to measure the similarities with theanchor features.

Original: Based on the similarities for each regional feature, we ﬁnd the most relevant anchorfeature and the associate set of the cross-image features.
Revised: Based on the similarities for each regional feature, we ﬁnd the most relevant anchorfeature and the associate set of the cross-image features.

Original: We inject the generalized context of thecross-image features into the regional features of the corrupted regions, achieving the augmentedfeatures for recovering the lost contents.
Revised: We inject the generalized context of thecross-image features into the regional features of the corrupted regions, achieving the augmentedfeatures for recovering the lost contents.

Original: The details of the context augmentation is illustrated Figure 2(d).
Revised: The details of the context augmentation is illustrated Figure 2(d).

Original: Given the regional feature F mof the m th region, we measure its similarities with the anchor features, choosing the most similaranchor feature A l and the associated feature set S l , as:
Revised: Given the regional feature F mof the m th region, we measure its similarities with the anchor features, choosing the most similaranchor feature A l and the associated feature set S l , as:

Original: [Equation]
Revised: [Equation]

Original: where we use all of the cross-image features in the set S l to yield the augmented feature R m ∈ R C .
Revised: where we use all of the cross-image features in the set S l to yield the augmented feature R m ∈ R C .

Original: This is done by weighting and adding the cross-image features { C l,k ∈ R C | k = 1 , ..., K } to F m .
Revised: This is done by weighting and adding the cross-image features { C l,k ∈ R C | k = 1 , ..., K } to F m .

Original: The corrupted/complete status of the image region is unavailable for the inpainting task.
Revised: The corrupted/complete status of the image region is unavailable for the inpainting task.

Original: Intuitively,the corrupted region requires rich context for reasoning the missing content.
Revised: Intuitively,the corrupted region requires rich context for reasoning the missing content.

Original: On the other hand, thecomplete region needs less context to preserve its speciﬁc property.
Revised: On the other hand, thecomplete region needs less context to preserve its speciﬁc property.

Original: Thus, for the m th region in theimage, we input the regional feature F m into the convolutional layer to estimate the corrupted ratio 133 γ m ∈ [0 , 1] .
Revised: Thus, for the m th region in theimage, we input the regional feature F m into the convolutional layer to estimate the corrupted ratio 133 γ m ∈ [0 , 1] .

Original: As formulated in Eq.
Revised: As formulated in Eq.

Original: (4), the corrupted ratio is used to weight the regional feature and thecross-image features for computing the augmented feature.
Revised: (4), the corrupted ratio is used to weight the regional feature and thecross-image features for computing the augmented feature.

Original: A larger ratio indicates a more corruptedregion, whose regional representation is signiﬁcantly augmented by the context information.
Revised: A larger ratio indicates a more corruptedregion, whose regional representation is signiﬁcantly augmented by the context information.

Original: Finally, the augmented feature R m is input into the convolutional layer for predicting the pixel valuesof the m th region.
Revised: Finally, the augmented feature R m is input into the convolutional layer for predicting the pixel valuesof the m th region.

Original: Based on the set of augmented features { R m | m = 1 , ..., N } , we produce theinpainting result I (cid:48)
Revised: Based on the set of augmented features { R m | m = 1 , ..., N } , we produce theinpainting result I (cid:48)

Original: ∈
Revised: ∈

Original: R H
Revised: R H

Original: × W × 3 .
Revised: × W × 3 .

Original: 4.3 Training Objective
Revised: 4.3 Training Objective

Original: During the network training, we resort to L2-norm and the adversarial loss [44] for measuring theinpainting error as:
Revised: During the network training, we resort to L2-norm and the adversarial loss [44] for measuring theinpainting error as:

Original: [Equation]
Revised: [Equation]

Original: (5) where
Revised: (5) where

Original: [Equation]
Revised: [Equation]

Original: In Eq.(5), I ∈
Revised: In Eq.(5), I ∈

Original: R H
Revised: R H

Original: × W × 3 is the ground-truth result.
Revised: × W × 3 is the ground-truth result.

Original: E x is the expectation with respect to x .
Revised: E x is the expectation with respect to x .

Original: In Eq.(6),
Revised: In Eq.(6),

Original: C is a discriminator network.
Revised: C is a discriminator network.

Original: We also use L2-norm for measuring the estimated corrupted ratio andthe ground-truth ratio of the image region as:
Revised: We also use L2-norm for measuring the estimated corrupted ratio andthe ground-truth ratio of the image region as:

Original: [Equation]
Revised: [Equation]

Original: where γ n and γ n are the estimated and ground-truth corrupted ratios for the n th region in the inputimage I .
Revised: where γ n and γ n are the estimated and ground-truth corrupted ratios for the n th region in the inputimage I .

Original: L ratio is minimized.
Revised: L ratio is minimized.

Original: To achieve γ n , we subtract the input image I from the ground-truthresult I , where the corrupted regions lead to larger differences of the pixel values.
Revised: To achieve γ n , we subtract the input image I from the ground-truthresult I , where the corrupted regions lead to larger differences of the pixel values.

Original: Within the n thregion, we accumulate and normalize the differences of the pixel values, ﬁnally yielding γ n .
Revised: Within the n thregion, we accumulate and normalize the differences of the pixel values, ﬁnally yielding γ n .

Original: As formulated in Eq.
Revised: As formulated in Eq.

Original: (8), we use the dot product between anchor features to measure the inter-setsimilarity, which is minimized to encourage different sets of cross-image features to capture morediverse context.
Revised: (8), we use the dot product between anchor features to measure the inter-setsimilarity, which is maximized to encourage different sets of cross-image features to capture morediverse context.

Original: In the same set, we maximize the intra-set similarity L intra between the anchorfeature and each cross-image feature, for encouraging the same set of cross-image features to capturemore consistent context.
Revised: In the same set, we minimize the intra-set similarity L intra between the anchorfeature and each cross-image feature, for encouraging the same set of cross-image features to capturemore consistent context.

Original: [Equation]
Revised: [Equation]

Original: With Eqs.
Revised: With Eqs.

Original: (5)–(8), we formulate the overall training objective as:
Revised: (5)–(8), we formulate the overall training objective as:

Original: [Equation]
Revised: [Equation]

Original: where λ 1 = 1 . 0 , λ 2 = 0 .

Original: 1 , λ 3 = 1 . 0 , λ 4 = 20 and λ 5 = 0 .

Original: We minimize the overall objective Lto optimize the network parameters.
Revised: where we minimize the overall objective L to optimize the network parameters.

Original:  Experiments
Revised:  Experiments

Original: We evaluate our approach on the Places2 [9] and CelebA [10] datasets.
Revised: We evaluate our approach on the Places2 [9] and CelebA [10] datasets.

Original: Places2 provides over 8Mimages taken from over 365 scenes for training, along with 30K images for testing.
Revised: Places2 provides over 8Mimages taken from over 365 scenes for training, along with 30K images for testing.

Original: It is used for theinternal study of our approach.
Revised: It is used for theinternal study of our approach.

Original: We also compare our approach with state-of-the-art methods on the
Revised: We also compare our approach with state-of-the-art methods on the

Original: CelebA dataset, which provides about 163K and 20K images, respectively, for training and testing.
Revised: CelebA dataset, which provides about 163K and 20K images, respectively, for training and testing.

Original: We use the Cityscapes [11] and Outdoor Scenes [12] to extensively justify the effectiveness of ourmethod.
Revised: We use the Cityscapes [11] and Outdoor Scenes [12] to extensively justify the effectiveness of ourmethod.

Original: These datasets provide the semantic object categories for assisting the inpainting task.
Revised: These datasets provide the semantic object categories for assisting the inpainting task.

Original: Cityscapes contains 5,000 street-view images, where 2,975 and 1,525 images are used for trainingand testing.
Revised: Cityscapes contains 5,000 street-view images, where 2,975 and 1,525 images are used for trainingand testing.

Original: Outdoor Scenes contains 9,900 training and 300 testing images.
Revised: Outdoor Scenes contains 9,900 training and 300 testing images.

Original: [Table] [Figure] To compare the inpainting performances of different methods, the corrupted images are generated bythe irregular masks [24, 25, 45].
Revised: To compare the inpainting performances of different methods, the corrupted images are generated bythe irregular masks [24, 25, 45].

Original: We divided irregular masks into three groups, where 0-20%, 20-40%,and 40-60% of the image areas are corrupted.
Revised: We divided irregular masks into four groups, where 0-20%, 20-40%, and 40-60% of the image areas are corrupted.

Original: We employ the peak signal-to-noise ratio (PSNR),structural similarity index (SSIM), L1,  perceptual similarity (LPIPS [46]), and fréchet inceptiondistance (FID [47]) as the evaluation metrics.
Revised: We employ the peak signal-to-noise ratio (PSNR),structural similarity index (SSIM), L1, and perceptual similarity (LPIPS [46]) as the evaluationmetrics.

Original: Note that PSNR, SSIM,L1, and FID measures thequality and realism of the restored image, while LPIPS measures the perceptual consistence betweenthe restored image and the ground-truth image.
Revised: Note that PSNR, SSIM, and L1 measures the quality of the restored image, while LPIPSmeasures the perceptual consistence between the restored image and the ground-truth image.

Original: Due to the limited space, we provide more details ofthe experimental setup in the supplementary material.
Revised: Due tothe limited space, we provide more details of the experimental setup in the supplementary material.

Original: 5.1 Internal Study
Revised: 5.1 Internal Study

Original: Sensitivity to Memory Capacity
Revised: Sensitivity to Memory Capacity

Original: The memory capacity of CICM is determined by the number offeature sets and the size of each set, which are set to 13 and 40 by default.
Revised: The memory capacity of CICM is determined by the number offeature sets and the size of each set, which are set to 13 and 40 by default.

Original: Below, we change thememory capacity and examine the effect on the inpainting performance.
Revised: Below, we change thememory capacity and examine the effect on the inpainting performance.

Original: In Figure 3(a), we ﬁx the size of each feature set as 40, and compare the inpainting performance by changing the number of feature sets L .
Revised: In Figure 3(a), we ﬁx the size of each feature set as 40, and compare the inpainting performance by changing the number of feature sets L .

Original: We choose the number from the set{ 1 , 3 , 5 , 7 , 9 , 11 , 13 , 15 , 17 } .
Revised: We choose the number from the set{ 1 , 3 , 5 , 7 , 9 , 11 , 13 , 15 , 17 } .

Original: With too less feature sets (e.g., L = 1 , 3 , 5 ), the diversity of crossimage context is unsatisfactorily captured, thus degrading the performance.
Revised: With too less feature sets (e.g., L = 1 , 3 , 5 ), the diversity of crossimage context is unsatisfactorily captured, thus degrading the performance.

Original: On the other hand,too many feature sets (e.g., L = 15 , 17 ) are redundant.
Revised: On the other hand,too many feature sets (e.g., L = 15 , 17 ) are redundant.

Original: They contain many cross-image features,which are computed based on similar image regions.
Revised: They contain many cross-image features,which are computed based on similar image regions.

Original: These cross-image features little improve theperformance but require extra testing time for each image.
Revised: These cross-image features little improve theperformance but require extra testing time for each image.

Original: Next, we change the size of each feature set (see Figure 3(b)).
Revised: Next, we change the size of each feature set (see Figure 3(b)).

Original: Here, the number of feature sets is ﬁxedas 13.
Revised: Here, the number of feature sets is ﬁxedas 13.

Original: The size is chosen from the set { 10 , 15 , 20 , 25 , 30 , 35 , 40 , 45 , 50 } .
Revised: The size is chosen from the set { 10 , 15 , 20 , 25 , 30 , 35 , 40 , 45 , 50 } .

Original: In the large feature sets, aportion of the cross-image features loss the opportunity of update, thus containing many meaninglesszeros.
Revised: In the large feature sets, aportion of the cross-image features loss the opportunity of update, thus containing many meaninglesszeros.

Original: Compared to the well-learned features, the zero features unnecessarily occupy the memoryspace but have less impact on the inpainting performance.
Revised: Compared to the well-learned features, the zero features unnecessarily occupy the memoryspace but have less impact on the inpainting performance.

Original: Variants of Context Generalization
Revised: Variants of Context Generalization

Original: In Table 1 (“Context Generalization”), we evaluate theeffectiveness of context generalization by comparing it with other alternatives.
Revised: In Table 1 (“Context Generalization”), we evaluate theeffectiveness of context generalization by comparing it with other alternatives.

Original: We examine the impact of the intra- and inter-set similarities, which guide the learning of the crossimage features, on the context generalization.
Revised: We examine the impact of the intra- and inter-set similarities, which guide the learning of the crossimage features, on the context generalization.

Original: We remove either the intra- or inter-set similarity(see “w/o intra” and “w/o inter”), thus disallowing the cross-image features in different or identicalsets to comprehensively capture the diverse or similar context.
Revised: We remove either the intra- or inter-set similarity(see “w/o intra” and “w/o inter”), thus disallowing the cross-image features in different or identicalsets to comprehensively capture the diverse or similar context.

Original: In this manner, we achieve lowerperformances than the full model trained with all of the similarities (see “intra & inter”).
Revised: In this manner, we achieve lowerperformances than the full model trained with all of the similarities (see “intra and inter”).

Original: We use different ways of updating the cross-image features for context generalization.
Revised: We use different ways of updating the cross-image features for context generalization.

Original: Given aregional feature, we ﬁnd the most relevant feature set, where all of the cross-image features areupdated (see “100% update”).
Revised: Given aregional feature, we ﬁnd the most relevant feature set, where all of the cross-image features areupdated (see “100% update”).

Original: We experiment with reducing the number of cross-image features tobe updated.
Revised: We experiment with reducing the number of cross-image features tobe updated.

Original: This is done by ranking the similarities between the regional feature and cross-imagefeatures, and selecting the top-50% (or even top-1) cross-image features for update.
Revised: This is done by ranking the similarities between the regional feature and cross-imagefeatures, and selecting the top-50% (or even top-1) cross-image features for update.

Original: Fewer crossimage features for update reduce the testing time.
Revised: Fewer crossimage features for update reduce the testing time.

Original: But the cross-image features without updated likelycontain outdated context information, letting themselves be inconsistent with the updated features inthe same set.
Revised: But the cross-image features without updated likelycontain outdated context information, letting themselves be inconsistent with the updated features inthe same set.

Original: They largely degrade the performances (see “top-1 update” and “top-50% update”).
Revised: They largely degrade the performances (see “top-1 update” and “top-50% update”).

Original: [Table]
Revised: [Table]

Original: Variants of Context Augmentation Given the cross-image features, we experiment with usingvarious ways of context augmentation, and report the results in Table 1 (“Context Augmentation”).
Revised: Variants of Context Augmentation Given the cross-image features, we experiment with usingvarious ways of context augmentation, and report the results in Table 1 (“Context Augmentation”).

Original: For the context augmentation, we estimate the corrupted ratios for controlling the context information,which is propagated from the cross-image features to the regional features.
Revised: For the context augmentation, we estimate the corrupted ratios for controlling the context information,which is propagated from the cross-image features to the regional features.

Original: We experiment withsetting the corrupted ratios to 1 unanimously.
Revised: We experiment withsetting the corrupted ratios to 1 unanimously.

Original: It means that the context information is fully propagatedto every regional feature.
Revised: It means that the context information is fully propagatedto every regional feature.

Original: The regional features of the complete regions are heavily affected by thecross-image features, yielding lower performances (see “w/o ratio”).
Revised: The regional features of the complete regions are heavily affected by thecross-image features, yielding lower performances (see “w/o ratio”).

Original: This is because the diversecross-image information is largely involved into the regional features, which are supposed to keeptheir original information for preserving the appearances of the complete regions in the result.
Revised: This is because the diversecross-image information is largely involved into the regional features, which are supposed to keeptheir original information for preserving the appearances of the complete regions in the result.

Original: We alsocompare the inpainting performances, which are achieved based on the estimated and ground-truthcorrupted ratios (see “w/ ratio” and “w/ GT”).
Revised: We alsocompare the inpainting performances, which are achieved based on the estimated and ground-truthcorrupted ratios (see “w/ ratio” and “w/ ground-truth”).

Original: Though the ground-truth corrupted ratios are moreaccurate than the estimated ones, the gap between the inpainting performances is reasonable.
Revised: Though the ground-truth corrupted ratios aremore accurate than the estimated ones, the gap between the inpainting performances is reasonable.

Original: Itdemonstrates the robustness of estimating the corrupted ratios.
Revised: Itdemonstrates the robustness of estimating the corrupted ratios.

Original: Moreover, we study the impact of changing the number of the cross-image features, which are usedby the context augmentation of the regional features.
Revised: Moreover, we study the impact of changing the number of the cross-image features, which are usedby the context augmentation of the regional features.

Original: Note that our full model resorts to all of thecross-image features in the relevant set for feature augmentation (see “100% aug”).
Revised: Note that our full model resorts to all of thecross-image features in the relevant set for feature augmentation (see “100% augmentation”).

Original: Based on thesimilarities between the cross-image features and the regional features, we select the top-1 andtop-50% of the cross-image features, respectively, for augmenting the regional features.
Revised: Basedon the similarities between the cross-image features and the regional features, we select the top-1 andtop-50% of the cross-image features, respectively, for augmenting the regional features.

Original: Apparently,it disallows the regional features of the corrupted regions to beneﬁt from richer cross-image context,consequently leading to worse inpainting results.
Revised: Apparently,it disallows the regional features of the corrupted regions to beneﬁt from richer cross-image context,consequently leading to worse inpainting results.

Original: [Table]
Revised: [Table]

Original: Different Ways of Using Image Context In Table 2, we compare CICM with different ways ofusing image context for image inpainting.
Revised: Different Ways of Using Image Context In Table 2, we compare CICM with different ways ofusing image context for image inpainting.

Original: Here, we divide the methods into two groups, whichrespectively use the single- and cross-image context for image inpainting.
Revised: Here, we divide the methods into two groups, whichrespectively use the single- and cross-image context for image inpainting.

Original: In Table 2 (“Single-Image Context”), we report the results of using k-means and the deep-learningbased RUC [48], which are the clustering methods for harnessing the single-image context in ourscenario.
Revised: In Table 2 (“Single-Image Context”), we report the results of using k-means and the deep-learningbased RUC [47], which are the clustering methods for harnessing the single-image context in ourscenario.

Original: For a single image, we use the clustering methods to divide the regional features of thecomplete regions into several clusters.
Revised: For a single image, we use the clustering methods to divide the regional features of thecomplete regions into several clusters.

Original: Based on the similarities with the cluster centers, we ﬁnd themost relevant regional features for recovering the corrupted regions.
Revised: Based on the similarities with the cluster centers, we ﬁnd themost relevant cross-image features for augmenting the regional features of the corrupted regions.

Original: Another variant of using thesingle-image context is to remove the feature sets of CICM and keep the anchor features only.
Revised: Another variant of using the single-image context is to remove the feature sets of CICM and keep theanchor features only.

Original: Theanchor features play in place of the cluster centers, which are produced by the clustering methods.
Revised: The anchor features play in place of the cluster centers, which are producedby the clustering methods.

Original: Compared to the cross-image context, the single-image context (see “k-means”, “RUC” and “anchoronly”) leads to lower performances.
Revised: Compared to the cross-image context, the single-image context (see“k-means”, “RUC” and “anchor only”) leads to lower performances.

Original: In Table 2 (“Cross-Image Context”)
Revised: In Table 2 (“Cross-Image Context”)

Original: [Table]
Revised: [Table]

Original: we experiment with merging the feature sets constructed by thecontext generalization.
Revised: we experiment with merging the feature sets constructed by thecontext generalization.

Original: This is done by removing the anchor features, only relying on the similaritiesbetween the cross-image features and the regional features for feature augmentation.
Revised: This is done by removing the anchor features, only relying on the similaritiesbetween the cross-image features and the regional features for feature augmentation.

Original: This methoduse the cross-image features, which contain discrepant visual context, to recover each corruptedregion.
Revised: This methoduse the cross-image features, which contain discrepant visual context, to recover each corruptedregion.

Original: Even though too discrepant visual context can be ﬁltered by the lower feature similarity, manycross-image features with diverse information still distract the inpainting of a corrupted region.
Revised: Even though too discrepant visual context can be ﬁltered by the lower feature similarity, manycross-image features with diverse information still distract the inpainting of a corrupted region.

Original: Itleads to worse results than CICM, which employs the anchor features to select the relevant context.
Revised: Itleads to worse results than CICM, which employs the anchor features to select the relevant context.

Original: [Table]
Revised: [Table]

Original: 5.2 External Evaluation
Revised: 5.2 External Evaluation

Original: Combination with Different Inpainting Networks To justify the generalization power of CICM,we equip CICM with different inpainting networks and report the change of performances in Table 3.
Revised: Combination with Different Inpainting Networks To justify the generalization power of CICM,we equip CICM with different inpainting networks and report the change of performances in Table 3.

Original: Apart from the baseline model, we choose RFR [28], JPG [49] [Table] and MISF [50] for evaluation.
Revised: Apart from the baseline model, we choose RFR [28], JPG [48], and MISF [49] for evaluation.

Original: Fora fair comparison, these methods use the lowest-resolution convolution feature maps to extractthe regional features, which are used for learning the cross-image features in CICM.
Revised: Fora fair comparison, these methods use the lowest-resolution convolution feature maps to extractthe regional features, which are used for learning the cross-image features in CICM.

Original: With CICM,different methods consistently improve the inpainting results on the test sets of Places2 and CelebA.
Revised: With CICM,different methods consistently improve the inpainting results on the test sets of Places2 and CelebA.

Original: Comparison with State-of-the-Art Methods In Table 4, we compare our method with state-ofthe-art methods [28,  49,50, 42, 43] on the test sets of Places2 and CelebA.We use a UNet with 15convolutional layers as the backbone. Among the compared methods, TMAD [42] and SRM [43]also construct the memory banks, which store the useful context for recovering the corrupted regions.
Revised: Comparison with State-of-the-Art Methods In Table 4, we compare our method with state-ofthe-art methods [28, 48, 49, 42, 43] on the test sets of Places2 and CelebA. Among the comparedmethods, TMAD [42] and SRM [43] also construct the memory banks, which store the useful contextfor recovering the corrupted regions.

Original: In contrast to CICM, the memory bank of TMAD contains the low-level features (e.g., colors andtextures) of the complete regions in a single image; the memory bank of SRM provides the crossimage distributions of semantic categories, but missing the appearance information of the imageregions.
Revised: In contrast to CICM, the memory bank of TMAD containsthe low-level features (e.g., colors and textures) of the complete regions in a single image; thememory bank of SRM provides the cross-image distributions of semantic categories, but missing theappearance information of the image regions.

Original: In comparison, CICM achieves better performances than other methods.
Revised: In comparison, CICM achieves better performancesthan other methods.

Original: We visualized theresults of different methods in Figure 4(a).
Revised: We visualized the results of different methods in Figure 4(a).

Original: [Table]
Revised: [Table]

Revised: [Table]

Original: [Figure]
Revised: [Table]

Original: Extensive Evaluationon Semantic Inpainting
Revised: Extensive Evaluation

Revised: on Semantic Inpainting

Original: We extensively evaluate CICM on the publicdatasets (i.e., Cityscapes and Outdoor Scenes), which provide the semantic object categories forassisting the image inpainting task.
Revised: We extensively evaluate CICM on the publicdatasets (i.e., Cityscapes and Outdoor Scenes), which provide the semantic object categories forassisting the image inpainting task.

Original: Here, CICM can be easily extended to learn the cross-imagefeatures from not only the RGB images but also the semantic segmentation results.
Revised: Here, CICM can be easily extended to learn the cross-imagefeatures from not only the RGB images but also the semantic segmentation results.

Original: On the test setsof Cityscapes and Outdoor Scenes, CICM outperforms state-of-the-art methods [25, 27, 29, 30, 51],thus demonstrating its power in terms of learning the cross-image context from various data sources.
Revised: On the test setsof Cityscapes and Outdoor Scenes, CICM outperforms state-of-the-art methods [25, 27, 29, 30, 50],thus demonstrating its power in terms of learning the cross-image context from various data sources.

Original: The visualized results of different methods are shown in Figure 4(b).[Figure]
Revised: The visualized results of different methods are shown in Figure 4(b).

Original: [Table]
Revised: [Table]

Original:  Conclusion
Revised:  Conclusion

Original: The latest progress in image inpainting beneﬁts from the visual context learned by deep neuralnetworks for representing the relationship between the image regions.
Revised: The latest progress in image inpainting beneﬁts from the visual context learned by deep neuralnetworks for representing the relationship between the image regions.

Original: It helps to propagate theinformation of the complete image regions to recover the corrupted regions.
Revised: It helps to propagate theinformation of the complete image regions to recover the corrupted regions.

Original: In this paper, we proposethe Cross-Image Context Memory (CICM), where the visual context is learned across different images.
Revised: In this paper, we proposethe Cross-Image Context Memory (CICM), where the visual context is learned across different images.

Original: Compared to the methods that utilize the single-image context for inpainting, CICM better addressesthe problem of missing the intrinsic relationship between regions for learning the visual context, giventhe images with lost contents.
Revised: Compared to the methods that utilize the single-image context for inpainting, CICM better addressesthe problem of missing the intrinsic relationship between regions for learning the visual context, giventhe images with lost contents.

Original: CICM achieves state-of-the-art performances on the public datasets.
Revised: CICM achieves state-of-the-art performances on the public datasets.

Original: In the future, we plan to extend CICM to other tasks (e.g., 3D scene completion and occluded objectdetection), where the critical image contents are required to be recovered and understood.
Revised: In the future, we plan to extend CICM to other tasks (e.g., 3D scene completion and occluded objectdetection), where the critical image contents are required to be recovered and understood.

Original: Checklist
Revised: Checklist

Original: For all authors...
Revised: For all authors...

Original: (a) Do the main claims made in the abstract and introduction accurately reﬂect the paper’scontributions and scope
Revised: (a) Do the main claims made in the abstract and introduction accurately reﬂect the paper’scontributions and scope?

Original: [Yes
Revised: [Yes]

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

Original: [Yes Please see the section of Limitation in the supplementary material.
Revised: [Yes] Please see the section of Limita- tion in the supplementary material.

Original: (c) Did you discuss any potential negative societal impacts of your work?
Revised: (c) Did you discuss any potential negative societal impacts of your work?

Original: [Yes] Please seethe section of Negative Societal Impacts in the supplementary material.
Revised: [Yes] Please see the section of Negative Societal Impacts in the supplementary material

Original: (d) Have you read the ethics review guidelines and ensured that your paper conforms to them?
Revised: (d) Have you read the ethics review guidelines and ensured that your paper conforms tothem?

Original: [Yes]
Revised: [Yes]

Original: If you are including theoretical results...
Revised: If you are including theoretical results...

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

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

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

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

Original: If you ran experiments...
Revised: If you ran experiments...

Original: (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: (a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as a URL)?

Original: [Yes] Please see the section of Implementation Details in the supplementary material.
Revised: [Yes] Please see thesection of Implementation Details in the supplementary material.

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 werechosen)?

Original: [Yes] Please see the section of Implementation Details in the supplementary
Revised: [Yes] Please see the section of Implementation Details in the supplementary

Original: material.
Revised: material.

Original: (c) Did you report error bars (e
Revised: (c) Did you report error bars (e

Original: g., with respect to the random seed after running experi-
Revised: g., with respect to the random seed after running experi-

Original: ments multiple times)?
Revised: ments multiple times)?

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

Original: (d) Did you include the total amount of compute and the type of resources used (e
Revised: (d) Did you include the total amount of compute and the type of resources used (e

Original: g.,type of GPUs, internal cluster, or cloud provider)?
Revised: g.,type of GPUs, internal cluster, or cloud provider)?

Original: [Yes] Please see the section ofImplementation Details in the supplementary material.
Revised: [Yes] Please see the section ofImplementation Details in the supplementary material.

Original: If you are using existing assets (e
Revised: If you are using existing assets (e

Original: g., code, data, models) or curating/releasing new assets...
Revised: g., code, data, models) or curating/releasing new assets...

Original: (a) If your work uses existing assets, did you cite the creators?
Revised: (a) If your work uses existing assets, did you cite the creators?

Original: [Yes]
Revised: [Yes]

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

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

Original: (c) Did you include any new assets either in the supplemental material or as a URL?
Revised: (c) Did you include any new assets either in the supplemental material or as a URL?

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

Original: (d) Did you discuss whether and how consent was obtained from people whose data you’re
Revised: (d) Did you discuss whether and how consent was obtained from people whose data you’re

Original: using/curating?
Revised: using/curating?

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

Original: (e) Did you discuss whether the data you are using/curating contains personally identiﬁable
Revised: (e) Did you discuss whether the data you are using/curating contains personally identiﬁable

Original: information or offensive content?
Revised: information or offensive content?

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

Original: If you used crowdsourcing or conducted research with human subjects...
Revised: If you used crowdsourcing or conducted research with human subjects...

Original: (a) Did you include the full text of instructions given to participants and screenshots, if
Revised: (a) Did you include the full text of instructions given to participants and screenshots, if

Original: applicable?
Revised: applicable?

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

Original: (b) Did you describe any potential participant risks, with links to Institutional Review
Revised: (b) Did you describe any potential participant risks, with links to Institutional Review

Original: Board (IRB) approvals, if applicable?
Revised: Board (IRB) approvals, if applicable?

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

Original: (c) Did you include the estimated hourly wage paid to participants and the total amount
Revised: (c) Did you include the estimated hourly wage paid to participants and the total amount

Revised: [Table]

Original: spent on participant compensation?
Revised: spent on participant compensation?

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