Original: Improving cross-modal attention via object detection
Revised: Improving cross-modal attention via object detection

Original: Anonymous Author(s)

Original: AffiliationAddress email

Original: Abstract
Revised: Abstract

Original: Cross-modal attention is widely used in multimodal learning to fuse informationfrom two modalities.
Revised: Cross-modal attention is widely used in multimodal learning to fuse information from two modalities.

Original: However, most existing models only assimilate cross-modalattention indirectly by relying on end-to-end learning and do not directly improvethe attention mechanisms.
Revised: However, most existing models only assimilate cross-modal attention indirectly by relying on end-to-end learning and do not directly improve the attention mechanisms.

Original: In this paper, we propose a methodology for directlyenhancing cross-modal attention by utilizing object-detection models for visionand-language tasks that deal with image and text information.
Revised: In this paper, we propose a methodology for directly enhancing cross-modal attention by utilizing object-detection models for visionand-language tasks that deal with image and text information.

Original: We used the maskof the detected objects obtained by the detection model as a pseudo label, and weadded a loss between the attention map of the multimodal learning model and thepseudo label.
Revised: We used the mask of the detected objects obtained by the detection model as a pseudo label, and we added a loss between the attention map of the multimodal learning model and the pseudo label.

Original: The proposed methodology drastically improves the performanceof the baseline model across all performance metrics in various popular datasetsfor the image-captioning task.
Revised: The proposed methodology drastically improves the performance of the baseline model across all performance metrics in various popular datasets for the image-captioning task.

Original: Moreover, our highly scalable methodology can beapplied to any multimodal task in terms of vision-and-language.
Revised: Moreover, our highly scalable methodology can be applied to any multimodal task in terms of vision-and-language.

Original:  Introduction
Revised:  Introduction

Original: Several studies have been conducted on multimodal learning models that deal with various modalitieshaving different characteristics, such as image, text, and speech information.
Revised: Several studies have been conducted on multimodal learning models that deal with various modalities having different characteristics, such as image, text and speech information.

Original: Vision-and-Language,which includes VisualQA[1] and Image Captioning[2], is one of the most extensively studied fieldsthat achieves the desired output by receiving image and text information as input.
Revised: Vision-and-Language, which includes VisualQA[1] and Image Captioning[2], is one of the most extensively studied fields that achieves the desired output by receiving image and text information as input.

Original: Similar to othermultimodal learning methods, a model that mixes the two modalities well should be designed forproducing the desired representation.
Revised: Similar to other multimodal learning methods, a model that mixes the two modalities well should be designed for producing the desired representation.

Original: Most multimodal models that display the best performance use attention mechanisms to mix two ormore modalities.
Revised: Most multimodal models that display the best performance use attention mechanisms to mix two or more modalities.

Original: Attention techniques help the model learn the most important parts on its own andshare relevant details between the two modalities.
Revised: Attention techniques help the model learn the most important parts on its own and share relevant details between the two modalities.

Original: Unlike attention applied in one modality, such asself-attention, cross-modal attention is applied between two modalities.
Revised: Unlike attention applied in one modality, such as self-attention, cross-modal attention is applied between two modalities.

Original: In particular, cross-modalattention has become essential in multimodal models because transformer models based on attentiontechniques exhibit strength in multimodal learning tasks.
Revised: In particular, cross-modal attention has become essential in multimodal models because transformer models based on attention techniques exhibit strength in multimodal learning tasks.

Original: Additionally, many studies have displayedexcellent performances using cross-modal attention in vision-and-language research.
Revised: Additionally, many studies have displayed excellent performances using cross-modal attention in vision-and-language research.

Original: However, most existing vision-and-language models only leverage the cross-modal attention module,and there have been no attempts to improve it.
Revised: However, most existing vision-and-language models only leverage the cross-modal attention module, and there have been no attempts to improve it.

Original: In other words, no study has directly trained across-modal attention module using an objective function.
Revised: In other words, no study has directly trained a cross-modal attention module using an objective function.

Original: In this paper, we propose a methodologythat directly improves cross-modal attention in vision-and-language research, thus increasing themodel’s overall performance.
Revised: In this paper, we propose a methodology that directly improves cross-modal attention in vision-and-language research, thus increasing the model’s overall performance.

Original: Specifically, we propose a method to improve attention mechanisms using object-detection models.
Revised: Specifically, we propose a method to improve attention mechanisms using object-detection models.

Original: Recently, powerful object-detection models, such as detectron2[3], have emerged that showperformances similar to or even exceeding those of humans. We leveraged the ability of these object-
Revised: Recently, powerful object-detection models, such as detectron2[3], have emerged that show

Original: [Figure]
Revised: [Figure]

Revised: performances similar to or even exceeding those of humans.

Original: detection models to improve cross-modal attention through pseudo-self-supervised learning.
Revised: We leveraged the ability of these objectdetection models to improve cross-modal attention through pseudo-self-supervised learning.

Original: Asshown in Figure 1, the object mask obtained by passing the input image through the object-detectionmodel was used as a pseudo label, and an auxiliary loss was configured such that the attention mapoutput by the cross-modal attention module of the vision-and-language model was similar to thepseudo label.
Revised: As shown in Figure 1, the object mask obtained by passing the input image through the object-detection model was used as a pseudo label, and an auxiliary loss was configured such that the attention map output by the cross-modal attention module of the vision-and-language model was similar to the pseudo label.

Original: Upon performing experiments on multiple datasets by applying our methodology to an imagecaptioning task, we observed a significant improvement in performance compared to the existingbaseline model across all metrics with all datasets.
Revised: Upon performing experiments on multiple datasets by applying our methodology to an imagecaptioning task, we observed a significant improvement in performance compared to the existing baseline model across all metrics with all datasets.

Original: The proposed methodology is a new trainingscheme that seeks to improve the performance of cross-modal attention.
Revised: The proposed methodology is a new training scheme that seeks to improve the performance of cross-modal attention.

Original: Furthermore, we highlightthat our highly scalable methodology can be applied task-agnostically to all vision-and-languagetasks.
Revised: Furthermore, we highlight that our highly scalable methodology can be applied task-agnostically to all vision-and-language tasks.

Original:  Related works2.1 Cross-modal attention
Revised:  Related works 2.1 Cross-modal attention

Original: Multimodal learning models that handle two modalities with different characteristics in one modelrequire a process of fusing two modality embeddings.
Revised: Multimodal learning models that handle two modalities with different characteristics in one model require a process of fusing two modality embeddings.

Original: Ever since the attention technique, whichallows models to focus on the important parts and derive information on their own, has been devised,studies have focused on mixing information and deriving performance using cross-modal attentionfor different modalities.
Revised: Ever since the attention technique, which allows models to focus on the important parts and derive information on their own, has been devised, studies have focused on mixing information and deriving performance using cross-modal attention for different modalities.

Original: Like other uni-modal models, many multimodal studies barely utilize the attention technique, withonly few existing studies on improving the attention effect.
Revised: Like other uni-modal models, many multimodal studies barely utilize the attention technique, with only few existing studies on improving the attention effect.

Original: Recently, some studies have beenconducted to enhance the effectiveness of cross-modal attention beyond simple utilization.
Revised: Recently, some studies have been conducted to enhance the effectiveness of cross-modal attention beyond simple utilization.

Original: Forexample, recent studies [4, 5] have attempted to improve the efficacy of attention between twomodalities by reducing the difference in characteristics between the two modalities.
Revised: For example, recent studies [4, 5] have attempted to improve the efficacy of attention between two modalities by reducing the difference in characteristics between the two modalities.

Original: However, to date, few studies exist on the direct improvement of attention because in most existingmodels, the attention technique is assimilated through end-to-end learning, making it challengingto design a direct objective function.
Revised: However, to date, few studies exist on the direct improvement of attention because in most existing models, the attention technique is assimilated through end-to-end learning, making it challenging to design a direct objective function.

Original: In this paper, we propose a direct improvement method forcross-modal attention via designing objective functions using an object-detection model. .
Revised: In this paper, we propose a direct improvement method for cross-modal attention via designing objective functions using an object-detection model. .

Original: 2.2 Surrogate IOU
Revised: 2.2 Surrogate IOU

Original: The Invention-of-Union (IOU) metric is used as an indicator of detection performance based on thedegree of overlap between the output of the object-detection model and the actual label.
Revised: The Invention-of-Union (IOU) metric is used as an indicator of detection performance based on the degree of overlap between the output of the object-detection model and the actual label.

Original: It is widelyused for model evaluation in semantic segmentation, instance segmentation, and object-detectiontasks.
Revised: It is widely used for model evaluation in semantic segmentation, instance segmentation, and object-detection tasks.

Original: Additionally, attempts have been made to use it as a direct objective function to improve theresults of the IOU metric.
Revised: Additionally, attempts have been made to use it as a direct objective function to improve the results of the IOU metric.

Original: However, because the IOU metric is indifferentiable, it is not availablein most gradient-based frameworks.
Revised: However, because the IOU metric is indifferentiable, it is not available in most gradient-based frameworks.

Original: Therefore, in several studies [6, 7], the IOU score was directlyused for learning through a differentiable surrogate function.
Revised: Therefore, in several studies [6, 7], the IOU score was directly used for learning through a differentiable surrogate function.

Original: In this study, we used the surrogate IOU loss function to learn the cross-modal attention of thevision-and-language model.
Revised: In this study, we used the surrogate IOU loss function to learn the cross-modal attention of the vision-and-language model.

Original: While previous studies have focused on improving the performance ofthe object-detection model itself, this study differs by using a surrogate IOU to design an objectivefunction between the results of the attention module and the object-detection model.
Revised: While previous studies have focused on improving the performance of the object-detection model itself, this study differs by using a surrogate IOU to design an objective function between the results of the attention module and the object-detection model.

Original:  Methods 
Revised:  Methods 3.1 Proposed Methodology for improving cross-modal attention Algorithm

Original: 3.1 Proposed Methodology for improving cross-modal attention Algorithm

Original: Input : image, sequence, ODM (Object Detection Model), VLM (Vision-and-Language Model)
Revised: Input : image, sequence, ODM (Object Detection Model), VLM (Vision-and-Language Model)

Original: Output : training_Loss objects, masks ← ODM (image) attentions, model-specific Loss ← VLM (image, sequence) for object in objects do if object is in sequence then merge masks [ object ]
Revised: Output : training_Loss objects, masks ← ODM (image) attentions, model-specific Loss ← VLM (image, sequence) for object in objects do if object is in sequence then merge masks [ object ]

Original: ICA _ Loss [ object ] =
Revised: ICA _ Loss [ object ] =

Original: Surrogate_IOU ( masks [ object ] , attentions [ object ]) end if end for average ICA_Loss training_Loss = model-specific_Loss + λ Aux ∗ ICA
Revised: Surrogate_IOU ( masks [ object ] , attentions [ object ]) end if end for average ICA_Loss training_Loss = model-specific_Loss + λ Aux ∗ ICA

Original: _ Loss return training_Loss
Revised: _ Loss return training_Loss

Original: We propose a training scheme that can be applied to vision-and-language tasks that deal with imageand text information, regardless of the task.
Revised: We propose a training scheme that can be applied to vision-and-language tasks that deal with image and text information, regardless of the task.

Original: As shown in Figure 1, the proposed methodology utilizesan object-detection model.
Revised: As shown in Figure 1, the proposed methodology utilizes an object-detection model.

Original: Many approaches use the ability of an object-detection model in thevision-and-language tasks [8, 9], but, to the best of our knowledge, our study is the first to introduceand use it as a factor for directly improving the attention module.
Revised: Many approaches use the ability of an object-detection model in the vision-and-language tasks [8, 9], but, to the best of our knowledge, our study is the first to introduce and use it as a factor for directly improving the attention module.

Original: Algorithm 1 provides our novel training scheme for improving cross-modal attention.
Revised: Algorithm 1 provides our novel training scheme for improving cross-modal attention.

Original: First, theobject-detection model extracts object instances from the input image and corresponding masks.
Revised: First, the object-detection model extracts object instances from the input image and corresponding masks.

Original: Then, among the labels of the objects detected, we select the one that matches the word token inthe input sequence.
Revised: Then, among the labels of the objects detected, we select the one that matches the word token in the input sequence.

Original: Additionally, the masks of the instances corresponding to these critical objectsare merged.
Revised: Additionally, the masks of the instances corresponding to these critical objects are merged.

Original: For example, in Figure 1, sheep are found as multiple instances by the object-detectionmodel, and as “sheep” is included in the caption, the masks of the instances corresponding to thesheep are merged.
Revised: For example, in Figure 1, sheep are found as multiple instances by the object-detection model, and as “sheep” is included in the caption, the masks of the instances corresponding to the sheep are merged.

Original: The merged mask is then used as a pseudo-ground-truth label for the attentionmap corresponding to the word token.
Revised: The merged mask is then used as a pseudo-ground-truth label for the attention map corresponding to the word token.

Original: Because of the bias in the detection model, it is called apseudo-ground-truth label.
Revised: Because of the bias in the detection model, it is called a pseudo-ground-truth label.

Original: However, as the performance of recent object-detection models exceedshuman capabilities, they can be considered as approaching human-annotated ground-truth labels.
Revised: However, as the performance of recent object-detection models exceeds human capabilities, they can be considered as approaching human-annotated ground-truth labels.

Original: Finally, the surrogate IOU is measured with the attention map output by the attention module inthe vision-and-language model and the pseudo-attention label and is used as an auxiliary loss.
Revised: Finally, the surrogate IOU is measured with the attention map output by the attention module in the vision-and-language model and the pseudo-attention label and is used as an auxiliary loss.

Original: Forthe surrogate IOU loss, the Lov ´ a sz hinge extension of Jaccard loss [6] is used.
Revised: For the surrogate IOU loss, the Lov ´ a sz hinge extension of Jaccard loss [6] is used.

Original: The Lov ´ a sz hingeextension of Jaccard loss converts the Jaccard index (also called the IOU score) into a differentiableloss function for use in a continuous optimization framework.
Revised: The Lov ´ a sz hinge extension of Jaccard loss converts the Jaccard index (also called the IOU score) into a differentiable loss function for use in a continuous optimization framework.

Original: For a segmentation output ˜ y andground truth y ∗ , mispredicted pixels for class c can be expressed as the following formula.
Revised: For a segmentation output ˜ y and ground truth y ∗ , mispredicted pixels for class c can be expressed as the following formula.

Original: [Equation]
Revised: [Equation]

Original: Subsequently, Jaccard loss is rewritten as shown below, and this Jaccard loss is employed withempirical risk minimization.
Revised: Subsequently, Jaccard loss is rewritten as shown below, and this Jaccard loss is employed with empirical risk minimization.

Original: [Figure]
Revised: [Equation]

Revised: However, because the Jaccard loss is indiiferentiable, it is unable to directly apply it to the gradient descent framework.

Revised: Previous studies [5, 10] proved that this Jaccard loss has submodular character-

Original: [Figure]
Revised: [Figure]

Revised: [Figure]

Original: ++
Revised: +

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

Original: + carrots brocolli
Revised: + carrots brocolli

Original: A plate of meat and with
Revised: A plate of meat and with

Original: [Figure]
Revised: [Figure]

Original: [Equation] However, because the Jaccard loss is indiiferentiable, it is unable to directly apply it to the gradientdescent framework. Previous studies [5, 10] proved that this Jaccard loss has submodular characteristics and that the convex closure of the submodular set is tight and computable in polynomial time.
Revised: istics and that the convex closure of the submodular set is tight and computable in polynomial time.

Original: The convex closure of the submodular set function is a Lov ´ a sz extension, which is defined as follows.
Revised: The convex closure of the submodular set function is a Lov ´ a sz extension, which is defined as follows.

Original: Definition 1 [[6], Def.2].
Revised: Definition 1 [[6], Def.2].

Original: The Lov ´ a sz extension of a set function ∆ : 0 , 1 p (cid:55)→ R such that ∆(0)
Revised: The Lov ´ a sz extension of a set function ∆ : 0 , 1 p (cid:55)→ R such that ∆(0)

Original: =is dfeined by
Revised: =is dfeined by

Original: [Equation]
Revised: [Equation]

Original: with g i ( m ) =
Revised: with g i ( m ) =

Original: ∆( { π 1 , ..., π i } ) − ∆( { π 1 , ..., π i − 1 } ) , π being a permutation ordering the componentsof the m in decreasing order, i.e. x π 1 ≥ x π 2 · · · ≥ x πp .
Revised: ∆( { π 1 , ..., π i } ) − ∆( { π 1 , ..., π i − 1 } ) , π being a permutation ordering the components of the m in decreasing order, i.e. x π 1 ≥ x π 2 · · · ≥ x πp .

Original: Finally, this surrogate IOU is composed of the following Lov ´ a sz hinge applied to the Jaccard loss byapplying the Lov ´ a sz extension to the hinge loss m ( F ) for the output score F .
Revised: Finally, this surrogate IOU is composed of the following Lov ´ a sz hinge applied to the Jaccard loss by applying the Lov ´ a sz extension to the hinge loss m ( F ) for the output score F .

Original: [Equation]
Revised: [Equation]

Original: This ICA(Improving Cross-modal Attention) loss was reflected in the objective function of theoriginal model additionally.
Revised: This ICA(Improving Cross-modal Attention) loss was reflected in the objective function of the original model additionally.

Original: For our setting, output score F is attention map feature rather thansegmentation output.
Revised: For our setting, output score F is attention map feature rather than segmentation output.

Original: The “ model-specific Loss ,” the original objective function, is different for eachtask of vision-and-language.
Revised: The “ model-specific Loss ,” the original objective function, is different for each task of vision-and-language.

Original: Because the proposed method is a task-agnostic methodology that canbe applied regardless of the task, it can be used for any loss through additional auxiliary loss.
Revised: Because the proposed method is a task-agnostic methodology that can be applied regardless of the task, it can be used for any loss through additional auxiliary loss.

Original: 3.2 Application to Image Captioning task
Revised: 3.2 Application to Image Captioning task

Original: The proposed methodology was applied to image captioning, a vision-and-language task.
Revised: The proposed methodology was applied to image captioning, a vision-and-language task.

Original: The modelused for image captioning is shown in Figure 2.
Revised: The model used for image captioning is shown in Figure 2.

Original: The Mask R-CNN [11], used as the object-detectionmodel, receives an image as the input and outputs the class label and the mask of the detected object.
Revised: The Mask R-CNN [11], used as the object-detection model, receives an image as the input and outputs the class label and the mask of the detected object.

Original: We also checked whether the class label of the detected object given in natural language exists inthe text sequence of the vision-and-language model.
Revised: We also checked whether the class label of the detected object given in natural language exists in the text sequence of the vision-and-language model.

Original: In the case of image captioning, if the objectdetected by Mask R-CNN was included in the word tokens of the caption given as input, the objectwas considered to be critical and the mask was extracted.
Revised: In the case of image captioning, if the object detected by Mask R-CNN was included in the word tokens of the caption given as input, the object was considered to be critical and the mask was extracted.

Original: This was used as a pseudo-attention label.
Revised: This was used as a pseudo-attention label.

Original: Moreover, as shown in Figure 2, the image was passed through the attention module of the model,and an attention map corresponding to each word token was generated.
Revised: Moreover, as shown in Figure 2, the image was passed through the attention module of the model, and an attention map corresponding to each word token was generated.

Original: Subsequently, the attentionmap corresponding to the word token of the caption and the critical object word token correspondingto the class label predicted by Mask R-CNN were extracted.
Revised: Subsequently, the attention map corresponding to the word token of the caption and the critical object word token corresponding to the class label predicted by Mask R-CNN were extracted.

Original: Finally, the auxiliary loss was configured[Table] as a surrogate IOU using this attention map and the pseudo-attention label previously output by Mask
Revised: Finally, the auxiliary loss was configured as a surrogate IOU using this attention map and the pseudo-attention label previously output by Mask

Original: R-CNN.
Revised: R-CNN.

Original:  Experiment
Revised:  Experiment

Original: We conducted an experiment using image captioning in a vision-and-language task.
Revised: We conducted an experiment using image captioning in a vision-and-language task.

Original: The showattend-tell [12] model was used as the baseline.
Revised: The showattend-tell [12] model was used as the baseline.

Original: This baseline model utilizes the attention moduleand attention with an image corresponding to each word token of the caption.
Revised: This baseline model utilizes the attention module and attention with an image corresponding to each word token of the caption.

Original: For the dataset,we used the most commonly used datasets for image captioning: Flickr8k[13], Flickr30k[14], and
Revised: For the dataset, [Table] we used the most commonly used datasets for image captioning: Flickr8k[13], Flickr30k[14] [Table]

Revised: and

Original: MSCOCO[15].
Revised: MSCOCO[15].

Original: Additionally, the following natural language generation performance indicators wereused: BLEU-1, BLEU-4[16], CIDEr[17], METEOR[18], ROUGE-L[19], and SPICE[20].
Revised: Additionally, the following natural language generation performance indicators were used: BLEU-1, BLEU-4[16], CIDEr[17], METEOR[18], ROUGE-L[19] [Table] and SPICE[20].

Original: Both theexisting baseline model and the proposed model use the directly implemented model, and the officialperformance index implemented in PyCoCo was used as a performance indicator.
Revised: Both the existing baseline model and the proposed model use the directly implemented model [Table] and the official performance index implemented in PyCoCo was used as a performance indicator.

Original: Among the classlabels discovered by Mask R-CNN during the experiment, the average number of critical objectsbelonging to the caption was 2.3 per instance, and the loss was calculated by taking the mean averageloss of the corresponding essential objects; the λ Aux value was set to 0.1.
Revised: Among the class labels discovered by Mask R-CNN during the experiment, the average number of critical objects belonging to the caption was 2.3 per instance [Table] and the loss was calculated by taking the mean average loss of the corresponding essential objects; the λ Aux value was set to 0.1.

Original: The experimental results are listed in Table 1.
Revised: The experimental results are listed in Table 1.

Original: The ICA Loss improves the cross-modal attention loss,which is the auxiliary loss added in this study.
Revised: The ICA Loss improves the cross-modal attention loss, which is the auxiliary loss added in this study.

Original: The model to which the proposed auxiliary loss isadded shows much better performance across all datasets and performance indicators.
Revised: The model to which the proposed auxiliary loss is added shows much better performance across all datasets and performance indicators.

Original: In particular,when ICA Loss is added to the Flickr8K and MSCOCO datasets, performance indicators such as
Revised: In particular, when ICA Loss is added to the Flickr8K and MSCOCO datasets, performance indicators such as

Original: BLEU-4 and ROUGE-L are markedly improved.
Revised: BLEU-4 and ROUGE-L are markedly improved.

Original: Thus, the proposed method for directly improvingattention has an apparent positive effect on model performance improvement.
Revised: Thus, the proposed method for directly improving attention has an apparent positive effect on model performance improvement.

Original:  Broader Impact
Revised:  Broader Impact

Original: As mentioned earlier, the proposed methodology can be applied to all vision-and-language studies in amodel-agnostic manner.
Revised: As mentioned earlier, the proposed methodology can be applied to all vision-and-language studies in a model-agnostic manner.

Original: The mask output by the object-detection model and the class label generatedin the corresponding natural language can be used in vision-and-language tasks.
Revised: The mask output by the object-detection model and the class label generated in the corresponding natural language can be used in vision-and-language tasks.

Original: For example, inaddition to the image captioning used in this study, it can be applied to visual question answeringtasks to help find a critical object in a question and directly improve the corresponding cross-modalattention.
Revised: For example, in addition to the image captioning used in this study, it can be applied to visual question answering tasks to help find a critical object in a question and directly improve the corresponding cross-modal attention.

Original: Further, in the natural language for a visual reasoning task, if there is an object withoverlapping prediction classes in the object-detection model for two images in the natural language,whether these objects play an important role can be determined.
Revised: Further, in the natural language for a visual reasoning task, if there is an object with overlapping prediction classes in the object-detection model for two images in the natural language, whether these objects play an important role can be determined.

Original:  Conclusion
Revised:  Conclusion

Original: In this paper, we proposed a method to improve performance by borrowing an object-detection modelfrom the existing vision-and-language research.
Revised: In this paper, we proposed a method to improve performance by borrowing an object-detection model from the existing vision-and-language research.

Original: We presented a methodology for enhancing attentionthrough direct learning by applying a differentiable surrogate IOU objective function to the outputmask of the object-detection model and the attention map of the model.
Revised: We presented a methodology for enhancing attention through direct learning by applying a differentiable surrogate IOU objective function to the output mask of the object-detection model and the attention map of the model.

Original: The proposed method can beextended to any task in the future, and its feasibility was verified experimentally by the significantimprovement in performance for the image-captioning task.  
Revised: The proposed method can be extended to any task in the future, and its feasibility was verified experimentally by the significant improvement in performance for the image-captioning task.  Acknowledgements

Revised: We thank anonymous reviewers for their constructive and insightful comments.

Revised: K. Jung is with ASRI,

Revised: Seoul National University, Korea.

Revised: This work was supported by Sumsung Electronics and the BK

Revised: FOUR program of the Education and Research Program for Future ICT Pioneers, Seoul National

Revised: University in 2022.

Revised: This work was partly supported by Institute of Information & communications

Revised: Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT)

Revised: [NO.20210-01343, Artificial Intelligence Graduate School Program (Seoul National University)].

Revised: This work was partially funded by gifts from Adobe Research.