Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

To answer this question, we introduce **UN**iversal **I**mage-**TExt** **R**epresentations (**UNITER**), a large-scale pre-trained model for multimodal embedding.

The review comment to be evaluated is:

# 1. Summary
The authors introduce a new pre-training procedure for image-text representations. The idea is to train the model on a huge collection of different image-text datasets and the use the model for downstream tasks. The difference between the proposal wrt the concurrent work is that conditioned masking is used: (i) Masked Language Modeling (MLM) conditioned on image; (ii) Masked Region Modeling (MRM) conditioned on text; and (iii) joint Image-Text Matching (ITM).

I am on the fence for this paper given the balance between strengths and  weaknesses listed below. I am conservative here and decide for weak reject; but I am open for discussion, if the authors answer to my concerns detailed below.

Strengths:
* State-of-the-art results on several downstream vision-language tasks
* Empirical work to investigate different ways to perform conditioned masking

Weaknesses:
* Some parts of the method needs clarification (see point 2 below) to better understand the details and practical advantages of the method.
* Limited novelty: the paper is an extension of BERT to the visual domain (see point 3 below)

# 2. Clarity and Motivation
The paper reads quite well, although some points need to be improved:
* "Compared with LXMERT (Tan & Bansal, 2019) and ViLBERT (Lu et al., 2019) that use two streams (one Transformer for each modality), our UNITER model can learn joint contextualized ...", why is this an advantage? Using two streams might also lead to learning context? Maybe an example can clarify my question.
* End of Sec. 3.1 (and paragraph in Sec. 3.2): not clear how the model is training for ITM. What's the input and output? Why do you need a new symbol [CLS]?
* Sec. 3.2 ITM: "an additional special token [CLS] is fed into our model, which indicates the fused representation of both modalities" - This is not clear. Why this special token is needed? Why is not needed in the MLM and MRM?
* "The scoring function is denoted as s" -> please indicate in the main text what function you used
* MRFM and MRC are clear, however the intuition of MRC-kl is missing. Why is this needed? What does it mean in practice to minimize such divergence (provide practical example)?
* Combination of tasks (MLM + ITM + MRC-kl + MRFR) -> it is not clear how this is done in practice. Is the loss function composed (summed)? Within the mini-batch, the method randomly chooses which operation to do (e.g., MLM) for each sample? This should be clarified in the main text of the paper.

# 3. Novelty
The novelty of the paper is quite limited since it is an extension of BERT to the visual domain. The authors propose an empirical analysis of different ways to mask the visual input, however this might not be a substantial extension of previous work. In fact, recently there are many other papers (ViLBERT, VisualBERT, LXBERT, ...) working on similar topic with small differences. What it is missing in this paper is an understanding and intuition on the reasons why the conditioned masking idea should be better than the other visual masking ideas proposed in previous work.

# 4. Experimentation
The main advantage of this paper relies on the extensive experimental analysis done on many challenging datasets reaching the state of the art on several downstream tasks.
The evaluation on both pre-training tasks and downstream tasks show that the method is working well in practice.

The evidence to be evaluated is:Recently, pre-trained language models such as ELMo (Peters et al., 2018), BERT (Devlin et al., 2019), GPT2 (Radford et al., 2019), and XLNet (Yang et al., 2019) have shown great advances for NLP tasks.

More recently, there has been some concurrent work on self-supervised learning for multimodal tasks, by pre-training on large-scale image/video and text pairs, then finetuning on downstream tasks.

ViLBERT (Lu et al., 2019) and LXMERT (Tan & Bansal, 2019) introduced the two-stream architecture, where two Transformers are applied to images and text independently, which will be fused by a third Transformer in a later stage. On the other hand, VisualBERT (Li et al., 2019), Unicoder-VL (Li et al., 2019), VL-BERT (Su et al., 2019) and B2T2 (Alberti et al., 2019) proposed the single-stream architecture, where a single Transformer is applied to both image and text.

Our UNITER model belongs to the second family. One key difference between UNITER and the other methods is the masking approach on pre-training tasks.

In this section, we first introduce the model architecture of UNITER (Section 3.1), then describe the designed pre-training tasks and V+L datasets used for pre-training (Section 3.2 and 3.3). The model architecture of UNITER is illustrated in Figure 1. Given a pair of image and sentence, UNITER takes the visual regions of the image and textual tokens of the sentence as the input. We design an Image Embedder and a Text Embedder to extract their respective embeddings. These embeddings are then feed it into a multi-layer self-attention Transformer to learn a cross-modality contextualized embedding between visual regions and textual tokens. Note that the self-attention mechanism in Transformer is order-less, thus it is necessary to explicitly encode positions/locations of tokens/regions as additional inputs. Specifically, in _Image Embedder_, we first use Faster R-CNN1 to extract the visual features (pooled ROI features) for each region. We also encode the location features for each region via a 7-dimensional vector2. Both visual and location features are then fed through a fully-connected (FC) layer, to be projected into the same embedding space. The final visual embedding for each region is obtained by summing up the two FC outputs and then passing through a layer normalization (LN) layer. For _Text Embedder_, we follow BERT (Devlin et al., 2019) and tokenize the input sentence into WordPieces (Wu et al., 2016). The final representation for each sub-word token3 is obtained via summing up its word embedding and position embedding, followed by another LN layer4.

We introduce three main tasks to pre-train our model: Masked Language Modeling _conditioned on image regions_ (MLM), Masked Region Modeling _conditioned on input text_ (with three variants) (MRM), and Image-Text Matching (ITM). As shown in Figure 1, our MRM and MLM are in analogy to BERT, where we randomly mask some words or regions from the input and learn to recover the words or regions as the output of Transformer. Specifically, word masking is realized by replacing the token with a special token [MASK], and region masking is implemented by replacing the visual feature vector with all zeros. Note that each time we only mask one modality while keeping the other modality intact, instead of randomly masking both modalities like ViLBERT and LXMERT. This prevents potential miss-alignment when a masked region happens to be described by a masked word. Empirically, we show that with conditional masking, our model is able to learn better embeddings(in Section 4.2).

During training, we sample both positive and negative image-sentence pairs and learn their matching scores. To pre-train UNITER with the aforementioned different tasks, we randomly sample one pre-training task for each mini-batch and train on only one objective per SGD update. Masked Language Modeling (MLM) We denote the image regions as \(\mathbf{v}=\{v_{1},...,v_{K}\}\), the input words as \(\mathbf{w}=\{w_{1},...,w_{T}\}\), and the mask indices as \(\mathbf{m}\in\mathbb{N}^{M}\). 5 In MLM, we randomly mask out the input words with probability of 15%, and replace the masked ones \(\mathbf{w_{m}}\) with special token [MASK] 6. The goal is to predict these masked words based on the observation of their surrounding words \(\mathbf{w}_{\setminus m}\) and all image regions \(\mathbf{v}\), by minimizing the negative log-likelihood:

\[\mathcal{L}_{\text{MLM}}(\theta)=-E_{(\mathbf{w},\mathbf{v})\sim D}\log P_{ \theta}(\mathbf{w_{m}}|\mathbf{w_{\setminus m}},\mathbf{v}). \tag{1}\] where \(\theta\) is the trainable parameters. Each pair \((\mathbf{w},\mathbf{v})\) is sampled from the whole training set \(D\).

In ITM, an additional special token [CLS] is fed into our model, which indicates the fused representation of both modalities. The inputs to ITM are a sentence and a set of image regions, and the output is a binary label (0 for negative match, and 1 for positive match). We extract the representation of [CLS] token as the joint representation of the input text and image, then fed into a fully connected layer and a sigmoid function to predict a score between 0 and 1.

During training, we sample a positive or negative pair \((\mathbf{w},\mathbf{v})\) from the dataset \(D\) at each step. The negative pair is created by replacing the image or text in a paired sample with a randomly-selected one from other samples. We denote the label as \(y\in\{0,1\}\), indicating if the sampled pair is a match.

Masked Region Modeling (MRM) Similar to MLM, we also sample image regions and mask their visual features with a probability of 15%. The model is trained to reconstruct the masked regions \(\mathbf{v_{m}}\) given the remaining regions \(\mathbf{v_{\setminus m}}\) and all the words \(\mathbf{w}\). The visual features \(v_{m}\) of the masked region are replaced by zeros.

Instead, we propose three variants for Masked Region Modeling, which share the same objective base: \[\mathcal{L}_{\text{MRM}}(\theta)=E_{(\mathbf{w},\mathbf{v})\sim D}f_{\theta}( \mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w}). \tag{3}\] 1) Masked Region Feature Regression (MRFR) MRFR learns to regress the Transformer output of each masked region \(\mathbf{v_{m}^{(i)}}\) to its visual features. Specifically, we apply an FC layer to convert its Transformer output into a vector \(h_{\theta}(\mathbf{v_{m}^{(i)}})\) of same dimension as the input ROI pooled feature \(r(\mathbf{v_{m}^{(i)}})\). Then we apply L2 regression between the two: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M }\|h_{\theta}(\mathbf{v_{m}^{(i)}})-r(\mathbf{v_{m}^{(i)}})\|_{2}^{2}\). 2) Masked Region Classification (MRC) MRC learns to predict the object semantic class for each masked region. We first feed the Transformer output of the masked region \(\mathbf{v_{m}^{(i)}}\) into an FC layer to predict the scores of \(K\) object classes, which further goes through a softmax function to be transformed into a normalized distribution \(g_{\theta}(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). Note that there is no ground-truth label, as the object categories are not provided. Thus, we use the object detection output from Faster R-CNN, and take the detected object category (with the highest confidence score) as the label of the masked region, which will be converted into a one-hot vector \(c(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). The final objective minimizes the cross-entropy (CE) loss: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M} \operatorname{CE}(c(\mathbf{v_{m}^{(i)}}),g_{\theta}(\mathbf{v_{m}^{(i)}}))\).

We construct our pre-training dataset based on four existing V+L datasets: COCO (Lin et al., 2014), Visual Genome (VG) (Krishna et al., 2017), Conceptual Captions (CC) (Sharma et al., 2018), and SBU Captions (Ordonez et al., 2011). Only image and sentence pairs are used for our pre-training purpose, which makes the model framework more scalable, as additional image-sentence pairs are easy to harvest for further pre-training.

We evaluate UNITER on six V+L tasks (listed in Table 2), by transferring the pre-trained model to each target task and finetuning through end-to-end training. We report experimental results on two model sizes: UNITER-base with 12 layers and UNITER-large with 24 layers10. In VQA, VCR and NLVR\({}^{2}\) tasks, given an input image (or a pair of images) and a natural language question (or description), the model predicts an answer (or judges the correctness of the description) based on the visual content in the image.

For VQA, VCR, NLVR\({}^{2}\), Visual Entailment and Image-Text Retrieval, we extract the joint embedding of the input image-text pairs via a multi-layer perceptron (MLP) from the representation of the [CLS] token. For RE Comprehension, we use the MLP to compute the region-wise alignment scores.

Table 4 presents the results of UNITER on all downstream tasks. Both our base and large models are pre-trained on In-domain+Out-of-domain datasets, with the optimal pre-training setting: MLM+ITM+MRC-kl+MRFR.

SOTA task-specific models include: MCAN (Yu et al., 2019) for VQA, MaxEnt (Suhr et al., 2019) for NLVR2, 2BT2 (Albert et al., 2019) for VCR, SCAN (Lee et al., 2018) for Image-Text Retrieval, EVE-Image (Xie et al., 2019) for SNLI-VE, and MAttNet for RE Comprehension (RefCOCO, ReFCOCO+ and ReFCOCOg)12. Concurrent pre-trained models include: ViLBERT, LXBERT, Unicoder-VL, VisualBERT and VLBERT.

Results show that our UNITER-large model achieves new state of the art across all the benchmarks. UNITER-base model also outperforms the others by a large margin across all tasks except VQA.

However, when evaluated on unseen tasks such as NLVR2, UNITER-base achieves 3% gain over LXMERT. In addition, among all the models pre-trained on image-text pairs only, our UNITER-base outperforms the others by \(>\)\(1.5\%\) on VQA.

It is also worth mentioning that both ViLBERT and LXMERT observed two-stream model outperforms single-stream model, while our results show empirically that with our pre-training setting, single-stream model can achieve new state-of-the-art results, with much fewer parameters (UNITER-base: 86M, LXMERT: 183M, ViLBERT: 221M)14.

Thus, we experimented with three modified settings on NLVR2: (\(i\)) _Triplet_: joint embedding of images pairs and query captions; (\(ii\)) _Pair_: individual embedding of each image and each query caption; and (\(iii\)) _Pair-bidattn_: a bidirectional attention is added to the _Pair_ model to learn the interactions between the paired images.

The _Pair_ setting achieves better performance than the _Triplet_ setting even without cross-attention between the image pairs. We hypothesize that it is due to the fact that our UNITER is pre-trained with image-text pairs. Thus, it is difficult to finetune a pair-based pre-trained model on triplet input. The bidirectional attention mechanism in the _Pair-bidattn_ setting, however, compensates the lack of cross-attention between images, hence yielding the best performance with a large margin. This show that with minimal surgery on the top layer of UNITER, our pre-trained model can adapt to new tasks that are very different from pre-training tasks. In this paper, we present UNITER, a large-scale pre-trained model providing UNiversal Image-TExt Representations for Vision-and-Language tasks. Three main pre-training tasks are proposed and evaluated through extensive ablation studies. Trained with both in-domain and out-of-domain datasets, UNITER outperforms state-of-the-art models over multiple V+L tasks by a significant margin.

Natural Language for Visual Reasoning for Real (NLVR2) NLVR2 is a new challenging task for visual reasoning. The goal is to determine whether a natural language statement is true about the given image pair.

Since UNITER only handles one image and one text input at pre-training, the'modality embedding' is extended to help distinguish the additional image presented in the NLVR2 task.

As for the joint representation, instead of rely on only two [CLS] outputs, we apply a multi-head attention layer (Vaswani et al., 2017) on one sequence of joint image-text embeddings to attend to the other sequence of embeddings, and vice versa. After this 'bidirectional' attention interactions, a simple attentional pooling is applied on each output sequences and then a final concat+MLP layer transforms the cross-attended joint representation for true/false classification.

Visual Entailment is a task derived from Flickr30K images and Stanford Natural Language Inference (SNLI) dataset, where the goal is to determine the logical relationship between a natural language statement and an image. Similar to BERT for Natural Language Inference (NLI), we treat SNLI-VE as a three-way classification problem and apply an MLP Transform on [CLS] output. The UNITER model is finetuned using cross-entropy loss.

To finetune UNITER on this task, we add a MLP layer on top of the region outputs from Transformer, to compute the alignment score between the query phrase/sentence and each region.

The finetuning is efficient - we train the model with a batch size of 64 examples and a learning rate of \(5e-5\) for only 5 epochs, and achieve state-of-the-art performance.

Similar to Kovaleva et al. (2019), we observe several patterns in the attention maps of the UNITER model, as shown in Fig. 3.

Note that _Reversed Block_ (Fig. 3f) shows cross-modality alignment between tokens and regions. In Fig. 4, 5, and 6, we visualize several examples of text-to-image attention to demonstrate the local cross-modality alignment between regions and tokens.

Intuitively, our conditional masking learns better latent alignment of entities (regions and words) across two modalities.

Each sub-figure shows a comparison between applying conditional masking and joint random masking during the pre-training of UNITER. The MLM accuracy measures how well UNITER can reconstruct the masked words, and MRC-kl accuracy18 measures how well UNITER can classify the masked regions. In both cases, as shown in Fig. 8, our conditional masking converges faster and achieves higher final accuracy than joint random masking.

We pre-train UNITER on Conceptual Captions only (instead of 4 datasets in 3.3) using our proposed conditional masking and the best pre-training tasks (MLM + \(\mathrm{ITM}\) + MRC-kl + MRFR). Table. 9 shows that UNITER still consistently outperforms the other models by a visible margin on VQA and RefCOCO+. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{2}{c}{Task} & \multicolumn{2}{c}{Datasets} & \multicolumn{2}{c}{Image Src.} & \multicolumn{2}{c}{\#Images} & \multicolumn{2}{c}{\#Text} & \multicolumn{1}{l}{Metric} \\ \hline
1 & VQA & VQA & COCO & 204K & 1.1M & VQA-score \\ \hline
2 & VCR & VCR & Movie Clips & 110K & 290K & Accuracy \\ \hline
3 & NLVR2  & NUVR2  \\ \hline
4 & Visual Entailment & SNLI-VE & Flickr30K & 31K & 507K & Accuracy \\ \hline
5 & Image-Text Retrieval & COCO & COCO & 92K & 460K & Recall@1,5,10 \\  & & Flickr30K & Flickr30K & 32K & 160K & \\ \hline \multirow{3}{*}{6} & RefCOCO & \multirow{3}{*}{COCO} & 20K & 142K & \multirow{3}{*}{Accuracy} \\  & & RefCOCO+ & & 20K & 142K & \\ \cline{1-1}  & RefCOCOg & & 26K & 95K & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics on the datasets of downstream tasks.

Input: Figure 1: Overview of the proposed UNITER model (best viewed in color), consisting of an Image Embedder, a Text Embedder and a multi-layer self-attention Transformer, learned through three pre-training tasks.

Input: Table 4: Results on downstream V+L tasks from UNITER model, compared with task-specific state-of-the-art (SOTA) and concurrent pre-trained models. ZS: Zero-Shot, IR: Image Retrieval and TR: Text Retrieval.

Input: Table 2: Statistics on the datasets of downstream tasks.

Input: Figure 4: Text-to-image attention visualization example.

Input: Figure 5: Text-to-image attention visualization example.

Input: Figure 6: Text-to-image attention visualization example.

Input: Figure 3: Attention heatmaps of UNITER

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find it to be well-stated (score of 1). The paper clearly introduces UNITER as a large-scale pre-trained model for multimodal embedding and thoroughly substantiates this claim through:

1. Detailed model architecture and methodology explanations (Fig 1)
2. Comprehensive experimental results showing SOTA performance across 6 V+L tasks (Table 4)
3. Clear demonstration of the model's effectiveness through attention visualizations (Fig 3-6)
4. Reviewer acknowledgment of "state-of-the-art results on several downstream vision-language tasks"

The claim is modest and accurately reflects the scope and contribution, without overstating capabilities or novelty. The extensive evidence directly supports positioning UNITER as a pre-trained multimodal embedding model.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

To answer this question, we introduce **UN**iversal **I**mage-**TExt** **R**epresentations (**UNITER**), a large-scale pre-trained model for multimodal embedding.

The review comment to be evaluated is:

This is an impressive paper. LIke BERT, it proposes a tranformer based approach to derive a pre-trained network for representing images and texts. The resulting pre-trained network, used in 9 different tasks, advances the SOTA on all the tasks.
The major limitation of this paper is why. Why does it happen? How this results can be achieved? What is exactly represented in this pre-trained network. Why the tasks used for pre-training build a network that is so informative?
This is really the major obscure point of this impressive paper.

The evidence to be evaluated is:Recently, pre-trained language models such as ELMo (Peters et al., 2018), BERT (Devlin et al., 2019), GPT2 (Radford et al., 2019), and XLNet (Yang et al., 2019) have shown great advances for NLP tasks.

More recently, there has been some concurrent work on self-supervised learning for multimodal tasks, by pre-training on large-scale image/video and text pairs, then finetuning on downstream tasks.

ViLBERT (Lu et al., 2019) and LXMERT (Tan & Bansal, 2019) introduced the two-stream architecture, where two Transformers are applied to images and text independently, which will be fused by a third Transformer in a later stage. On the other hand, VisualBERT (Li et al., 2019), Unicoder-VL (Li et al., 2019), VL-BERT (Su et al., 2019) and B2T2 (Alberti et al., 2019) proposed the single-stream architecture, where a single Transformer is applied to both image and text.

Our UNITER model belongs to the second family. One key difference between UNITER and the other methods is the masking approach on pre-training tasks.

In this section, we first introduce the model architecture of UNITER (Section 3.1), then describe the designed pre-training tasks and V+L datasets used for pre-training (Section 3.2 and 3.3). The model architecture of UNITER is illustrated in Figure 1. Given a pair of image and sentence, UNITER takes the visual regions of the image and textual tokens of the sentence as the input. We design an Image Embedder and a Text Embedder to extract their respective embeddings. These embeddings are then feed it into a multi-layer self-attention Transformer to learn a cross-modality contextualized embedding between visual regions and textual tokens. Note that the self-attention mechanism in Transformer is order-less, thus it is necessary to explicitly encode positions/locations of tokens/regions as additional inputs. Specifically, in _Image Embedder_, we first use Faster R-CNN1 to extract the visual features (pooled ROI features) for each region. We also encode the location features for each region via a 7-dimensional vector2. Both visual and location features are then fed through a fully-connected (FC) layer, to be projected into the same embedding space. The final visual embedding for each region is obtained by summing up the two FC outputs and then passing through a layer normalization (LN) layer. For _Text Embedder_, we follow BERT (Devlin et al., 2019) and tokenize the input sentence into WordPieces (Wu et al., 2016). The final representation for each sub-word token3 is obtained via summing up its word embedding and position embedding, followed by another LN layer4.

We introduce three main tasks to pre-train our model: Masked Language Modeling _conditioned on image regions_ (MLM), Masked Region Modeling _conditioned on input text_ (with three variants) (MRM), and Image-Text Matching (ITM). As shown in Figure 1, our MRM and MLM are in analogy to BERT, where we randomly mask some words or regions from the input and learn to recover the words or regions as the output of Transformer. Specifically, word masking is realized by replacing the token with a special token [MASK], and region masking is implemented by replacing the visual feature vector with all zeros. Note that each time we only mask one modality while keeping the other modality intact, instead of randomly masking both modalities like ViLBERT and LXMERT. This prevents potential miss-alignment when a masked region happens to be described by a masked word. Empirically, we show that with conditional masking, our model is able to learn better embeddings(in Section 4.2).

During training, we sample both positive and negative image-sentence pairs and learn their matching scores. To pre-train UNITER with the aforementioned different tasks, we randomly sample one pre-training task for each mini-batch and train on only one objective per SGD update. Masked Language Modeling (MLM) We denote the image regions as \(\mathbf{v}=\{v_{1},...,v_{K}\}\), the input words as \(\mathbf{w}=\{w_{1},...,w_{T}\}\), and the mask indices as \(\mathbf{m}\in\mathbb{N}^{M}\). 5 In MLM, we randomly mask out the input words with probability of 15%, and replace the masked ones \(\mathbf{w_{m}}\) with special token [MASK] 6. The goal is to predict these masked words based on the observation of their surrounding words \(\mathbf{w}_{\setminus m}\) and all image regions \(\mathbf{v}\), by minimizing the negative log-likelihood:

\[\mathcal{L}_{\text{MLM}}(\theta)=-E_{(\mathbf{w},\mathbf{v})\sim D}\log P_{ \theta}(\mathbf{w_{m}}|\mathbf{w_{\setminus m}},\mathbf{v}). \tag{1}\] where \(\theta\) is the trainable parameters. Each pair \((\mathbf{w},\mathbf{v})\) is sampled from the whole training set \(D\).

In ITM, an additional special token [CLS] is fed into our model, which indicates the fused representation of both modalities. The inputs to ITM are a sentence and a set of image regions, and the output is a binary label (0 for negative match, and 1 for positive match). We extract the representation of [CLS] token as the joint representation of the input text and image, then fed into a fully connected layer and a sigmoid function to predict a score between 0 and 1.

During training, we sample a positive or negative pair \((\mathbf{w},\mathbf{v})\) from the dataset \(D\) at each step. The negative pair is created by replacing the image or text in a paired sample with a randomly-selected one from other samples. We denote the label as \(y\in\{0,1\}\), indicating if the sampled pair is a match.

Masked Region Modeling (MRM) Similar to MLM, we also sample image regions and mask their visual features with a probability of 15%. The model is trained to reconstruct the masked regions \(\mathbf{v_{m}}\) given the remaining regions \(\mathbf{v_{\setminus m}}\) and all the words \(\mathbf{w}\). The visual features \(v_{m}\) of the masked region are replaced by zeros.

Instead, we propose three variants for Masked Region Modeling, which share the same objective base: \[\mathcal{L}_{\text{MRM}}(\theta)=E_{(\mathbf{w},\mathbf{v})\sim D}f_{\theta}( \mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w}). \tag{3}\] 1) Masked Region Feature Regression (MRFR) MRFR learns to regress the Transformer output of each masked region \(\mathbf{v_{m}^{(i)}}\) to its visual features. Specifically, we apply an FC layer to convert its Transformer output into a vector \(h_{\theta}(\mathbf{v_{m}^{(i)}})\) of same dimension as the input ROI pooled feature \(r(\mathbf{v_{m}^{(i)}})\). Then we apply L2 regression between the two: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M }\|h_{\theta}(\mathbf{v_{m}^{(i)}})-r(\mathbf{v_{m}^{(i)}})\|_{2}^{2}\). 2) Masked Region Classification (MRC) MRC learns to predict the object semantic class for each masked region. We first feed the Transformer output of the masked region \(\mathbf{v_{m}^{(i)}}\) into an FC layer to predict the scores of \(K\) object classes, which further goes through a softmax function to be transformed into a normalized distribution \(g_{\theta}(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). Note that there is no ground-truth label, as the object categories are not provided. Thus, we use the object detection output from Faster R-CNN, and take the detected object category (with the highest confidence score) as the label of the masked region, which will be converted into a one-hot vector \(c(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). The final objective minimizes the cross-entropy (CE) loss: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M} \operatorname{CE}(c(\mathbf{v_{m}^{(i)}}),g_{\theta}(\mathbf{v_{m}^{(i)}}))\).

We construct our pre-training dataset based on four existing V+L datasets: COCO (Lin et al., 2014), Visual Genome (VG) (Krishna et al., 2017), Conceptual Captions (CC) (Sharma et al., 2018), and SBU Captions (Ordonez et al., 2011). Only image and sentence pairs are used for our pre-training purpose, which makes the model framework more scalable, as additional image-sentence pairs are easy to harvest for further pre-training.

We evaluate UNITER on six V+L tasks (listed in Table 2), by transferring the pre-trained model to each target task and finetuning through end-to-end training. We report experimental results on two model sizes: UNITER-base with 12 layers and UNITER-large with 24 layers10. In VQA, VCR and NLVR\({}^{2}\) tasks, given an input image (or a pair of images) and a natural language question (or description), the model predicts an answer (or judges the correctness of the description) based on the visual content in the image.

For VQA, VCR, NLVR\({}^{2}\), Visual Entailment and Image-Text Retrieval, we extract the joint embedding of the input image-text pairs via a multi-layer perceptron (MLP) from the representation of the [CLS] token. For RE Comprehension, we use the MLP to compute the region-wise alignment scores.

Table 4 presents the results of UNITER on all downstream tasks. Both our base and large models are pre-trained on In-domain+Out-of-domain datasets, with the optimal pre-training setting: MLM+ITM+MRC-kl+MRFR.

SOTA task-specific models include: MCAN (Yu et al., 2019) for VQA, MaxEnt (Suhr et al., 2019) for NLVR2, 2BT2 (Albert et al., 2019) for VCR, SCAN (Lee et al., 2018) for Image-Text Retrieval, EVE-Image (Xie et al., 2019) for SNLI-VE, and MAttNet for RE Comprehension (RefCOCO, ReFCOCO+ and ReFCOCOg)12. Concurrent pre-trained models include: ViLBERT, LXBERT, Unicoder-VL, VisualBERT and VLBERT.

Results show that our UNITER-large model achieves new state of the art across all the benchmarks. UNITER-base model also outperforms the others by a large margin across all tasks except VQA.

However, when evaluated on unseen tasks such as NLVR2, UNITER-base achieves 3% gain over LXMERT. In addition, among all the models pre-trained on image-text pairs only, our UNITER-base outperforms the others by \(>\)\(1.5\%\) on VQA.

It is also worth mentioning that both ViLBERT and LXMERT observed two-stream model outperforms single-stream model, while our results show empirically that with our pre-training setting, single-stream model can achieve new state-of-the-art results, with much fewer parameters (UNITER-base: 86M, LXMERT: 183M, ViLBERT: 221M)14.

Thus, we experimented with three modified settings on NLVR2: (\(i\)) _Triplet_: joint embedding of images pairs and query captions; (\(ii\)) _Pair_: individual embedding of each image and each query caption; and (\(iii\)) _Pair-bidattn_: a bidirectional attention is added to the _Pair_ model to learn the interactions between the paired images.

The _Pair_ setting achieves better performance than the _Triplet_ setting even without cross-attention between the image pairs. We hypothesize that it is due to the fact that our UNITER is pre-trained with image-text pairs. Thus, it is difficult to finetune a pair-based pre-trained model on triplet input. The bidirectional attention mechanism in the _Pair-bidattn_ setting, however, compensates the lack of cross-attention between images, hence yielding the best performance with a large margin. This show that with minimal surgery on the top layer of UNITER, our pre-trained model can adapt to new tasks that are very different from pre-training tasks. In this paper, we present UNITER, a large-scale pre-trained model providing UNiversal Image-TExt Representations for Vision-and-Language tasks. Three main pre-training tasks are proposed and evaluated through extensive ablation studies. Trained with both in-domain and out-of-domain datasets, UNITER outperforms state-of-the-art models over multiple V+L tasks by a significant margin.

Natural Language for Visual Reasoning for Real (NLVR2) NLVR2 is a new challenging task for visual reasoning. The goal is to determine whether a natural language statement is true about the given image pair.

Since UNITER only handles one image and one text input at pre-training, the'modality embedding' is extended to help distinguish the additional image presented in the NLVR2 task.

As for the joint representation, instead of rely on only two [CLS] outputs, we apply a multi-head attention layer (Vaswani et al., 2017) on one sequence of joint image-text embeddings to attend to the other sequence of embeddings, and vice versa. After this 'bidirectional' attention interactions, a simple attentional pooling is applied on each output sequences and then a final concat+MLP layer transforms the cross-attended joint representation for true/false classification.

Visual Entailment is a task derived from Flickr30K images and Stanford Natural Language Inference (SNLI) dataset, where the goal is to determine the logical relationship between a natural language statement and an image. Similar to BERT for Natural Language Inference (NLI), we treat SNLI-VE as a three-way classification problem and apply an MLP Transform on [CLS] output. The UNITER model is finetuned using cross-entropy loss.

To finetune UNITER on this task, we add a MLP layer on top of the region outputs from Transformer, to compute the alignment score between the query phrase/sentence and each region.

The finetuning is efficient - we train the model with a batch size of 64 examples and a learning rate of \(5e-5\) for only 5 epochs, and achieve state-of-the-art performance.

Similar to Kovaleva et al. (2019), we observe several patterns in the attention maps of the UNITER model, as shown in Fig. 3.

Note that _Reversed Block_ (Fig. 3f) shows cross-modality alignment between tokens and regions. In Fig. 4, 5, and 6, we visualize several examples of text-to-image attention to demonstrate the local cross-modality alignment between regions and tokens.

Intuitively, our conditional masking learns better latent alignment of entities (regions and words) across two modalities.

Each sub-figure shows a comparison between applying conditional masking and joint random masking during the pre-training of UNITER. The MLM accuracy measures how well UNITER can reconstruct the masked words, and MRC-kl accuracy18 measures how well UNITER can classify the masked regions. In both cases, as shown in Fig. 8, our conditional masking converges faster and achieves higher final accuracy than joint random masking.

We pre-train UNITER on Conceptual Captions only (instead of 4 datasets in 3.3) using our proposed conditional masking and the best pre-training tasks (MLM + \(\mathrm{ITM}\) + MRC-kl + MRFR). Table. 9 shows that UNITER still consistently outperforms the other models by a visible margin on VQA and RefCOCO+. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{2}{c}{Task} & \multicolumn{2}{c}{Datasets} & \multicolumn{2}{c}{Image Src.} & \multicolumn{2}{c}{\#Images} & \multicolumn{2}{c}{\#Text} & \multicolumn{1}{l}{Metric} \\ \hline
1 & VQA & VQA & COCO & 204K & 1.1M & VQA-score \\ \hline
2 & VCR & VCR & Movie Clips & 110K & 290K & Accuracy \\ \hline
3 & NLVR2  & NUVR2  \\ \hline
4 & Visual Entailment & SNLI-VE & Flickr30K & 31K & 507K & Accuracy \\ \hline
5 & Image-Text Retrieval & COCO & COCO & 92K & 460K & Recall@1,5,10 \\  & & Flickr30K & Flickr30K & 32K & 160K & \\ \hline \multirow{3}{*}{6} & RefCOCO & \multirow{3}{*}{COCO} & 20K & 142K & \multirow{3}{*}{Accuracy} \\  & & RefCOCO+ & & 20K & 142K & \\ \cline{1-1}  & RefCOCOg & & 26K & 95K & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics on the datasets of downstream tasks.

Input: Figure 1: Overview of the proposed UNITER model (best viewed in color), consisting of an Image Embedder, a Text Embedder and a multi-layer self-attention Transformer, learned through three pre-training tasks.

Input: Table 4: Results on downstream V+L tasks from UNITER model, compared with task-specific state-of-the-art (SOTA) and concurrent pre-trained models. ZS: Zero-Shot, IR: Image Retrieval and TR: Text Retrieval.

Input: Table 2: Statistics on the datasets of downstream tasks.

Input: Figure 4: Text-to-image attention visualization example.

Input: Figure 5: Text-to-image attention visualization example.

Input: Figure 6: Text-to-image attention visualization example.

Input: Figure 3: Attention heatmaps of UNITER

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on the evidence, I'll evaluate the claim: "To answer this question, we introduce UNiversal Image-TExt Representations (UNITER), a large-scale pre-trained model for multimodal embedding."

The claim is relatively well-stated and modest, as the evidence demonstrates that:
1. UNITER is indeed a large-scale pre-trained model for multimodal (image-text) embeddings
2. The model architecture, pre-training tasks, and extensive experimental results are thoroughly documented
3. The review comment acknowledges it as "impressive" while noting limitations around explanatory depth
4. Performance results in Table 4 validate the model's capabilities across multiple tasks

The claim simply introduces the model without making exaggerated promises or overstating its capabilities. It lets the evidence speak for itself.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

To further investigate the effectiveness of MRM, we propose three MRM variants: (\(i\)) Masked Region Classification (MRC); (\(ii\)) Masked Region Feature Regression (MRFR); and (\(iii\)) Masked Region Classification with KL-divergence (MRC-kl).

The evidence to be evaluated is:We introduce three main tasks to pre-train our model: Masked Language Modeling _conditioned on image regions_ (MLM), Masked Region Modeling _conditioned on input text_ (with three variants) (MRM), and Image-Text Matching (ITM). As shown in Figure 1, our MRM and MLM are in analogy to BERT, where we randomly mask some words or regions from the input and learn to recover the words or regions as the output of Transformer. Specifically, word masking is realized by replacing the token with a special token [MASK], and region masking is implemented by replacing the visual feature vector with all zeros. Note that each time we only mask one modality while keeping the other modality intact, instead of randomly masking both modalities like ViLBERT and LXMERT. This prevents potential miss-alignment when a masked region happens to be described by a masked word.

Unlike textual tokens that are represented as discrete labels, visual features are high-dimensional and continuous, thus cannot be supervised via class likelihood. Instead, we propose three variants for Masked Region Modeling, which share the same objective base:

1) Masked Region Feature Regression (MRFR) MRFR learns to regress the Transformer output of each masked region \(\mathbf{v_{m}^{(i)}}\) to its visual features. Specifically, we apply an FC layer to convert its Transformer output into a vector \(h_{\theta}(\mathbf{v_{m}^{(i)}})\) of same dimension as the input ROI pooled feature \(r(\mathbf{v_{m}^{(i)}})\). Then we apply L2 regression between the two: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M }\|h_{\theta}(\mathbf{v_{m}^{(i)}})-r(\mathbf{v_{m}^{(i)}})\|_{2}^{2}\). 2) Masked Region Classification (MRC) MRC learns to predict the object semantic class for each masked region. We first feed the Transformer output of the masked region \(\mathbf{v_{m}^{(i)}}\) into an FC layer to predict the scores of \(K\) object classes, which further goes through a softmax function to be transformed into a normalized distribution \(g_{\theta}(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). Note that there is no ground-truth label, as the object categories are not provided. Thus, we use the object detection output from Faster R-CNN, and take the detected object category (with the highest confidence score) as the label of the masked region, which will be converted into a one-hot vector \(c(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). The final objective minimizes the cross-entropy (CE) loss: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M} \operatorname{CE}(c(\mathbf{v_{m}^{(i)}}),g_{\theta}(\mathbf{v_{m}^{(i)}}))\). 3) Masked Region Classification with KL-Divergence (MRC-kl) MRC takes the most likely object class from the object detection model as the hard label (w.p. 0 or 1), which assumes the detected object class is the ground-truth label for the region. However, this may not be true, as no ground-truth label is provided for the detected region. Thus, in MRC-kl, we avoid this assumption by using soft label as supervision signal, which is the raw output from the detector (i.e., a distribution of object classes \(\tilde{c}(\mathbf{v}_{m}^{(i)})\)). MRC-kl aims to distill such knowledge into UNITER as Hinton et al. (2015), by minimizing the KL divergence between two distributions: \(f_{\theta}(\mathbf{v}_{\mathbf{m}}|\mathbf{v}_{\mathbf{m}},\mathbf{w})=\sum_{i= 1}^{M}D_{KL}(\tilde{c}(\mathbf{v}_{\mathbf{m}}^{(i)})||g_{\theta}(\mathbf{v}_{ \mathbf{m}}^{(i)}))\).

Secondly, we validate the effectiveness of each pre-training task through a thorough ablation study. Comparing L2 and L3, MRFR (L3) achieves better results than MLM (L2) only on NLVR\({}^{2}\).

Among the three variants of MRM (L7-L9), we observe that MRC-kl (L9) achieves the best performance (397.09) when combined with MLM + ITM, while MRC (L7) the worst (393.97). When combining MRC-kl and MRFR together with MLM and ITM (L10), we find that they are complimentary to each other, which leads to the highest Meta-Sum score.

Both our base and large models are pre-trained on In-domain+Out-of-domain datasets, with the optimal pre-training setting: MLM+ITM+MRC-kl+MRFR.

Three main pre-training tasks are proposed and evaluated through extensive ablation studies.

Since the images and text in VCR dataset are very different from our pre-training dataset, we further pre-train our model on VCR, using MLM, MRFR and MRC-kl as the pre-training tasks.

To verify this intuition, we show the validation curves during pre-training of MLM and MRC-kl in Fig. 8. Each sub-figure shows a comparison between applying conditional masking and joint random masking during the pre-training of UNITER. The MLM accuracy measures how well UNITER can reconstruct the masked words, and MRC-kl accuracy18 measures how well UNITER can classify the masked regions. In both cases, as shown in Fig. 8, our conditional masking converges faster and achieves higher final accuracy than joint random masking. In addition, Table 3 (row 10 & 11) shows our conditional masking also performs better on fine-tuned downstream tasks.

We pre-train UNITER on Conceptual Captions only (instead of 4 datasets in 3.3) using our proposed conditional masking and the best pre-training tasks (MLM + \(\mathrm{ITM}\) + MRC-kl + MRFR).

Input: Table 3: Evaluation on pre-training tasks and datasets using VQA, Image-Text Retrieval on Flickr30K, NLVR2, and RefCOCO+ as benchmarks. All results are obtained from UNITER-base. Averages of R@1, R@5 and R@10 on Flickr30K for Image Retrieval (IR) and Text Retrieval (TR) are reported. Dark and light grey colors highlight the top and second best results across all the tasks trained with In-domain data.

Input: Figure 1: Overview of the proposed UNITER model (best viewed in color), consisting of an Image Embedder, a Text Embedder and a multi-layer self-attention Transformer, learned through three pre-training tasks.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find the claim to be well-stated and accurately reflects the research presented. The evidence thoroughly describes and validates all three MRM variants:

1. MRC is detailed with its object class prediction approach using cross-entropy loss
2. MRFR is explained with its feature regression method using L2 loss
3. MRC-kl is described with its KL-divergence based soft label approach

The evidence supports these variants through ablation studies (Table 3) showing their effectiveness, with MRC-kl performing best (L9) and MRFR complementing it (L10). The claim doesn't overstate capabilities and aligns with the empirical results presented.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

To further investigate the effectiveness of MRM, we propose three MRM variants: (\(i\)) Masked Region Classification (MRC); (\(ii\)) Masked Region Feature Regression (MRFR); and (\(iii\)) Masked Region Classification with KL-divergence (MRC-kl).

The review comment to be evaluated is:

This paper presents a novel method for image-text representations called UNITER. The proposed method has been subsequently tested in many downstream tasks. A detailed ablation study helps to understand the role of each pretrained task in the proposed model.

Although the empirical results are nice, performing the intensive set of experiments on many different tasks is definitely time-consuming and needs a lot of engineering efforts, the technical contribution does not seem significant to me. The paper modifies an existing pre-training procedure by conditional masking (Section 2). I agree this is well-motivated but it has little novelty and a similar idea is there in VQA (See “Dynamic fusion with intra and inter-modality attention flow for visual question answering”). MLM and MRM are not new training procedure either, they are basically extending the BERT’s training procedure with the consideration of multiple modalities.

I have some questions for the authors:
(1) What are the advantages of using single-stream transformer over two-stream transformer (page 2). I guess it leads to fewer parameters but I don’t think this is a big problem.
(2) Some visualization of attention weights would be helpful.
Minor
•	In “m \e N^M” (equation 1), what is N and M?

The evidence to be evaluated is:We introduce three main tasks to pre-train our model: Masked Language Modeling _conditioned on image regions_ (MLM), Masked Region Modeling _conditioned on input text_ (with three variants) (MRM), and Image-Text Matching (ITM). As shown in Figure 1, our MRM and MLM are in analogy to BERT, where we randomly mask some words or regions from the input and learn to recover the words or regions as the output of Transformer. Specifically, word masking is realized by replacing the token with a special token [MASK], and region masking is implemented by replacing the visual feature vector with all zeros. Note that each time we only mask one modality while keeping the other modality intact, instead of randomly masking both modalities like ViLBERT and LXMERT. This prevents potential miss-alignment when a masked region happens to be described by a masked word.

Unlike textual tokens that are represented as discrete labels, visual features are high-dimensional and continuous, thus cannot be supervised via class likelihood. Instead, we propose three variants for Masked Region Modeling, which share the same objective base:

1) Masked Region Feature Regression (MRFR) MRFR learns to regress the Transformer output of each masked region \(\mathbf{v_{m}^{(i)}}\) to its visual features. Specifically, we apply an FC layer to convert its Transformer output into a vector \(h_{\theta}(\mathbf{v_{m}^{(i)}})\) of same dimension as the input ROI pooled feature \(r(\mathbf{v_{m}^{(i)}})\). Then we apply L2 regression between the two: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M }\|h_{\theta}(\mathbf{v_{m}^{(i)}})-r(\mathbf{v_{m}^{(i)}})\|_{2}^{2}\). 2) Masked Region Classification (MRC) MRC learns to predict the object semantic class for each masked region. We first feed the Transformer output of the masked region \(\mathbf{v_{m}^{(i)}}\) into an FC layer to predict the scores of \(K\) object classes, which further goes through a softmax function to be transformed into a normalized distribution \(g_{\theta}(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). Note that there is no ground-truth label, as the object categories are not provided. Thus, we use the object detection output from Faster R-CNN, and take the detected object category (with the highest confidence score) as the label of the masked region, which will be converted into a one-hot vector \(c(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). The final objective minimizes the cross-entropy (CE) loss: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M} \operatorname{CE}(c(\mathbf{v_{m}^{(i)}}),g_{\theta}(\mathbf{v_{m}^{(i)}}))\). 3) Masked Region Classification with KL-Divergence (MRC-kl) MRC takes the most likely object class from the object detection model as the hard label (w.p. 0 or 1), which assumes the detected object class is the ground-truth label for the region. However, this may not be true, as no ground-truth label is provided for the detected region. Thus, in MRC-kl, we avoid this assumption by using soft label as supervision signal, which is the raw output from the detector (i.e., a distribution of object classes \(\tilde{c}(\mathbf{v}_{m}^{(i)})\)). MRC-kl aims to distill such knowledge into UNITER as Hinton et al. (2015), by minimizing the KL divergence between two distributions: \(f_{\theta}(\mathbf{v}_{\mathbf{m}}|\mathbf{v}_{\mathbf{m}},\mathbf{w})=\sum_{i= 1}^{M}D_{KL}(\tilde{c}(\mathbf{v}_{\mathbf{m}}^{(i)})||g_{\theta}(\mathbf{v}_{ \mathbf{m}}^{(i)}))\).

Secondly, we validate the effectiveness of each pre-training task through a thorough ablation study. Comparing L2 and L3, MRFR (L3) achieves better results than MLM (L2) only on NLVR\({}^{2}\).

Among the three variants of MRM (L7-L9), we observe that MRC-kl (L9) achieves the best performance (397.09) when combined with MLM + ITM, while MRC (L7) the worst (393.97). When combining MRC-kl and MRFR together with MLM and ITM (L10), we find that they are complimentary to each other, which leads to the highest Meta-Sum score.

Both our base and large models are pre-trained on In-domain+Out-of-domain datasets, with the optimal pre-training setting: MLM+ITM+MRC-kl+MRFR.

Three main pre-training tasks are proposed and evaluated through extensive ablation studies.

Since the images and text in VCR dataset are very different from our pre-training dataset, we further pre-train our model on VCR, using MLM, MRFR and MRC-kl as the pre-training tasks.

To verify this intuition, we show the validation curves during pre-training of MLM and MRC-kl in Fig. 8. Each sub-figure shows a comparison between applying conditional masking and joint random masking during the pre-training of UNITER. The MLM accuracy measures how well UNITER can reconstruct the masked words, and MRC-kl accuracy18 measures how well UNITER can classify the masked regions. In both cases, as shown in Fig. 8, our conditional masking converges faster and achieves higher final accuracy than joint random masking. In addition, Table 3 (row 10 & 11) shows our conditional masking also performs better on fine-tuned downstream tasks.

We pre-train UNITER on Conceptual Captions only (instead of 4 datasets in 3.3) using our proposed conditional masking and the best pre-training tasks (MLM + \(\mathrm{ITM}\) + MRC-kl + MRFR).

Input: Table 3: Evaluation on pre-training tasks and datasets using VQA, Image-Text Retrieval on Flickr30K, NLVR2, and RefCOCO+ as benchmarks. All results are obtained from UNITER-base. Averages of R@1, R@5 and R@10 on Flickr30K for Image Retrieval (IR) and Text Retrieval (TR) are reported. Dark and light grey colors highlight the top and second best results across all the tasks trained with In-domain data.

Input: Figure 1: Overview of the proposed UNITER model (best viewed in color), consisting of an Image Embedder, a Text Embedder and a multi-layer self-attention Transformer, learned through three pre-training tasks.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find the statement about proposing three MRM variants to be well-stated and accurately reflects the evidence provided. The paper clearly describes and implements all three variants (MRC, MRFR, and MRC-kl) with detailed mathematical formulations and explanations. The evidence shows thorough empirical validation through ablation studies (Table 3), demonstrating the effectiveness of each variant. The reviewer's comment, while critical of overall novelty, does not dispute the specific claim about proposing these variants. The claim stays within the bounds of what is demonstrated and doesn't overstate the contributions.

<score>1</score>

Justification:
1. The evidence directly supports the existence and implementation of all three claimed MRM variants with detailed formulations
2. Table 3 provides empirical validation showing distinct performance differences between variants
3. The paper offers clear technical distinctions between the variants (e.g., MRC uses hard labels while MRC-kl uses soft labels)
4. The claim is purely descriptive about proposing variants, without making exaggerated claims about novelty or superiority
5. Even though the reviewer questions overall novelty, they don't dispute this specific claim about the variants being proposed


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

To further investigate the effectiveness of MRM, we propose three MRM variants: (\(i\)) Masked Region Classification (MRC); (\(ii\)) Masked Region Feature Regression (MRFR); and (\(iii\)) Masked Region Classification with KL-divergence (MRC-kl).

The review comment to be evaluated is:

# 1. Summary
The authors introduce a new pre-training procedure for image-text representations. The idea is to train the model on a huge collection of different image-text datasets and the use the model for downstream tasks. The difference between the proposal wrt the concurrent work is that conditioned masking is used: (i) Masked Language Modeling (MLM) conditioned on image; (ii) Masked Region Modeling (MRM) conditioned on text; and (iii) joint Image-Text Matching (ITM).

I am on the fence for this paper given the balance between strengths and  weaknesses listed below. I am conservative here and decide for weak reject; but I am open for discussion, if the authors answer to my concerns detailed below.

Strengths:
* State-of-the-art results on several downstream vision-language tasks
* Empirical work to investigate different ways to perform conditioned masking

Weaknesses:
* Some parts of the method needs clarification (see point 2 below) to better understand the details and practical advantages of the method.
* Limited novelty: the paper is an extension of BERT to the visual domain (see point 3 below)

# 2. Clarity and Motivation
The paper reads quite well, although some points need to be improved:
* "Compared with LXMERT (Tan & Bansal, 2019) and ViLBERT (Lu et al., 2019) that use two streams (one Transformer for each modality), our UNITER model can learn joint contextualized ...", why is this an advantage? Using two streams might also lead to learning context? Maybe an example can clarify my question.
* End of Sec. 3.1 (and paragraph in Sec. 3.2): not clear how the model is training for ITM. What's the input and output? Why do you need a new symbol [CLS]?
* Sec. 3.2 ITM: "an additional special token [CLS] is fed into our model, which indicates the fused representation of both modalities" - This is not clear. Why this special token is needed? Why is not needed in the MLM and MRM?
* "The scoring function is denoted as s" -> please indicate in the main text what function you used
* MRFM and MRC are clear, however the intuition of MRC-kl is missing. Why is this needed? What does it mean in practice to minimize such divergence (provide practical example)?
* Combination of tasks (MLM + ITM + MRC-kl + MRFR) -> it is not clear how this is done in practice. Is the loss function composed (summed)? Within the mini-batch, the method randomly chooses which operation to do (e.g., MLM) for each sample? This should be clarified in the main text of the paper.

# 3. Novelty
The novelty of the paper is quite limited since it is an extension of BERT to the visual domain. The authors propose an empirical analysis of different ways to mask the visual input, however this might not be a substantial extension of previous work. In fact, recently there are many other papers (ViLBERT, VisualBERT, LXBERT, ...) working on similar topic with small differences. What it is missing in this paper is an understanding and intuition on the reasons why the conditioned masking idea should be better than the other visual masking ideas proposed in previous work.

# 4. Experimentation
The main advantage of this paper relies on the extensive experimental analysis done on many challenging datasets reaching the state of the art on several downstream tasks.
The evaluation on both pre-training tasks and downstream tasks show that the method is working well in practice.

The evidence to be evaluated is:We introduce three main tasks to pre-train our model: Masked Language Modeling _conditioned on image regions_ (MLM), Masked Region Modeling _conditioned on input text_ (with three variants) (MRM), and Image-Text Matching (ITM). As shown in Figure 1, our MRM and MLM are in analogy to BERT, where we randomly mask some words or regions from the input and learn to recover the words or regions as the output of Transformer. Specifically, word masking is realized by replacing the token with a special token [MASK], and region masking is implemented by replacing the visual feature vector with all zeros. Note that each time we only mask one modality while keeping the other modality intact, instead of randomly masking both modalities like ViLBERT and LXMERT. This prevents potential miss-alignment when a masked region happens to be described by a masked word.

Unlike textual tokens that are represented as discrete labels, visual features are high-dimensional and continuous, thus cannot be supervised via class likelihood. Instead, we propose three variants for Masked Region Modeling, which share the same objective base:

1) Masked Region Feature Regression (MRFR) MRFR learns to regress the Transformer output of each masked region \(\mathbf{v_{m}^{(i)}}\) to its visual features. Specifically, we apply an FC layer to convert its Transformer output into a vector \(h_{\theta}(\mathbf{v_{m}^{(i)}})\) of same dimension as the input ROI pooled feature \(r(\mathbf{v_{m}^{(i)}})\). Then we apply L2 regression between the two: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M }\|h_{\theta}(\mathbf{v_{m}^{(i)}})-r(\mathbf{v_{m}^{(i)}})\|_{2}^{2}\). 2) Masked Region Classification (MRC) MRC learns to predict the object semantic class for each masked region. We first feed the Transformer output of the masked region \(\mathbf{v_{m}^{(i)}}\) into an FC layer to predict the scores of \(K\) object classes, which further goes through a softmax function to be transformed into a normalized distribution \(g_{\theta}(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). Note that there is no ground-truth label, as the object categories are not provided. Thus, we use the object detection output from Faster R-CNN, and take the detected object category (with the highest confidence score) as the label of the masked region, which will be converted into a one-hot vector \(c(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). The final objective minimizes the cross-entropy (CE) loss: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M} \operatorname{CE}(c(\mathbf{v_{m}^{(i)}}),g_{\theta}(\mathbf{v_{m}^{(i)}}))\). 3) Masked Region Classification with KL-Divergence (MRC-kl) MRC takes the most likely object class from the object detection model as the hard label (w.p. 0 or 1), which assumes the detected object class is the ground-truth label for the region. However, this may not be true, as no ground-truth label is provided for the detected region. Thus, in MRC-kl, we avoid this assumption by using soft label as supervision signal, which is the raw output from the detector (i.e., a distribution of object classes \(\tilde{c}(\mathbf{v}_{m}^{(i)})\)). MRC-kl aims to distill such knowledge into UNITER as Hinton et al. (2015), by minimizing the KL divergence between two distributions: \(f_{\theta}(\mathbf{v}_{\mathbf{m}}|\mathbf{v}_{\mathbf{m}},\mathbf{w})=\sum_{i= 1}^{M}D_{KL}(\tilde{c}(\mathbf{v}_{\mathbf{m}}^{(i)})||g_{\theta}(\mathbf{v}_{ \mathbf{m}}^{(i)}))\).

Secondly, we validate the effectiveness of each pre-training task through a thorough ablation study. Comparing L2 and L3, MRFR (L3) achieves better results than MLM (L2) only on NLVR\({}^{2}\).

Among the three variants of MRM (L7-L9), we observe that MRC-kl (L9) achieves the best performance (397.09) when combined with MLM + ITM, while MRC (L7) the worst (393.97). When combining MRC-kl and MRFR together with MLM and ITM (L10), we find that they are complimentary to each other, which leads to the highest Meta-Sum score.

Both our base and large models are pre-trained on In-domain+Out-of-domain datasets, with the optimal pre-training setting: MLM+ITM+MRC-kl+MRFR.

Three main pre-training tasks are proposed and evaluated through extensive ablation studies.

Since the images and text in VCR dataset are very different from our pre-training dataset, we further pre-train our model on VCR, using MLM, MRFR and MRC-kl as the pre-training tasks.

To verify this intuition, we show the validation curves during pre-training of MLM and MRC-kl in Fig. 8. Each sub-figure shows a comparison between applying conditional masking and joint random masking during the pre-training of UNITER. The MLM accuracy measures how well UNITER can reconstruct the masked words, and MRC-kl accuracy18 measures how well UNITER can classify the masked regions. In both cases, as shown in Fig. 8, our conditional masking converges faster and achieves higher final accuracy than joint random masking. In addition, Table 3 (row 10 & 11) shows our conditional masking also performs better on fine-tuned downstream tasks.

We pre-train UNITER on Conceptual Captions only (instead of 4 datasets in 3.3) using our proposed conditional masking and the best pre-training tasks (MLM + \(\mathrm{ITM}\) + MRC-kl + MRFR).

Input: Table 3: Evaluation on pre-training tasks and datasets using VQA, Image-Text Retrieval on Flickr30K, NLVR2, and RefCOCO+ as benchmarks. All results are obtained from UNITER-base. Averages of R@1, R@5 and R@10 on Flickr30K for Image Retrieval (IR) and Text Retrieval (TR) are reported. Dark and light grey colors highlight the top and second best results across all the tasks trained with In-domain data.

Input: Figure 1: Overview of the proposed UNITER model (best viewed in color), consisting of an Image Embedder, a Text Embedder and a multi-layer self-attention Transformer, learned through three pre-training tasks.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that the claim about proposing three MRM variants (MRC, MRFR, and MRC-kl) is well-stated and accurately reflects the evidence. The paper clearly presents and describes each variant in detail, including their mathematical formulations and objectives. The evidence shows thorough empirical validation through ablation studies (Table 3), demonstrating the relative effectiveness of each variant. The MRC-kl variant proved most effective when combined with MLM + ITM, while combining MRC-kl and MRFR led to the highest Meta-Sum score, supporting the value of proposing these variants.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

To further investigate the effectiveness of MRM, we propose three MRM variants: (\(i\)) Masked Region Classification (MRC); (\(ii\)) Masked Region Feature Regression (MRFR); and (\(iii\)) Masked Region Classification with KL-divergence (MRC-kl).

The review comment to be evaluated is:

This is an impressive paper. LIke BERT, it proposes a tranformer based approach to derive a pre-trained network for representing images and texts. The resulting pre-trained network, used in 9 different tasks, advances the SOTA on all the tasks.
The major limitation of this paper is why. Why does it happen? How this results can be achieved? What is exactly represented in this pre-trained network. Why the tasks used for pre-training build a network that is so informative?
This is really the major obscure point of this impressive paper.

The evidence to be evaluated is:We introduce three main tasks to pre-train our model: Masked Language Modeling _conditioned on image regions_ (MLM), Masked Region Modeling _conditioned on input text_ (with three variants) (MRM), and Image-Text Matching (ITM). As shown in Figure 1, our MRM and MLM are in analogy to BERT, where we randomly mask some words or regions from the input and learn to recover the words or regions as the output of Transformer. Specifically, word masking is realized by replacing the token with a special token [MASK], and region masking is implemented by replacing the visual feature vector with all zeros. Note that each time we only mask one modality while keeping the other modality intact, instead of randomly masking both modalities like ViLBERT and LXMERT. This prevents potential miss-alignment when a masked region happens to be described by a masked word.

Unlike textual tokens that are represented as discrete labels, visual features are high-dimensional and continuous, thus cannot be supervised via class likelihood. Instead, we propose three variants for Masked Region Modeling, which share the same objective base:

1) Masked Region Feature Regression (MRFR) MRFR learns to regress the Transformer output of each masked region \(\mathbf{v_{m}^{(i)}}\) to its visual features. Specifically, we apply an FC layer to convert its Transformer output into a vector \(h_{\theta}(\mathbf{v_{m}^{(i)}})\) of same dimension as the input ROI pooled feature \(r(\mathbf{v_{m}^{(i)}})\). Then we apply L2 regression between the two: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M }\|h_{\theta}(\mathbf{v_{m}^{(i)}})-r(\mathbf{v_{m}^{(i)}})\|_{2}^{2}\). 2) Masked Region Classification (MRC) MRC learns to predict the object semantic class for each masked region. We first feed the Transformer output of the masked region \(\mathbf{v_{m}^{(i)}}\) into an FC layer to predict the scores of \(K\) object classes, which further goes through a softmax function to be transformed into a normalized distribution \(g_{\theta}(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). Note that there is no ground-truth label, as the object categories are not provided. Thus, we use the object detection output from Faster R-CNN, and take the detected object category (with the highest confidence score) as the label of the masked region, which will be converted into a one-hot vector \(c(\mathbf{v_{m}^{(i)}})\in\mathbb{R}^{K}\). The final objective minimizes the cross-entropy (CE) loss: \(f_{\theta}(\mathbf{v_{m}}|\mathbf{v_{\setminus m}},\mathbf{w})=\sum_{i=1}^{M} \operatorname{CE}(c(\mathbf{v_{m}^{(i)}}),g_{\theta}(\mathbf{v_{m}^{(i)}}))\). 3) Masked Region Classification with KL-Divergence (MRC-kl) MRC takes the most likely object class from the object detection model as the hard label (w.p. 0 or 1), which assumes the detected object class is the ground-truth label for the region. However, this may not be true, as no ground-truth label is provided for the detected region. Thus, in MRC-kl, we avoid this assumption by using soft label as supervision signal, which is the raw output from the detector (i.e., a distribution of object classes \(\tilde{c}(\mathbf{v}_{m}^{(i)})\)). MRC-kl aims to distill such knowledge into UNITER as Hinton et al. (2015), by minimizing the KL divergence between two distributions: \(f_{\theta}(\mathbf{v}_{\mathbf{m}}|\mathbf{v}_{\mathbf{m}},\mathbf{w})=\sum_{i= 1}^{M}D_{KL}(\tilde{c}(\mathbf{v}_{\mathbf{m}}^{(i)})||g_{\theta}(\mathbf{v}_{ \mathbf{m}}^{(i)}))\).

Secondly, we validate the effectiveness of each pre-training task through a thorough ablation study. Comparing L2 and L3, MRFR (L3) achieves better results than MLM (L2) only on NLVR\({}^{2}\).

Among the three variants of MRM (L7-L9), we observe that MRC-kl (L9) achieves the best performance (397.09) when combined with MLM + ITM, while MRC (L7) the worst (393.97). When combining MRC-kl and MRFR together with MLM and ITM (L10), we find that they are complimentary to each other, which leads to the highest Meta-Sum score.

Both our base and large models are pre-trained on In-domain+Out-of-domain datasets, with the optimal pre-training setting: MLM+ITM+MRC-kl+MRFR.

Three main pre-training tasks are proposed and evaluated through extensive ablation studies.

Since the images and text in VCR dataset are very different from our pre-training dataset, we further pre-train our model on VCR, using MLM, MRFR and MRC-kl as the pre-training tasks.

To verify this intuition, we show the validation curves during pre-training of MLM and MRC-kl in Fig. 8. Each sub-figure shows a comparison between applying conditional masking and joint random masking during the pre-training of UNITER. The MLM accuracy measures how well UNITER can reconstruct the masked words, and MRC-kl accuracy18 measures how well UNITER can classify the masked regions. In both cases, as shown in Fig. 8, our conditional masking converges faster and achieves higher final accuracy than joint random masking. In addition, Table 3 (row 10 & 11) shows our conditional masking also performs better on fine-tuned downstream tasks.

We pre-train UNITER on Conceptual Captions only (instead of 4 datasets in 3.3) using our proposed conditional masking and the best pre-training tasks (MLM + \(\mathrm{ITM}\) + MRC-kl + MRFR).

Input: Table 3: Evaluation on pre-training tasks and datasets using VQA, Image-Text Retrieval on Flickr30K, NLVR2, and RefCOCO+ as benchmarks. All results are obtained from UNITER-base. Averages of R@1, R@5 and R@10 on Flickr30K for Image Retrieval (IR) and Text Retrieval (TR) are reported. Dark and light grey colors highlight the top and second best results across all the tasks trained with In-domain data.

Input: Figure 1: Overview of the proposed UNITER model (best viewed in color), consisting of an Image Embedder, a Text Embedder and a multi-layer self-attention Transformer, learned through three pre-training tasks.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After careful review, the claim about proposing three MRM variants (MRC, MRFR, and MRC-kl) is well-stated and accurately reflects the evidence. The paper thoroughly describes each variant's methodology, implementation details, and evaluation results. The evidence shows comprehensive ablation studies comparing these variants (Table 3, rows 7-9), with MRC-kl performing best when combined with MLM + ITM. The findings are presented without exaggeration, acknowledging the relative performance differences between variants. The review comment, while positive about the overall paper, does not specifically challenge this particular claim about the MRM variants.

<score>1</score>

Justification:
1. The evidence directly supports the claim by providing detailed technical descriptions of each MRM variant
2. Experimental validation through ablation studies (Table 3) demonstrates the effectiveness of each variant
3. The claim makes no overreaching statements about performance or superiority
4. The methodology and implementation details are clearly documented for each variant
5. Results are presented objectively with proper comparisons between variants (e.g., "MRC-kl achieves the best performance... while MRC the worst")
6. The claim's scope matches exactly what is demonstrated in the evidence


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Besides, our masked language/region modeling is conditioned on full observation of image/text, different from other concurrent pre-trained models that apply joint random masking to both modalities.

The evidence to be evaluated is:More recently, there has been some concurrent work on self-supervised learning for multimodal tasks, by pre-training on large-scale image/video and text pairs, then finetuning on downstream tasks.

ViLBERT (Lu et al., 2019) and LXMERT (Tan & Bansal, 2019) introduced the two-stream architecture, where two Transformers are applied to images and text independently, which will be fused by a third Transformer in a later stage. On the other hand, VisualBERT (Li et al., 2019), Unicoder-VL (Li et al., 2019), VL-BERT (Su et al., 2019) and B2T2 (Alberti et al., 2019) proposed the single-stream architecture, where a single Transformer is applied to both image and text.

One key difference between UNITER and the other methods is the masking approach on pre-training tasks. Instead of randomly masking both image regions and sentence words, we use conditional masking, i.e., masking only one modality while keeping the other untainted.

Note that each time we only mask one modality while keeping the other modality intact, instead of randomly masking both modalities like ViLBERT and LXMERT. This prevents potential miss-alignment when a masked region happens to be described by a masked word. Empirically, we show that with conditional masking, our model is able to learn better embeddings(in Section 4.2).

We denote the image regions as \(\mathbf{v}=\{v_{1},...,v_{K}\}\), the input words as \(\mathbf{w}=\{w_{1},...,w_{T}\}\), and the mask indices as \(\mathbf{m}\in\mathbb{N}^{M}\). 5 In MLM, we randomly mask out the input words with probability of 15%, and replace the masked ones \(\mathbf{w_{m}}\) with special token [MASK] 6. The goal is to predict these masked words based on the observation of their surrounding words \(\mathbf{w}_{\setminus m}\) and all image regions \(\mathbf{v}\), by minimizing the negative log-likelihood:

Similar to MLM, we also sample image regions and mask their visual features with a probability of 15%. The model is trained to reconstruct the masked regions \(\mathbf{v_{m}}\) given the remaining regions \(\mathbf{v_{\setminus m}}\) and all the words \(\mathbf{w}\). The visual features \(v_{m}\) of the masked region are replaced by zeros.

When we perform random masking on both modalities simultaneously during pre-training, i.e., w/o conditional masking (L11), we observe a decrease in Meta-Sum score (396.51) compared to that with conditional masking (399.97). This indicates that the conditional masking strategy enables the model to learn better joint image-text representations effectively.

We further discuss the advantage of our proposed conditional masking over joint random masking used in (Tan and Bansal, 2019; Lu et al., 2019). Intuitively, our conditional masking learns better latent alignment of entities (regions and words) across two modalities.

With conditional masking, when the region of dog is masked, our model should be able to infer that the region is dog, based on the context of both surrounding regions and the full sentence (Fig. 7(a)), and vice versa.

Each sub-figure shows a comparison between applying conditional masking and joint random masking during the pre-training of UNITER.

In both cases, as shown in Fig. 8, our conditional masking converges faster and achieves higher final accuracy than joint random masking. In addition, Table 3 (row 10 & 11) shows our conditional masking also performs better on fine-tuned downstream tasks.

We pre-train UNITER on Conceptual Captions only (instead of 4 datasets in 3.3) using our proposed conditional masking and the best pre-training tasks (MLM + \(\mathrm{ITM}\) + MRC-kl + MRFR).

Input: Table 3: Evaluation on pre-training tasks and datasets using VQA, Image-Text Retrieval on Flickr30K, NLVR2, and RefCOCO+ as benchmarks. All results are obtained from UNITER-base. Averages of R@1, R@5 and R@10 on Flickr30K for Image Retrieval (IR) and Text Retrieval (TR) are reported. Dark and light grey colors highlight the top and second best results across all the tasks trained with In-domain data.

Input: Figure 8: Comparison of MLM and MRC-kl validation accuracy using joint masking and our proposed conditional masking.

Input: Figure 7: Example showing difference between conditional masking and joint random masking.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, this appears to be a well-stated claim that accurately reflects the evidence without exaggeration. The evidence clearly shows that:

1. UNITER uses conditional masking where only one modality is masked while keeping the other intact, which is explicitly different from other concurrent models like ViLBERT and LXMERT that use joint random masking.

2. The evidence provides experimental results demonstrating the advantages of this approach:
- Higher Meta-Sum scores (399.97 vs 396.51)
- Faster convergence and higher accuracy shown in Figure 8
- Better downstream task performance shown in Table 3

The claim simply states this key differentiating feature without overstating its impact or significance.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Besides, our masked language/region modeling is conditioned on full observation of image/text, different from other concurrent pre-trained models that apply joint random masking to both modalities.

The review comment to be evaluated is:

This paper presents a novel method for image-text representations called UNITER. The proposed method has been subsequently tested in many downstream tasks. A detailed ablation study helps to understand the role of each pretrained task in the proposed model.

Although the empirical results are nice, performing the intensive set of experiments on many different tasks is definitely time-consuming and needs a lot of engineering efforts, the technical contribution does not seem significant to me. The paper modifies an existing pre-training procedure by conditional masking (Section 2). I agree this is well-motivated but it has little novelty and a similar idea is there in VQA (See “Dynamic fusion with intra and inter-modality attention flow for visual question answering”). MLM and MRM are not new training procedure either, they are basically extending the BERT’s training procedure with the consideration of multiple modalities.

I have some questions for the authors:
(1) What are the advantages of using single-stream transformer over two-stream transformer (page 2). I guess it leads to fewer parameters but I don’t think this is a big problem.
(2) Some visualization of attention weights would be helpful.
Minor
•	In “m \e N^M” (equation 1), what is N and M?

The evidence to be evaluated is:More recently, there has been some concurrent work on self-supervised learning for multimodal tasks, by pre-training on large-scale image/video and text pairs, then finetuning on downstream tasks.

ViLBERT (Lu et al., 2019) and LXMERT (Tan & Bansal, 2019) introduced the two-stream architecture, where two Transformers are applied to images and text independently, which will be fused by a third Transformer in a later stage. On the other hand, VisualBERT (Li et al., 2019), Unicoder-VL (Li et al., 2019), VL-BERT (Su et al., 2019) and B2T2 (Alberti et al., 2019) proposed the single-stream architecture, where a single Transformer is applied to both image and text.

One key difference between UNITER and the other methods is the masking approach on pre-training tasks. Instead of randomly masking both image regions and sentence words, we use conditional masking, i.e., masking only one modality while keeping the other untainted.

Note that each time we only mask one modality while keeping the other modality intact, instead of randomly masking both modalities like ViLBERT and LXMERT. This prevents potential miss-alignment when a masked region happens to be described by a masked word. Empirically, we show that with conditional masking, our model is able to learn better embeddings(in Section 4.2).

We denote the image regions as \(\mathbf{v}=\{v_{1},...,v_{K}\}\), the input words as \(\mathbf{w}=\{w_{1},...,w_{T}\}\), and the mask indices as \(\mathbf{m}\in\mathbb{N}^{M}\). 5 In MLM, we randomly mask out the input words with probability of 15%, and replace the masked ones \(\mathbf{w_{m}}\) with special token [MASK] 6. The goal is to predict these masked words based on the observation of their surrounding words \(\mathbf{w}_{\setminus m}\) and all image regions \(\mathbf{v}\), by minimizing the negative log-likelihood:

Similar to MLM, we also sample image regions and mask their visual features with a probability of 15%. The model is trained to reconstruct the masked regions \(\mathbf{v_{m}}\) given the remaining regions \(\mathbf{v_{\setminus m}}\) and all the words \(\mathbf{w}\). The visual features \(v_{m}\) of the masked region are replaced by zeros.

When we perform random masking on both modalities simultaneously during pre-training, i.e., w/o conditional masking (L11), we observe a decrease in Meta-Sum score (396.51) compared to that with conditional masking (399.97). This indicates that the conditional masking strategy enables the model to learn better joint image-text representations effectively.

We further discuss the advantage of our proposed conditional masking over joint random masking used in (Tan and Bansal, 2019; Lu et al., 2019). Intuitively, our conditional masking learns better latent alignment of entities (regions and words) across two modalities.

With conditional masking, when the region of dog is masked, our model should be able to infer that the region is dog, based on the context of both surrounding regions and the full sentence (Fig. 7(a)), and vice versa.

Each sub-figure shows a comparison between applying conditional masking and joint random masking during the pre-training of UNITER.

In both cases, as shown in Fig. 8, our conditional masking converges faster and achieves higher final accuracy than joint random masking. In addition, Table 3 (row 10 & 11) shows our conditional masking also performs better on fine-tuned downstream tasks.

We pre-train UNITER on Conceptual Captions only (instead of 4 datasets in 3.3) using our proposed conditional masking and the best pre-training tasks (MLM + \(\mathrm{ITM}\) + MRC-kl + MRFR).

Input: Table 3: Evaluation on pre-training tasks and datasets using VQA, Image-Text Retrieval on Flickr30K, NLVR2, and RefCOCO+ as benchmarks. All results are obtained from UNITER-base. Averages of R@1, R@5 and R@10 on Flickr30K for Image Retrieval (IR) and Text Retrieval (TR) are reported. Dark and light grey colors highlight the top and second best results across all the tasks trained with In-domain data.

Input: Figure 8: Comparison of MLM and MRC-kl validation accuracy using joint masking and our proposed conditional masking.

Input: Figure 7: Example showing difference between conditional masking and joint random masking.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on my review of the evidence, this claim is well-stated and accurately reflects what is shown in the data. The evidence clearly demonstrates that:

1. The authors explicitly use conditional masking where only one modality is masked while keeping the other intact, as opposed to joint random masking used by other models.

2. The empirical results support this claim:
- Figure 8 shows conditional masking achieves better validation accuracy than joint masking
- Table 3 shows improved Meta-Sum scores with conditional masking (399.97) vs without it (396.51)
- The paper includes detailed explanations of how conditional masking is implemented and its benefits

While the reviewer comments note limited technical novelty, they don't dispute the factual accuracy of this specific claim about the masking approach. The claim simply states what is different about their masking strategy, without overstating its significance.

<score>1</score>