Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond
Authors: Zhecan Wang
Long Chen
Haoxuan You
Keyang Xu
Yicheng He
Wenhao Li
Noel C Codella
Kai-Wei Chang
Shih-Fu Chang
Venue: EMNLP 2023 Findings (2023)
Keywords: vision language
vcr
vqa
snli-ve
visual question answering
commonsense reasoning
pretraining
multimodal
robust
low-shot
zero-shot
domain-shift
debiased
shortcut
TL;DR: Bias Mitigation in Multiple-Choice Visual Question Answering and Other Vision Language Understanding Tasks with Long Candidate Choices

Abstract: Vision-language (VL) understanding tasks evaluate models' comprehension of complex visual scenes through multiple-choice questions. However, we have identified two dataset biases that models can exploit as shortcuts to resolve various VL tasks correctly without proper understanding. The first type of dataset bias is Unbalanced Matching bias, where the correct answer overlaps the question and image more than the incorrect answers. The second type of dataset bias is Distractor Similarity bias, where incorrect answers are overly dissimilar to the correct answer but significantly similar to other incorrect answers within the same sample. To address these dataset biases, we first propose Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data. We then introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing the synthesized training data, particularly the counterfactual data, via focusing on intra-sample differentiation. Extensive experiments demonstrate the effectiveness of ADS and ICT in consistently improving model performance across different benchmarks, even in domain-shifted scenarios.

Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond
Zhecan Wang', Long Chen'; Haoxuan You'; Keyang Xu', Yicheng He'
Wenhao Li Noel Codella', Kai-Wei Chang', Shih-Fu Chang!
Columbia University HKUST Microsoft Research
University of Califomia, Los Angeles
{zw2627, hy2612, yh3330, w1275e, sc250)@columbia.. edu
longchen@ust. xkyē7148gmail. .oom ncodell crosoft. com, kwchangocs ucla.edu
Abstract
Classification with SHORT Answers (VGA, 6GA.er))
Vision-language (VI.) understanding tasks eval- uate models' comprehension of complex vi- sual scenes through multiple-choice questions. However, we have identified two dataset biases that models can exploit as shortcuts to resolve various VL tasks correctly without proper un- derstanding. The first type of dataset bias is Unbalanced Matching bias, where the correct answer overlaps the question and image more than the incorrect answers. The second type of dataset bias is Distractor Similarity bias, where incorrect answers are overly dissimilar the correct answer but significantly similar to other incorrect answers within the same sample. To address these dataset biases, we first propose Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data. We then introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing the synthesized training data, particularly the coun- terfactual data, via focusing on intra-sample differentiation. Extensive experiments demon- strate the effectiveness of ADS and ICT in con- sistently improving model performance across different benchmarks, even Nomain-shifter scenarios.
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VLEP,
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Overty Similarity
Figure 1: (a): Prior studies examined dataset bias in the distribution of short-phrase answers (e.g., "white" is often the answer when asking about color). (b): Our work investigates the biases in VQA with long answer choices, where the correct answer has more n-grams overlapped with the image and question (orange and green). Meanwhile, the incorrect answers contain more irrelevant n-grams to the scene (bluc).
(VQA-Short) (Chen et al., 2020a; Dancette et al., 2021; Gokhale et al., 2020; Gupta et al., 2022; Niu et al.. 2021; Ramakrishnan et al., 2018). These benchmarks typically consist of simple questions that can be answered using one two words, fo- cusing primarily visual perception. However, the demand for complex reasoning capabilities has increased, VLU benchmarks incorporating rich text annotation as contextual information have gained popularity, leaning towards the annotation of long answers. In this paper, we uncover dataset biases in VQA-Long, which employs long answer for- mats. We contend that these biases pose greater challenges for mitigation and have a significant impact on the training and evaluation process of supervised models. Specifically, we identify two prominent types of biases in VQA-Long. The first is Unbalanced Matching (UM) bias, characterized by an uneven distribution of n-gram matches be-
1 Introduction
Visual Question Answering (VQA) is challeng- ing vision-language task that requires reasoning with integrated information from visual and text modalities (Antol al., 2015; Zellers et al., 2019; Lei et al., 2020; Ren et al., 2015; Lu et al., 2021; Tapaswi et al., 2016; Schwenk et al., 2022).
VQA benchmarks (Zellers et al., 2019; Lei et al., 2020; Tapaswi et al., 2016; Lei et al., 2018, 2019) present complex scenarios with multiple entities in multiple-choice question format, where the model selects the correct answer from multiple long, context-dependent candidate answers.
Previous studies have examined dataset bias in VQA benchmarks with short-phrase answers
Equal Contribution

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tween the answer choices and premise (i.e., image and question). The correct answer often exhibits a higher n-gram overlap with the question or men- tions more objects in the image than distracting op- tions, which frequently contain unrelated N-grams. The second type, termed Distractor Similarity (DS) bias, occurs when the model can identify the cor- rect answer without considering the question and image. This bias arises when the correct answer distinctly differs from the distractors, which are highly similar amongst themselves.
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The two biases we have identified are also not limited to VQA-Long: they are also present in other VLU benchmarks, including SNLI-VE (Xie et al., 2019) and VLEP (Lei et al., 2020). Capitaliz- ing on these biases, we design a simple algorithm based on heuristic rules without any training. Sur- prisingly, yields -esh-prrrfraeeee comparable to supervised models: 66.29% Q2A accuracy on VCR, long-form VQA problem on visual com- monsense reasoning, 69.77% accuracy on SNLI- VE, and 48.85% on VLEP. These results raise ques- tions about if the existing models truly comprehend the context rely on shorteuts to answer questions.
Figure 2: A comparison of image-text data. (a) lists the original VCR question, answer choices, and image data. (b) lists our synthesized factual (I+, A+) and counter- factual (I-, A-) image-text data. (c) shows an example of modifying sample of VCR data using the former solutions (Chen et al., 2020a; Liang et al., 2020).
sentence answers and distractors directly, while ADS-I generates images that closely resemble real images with minimal disturbance to the data.
Furthermore, previous debiasing methods di- rectly incorporate the synthesized counterfactual questions and images into the models' training in- put (Figure 2c). However, these methods are not applicable to VQA-Long, as it requires an exact- matched ground truth long answer and distinct dis- tracting options for constructing multiple- choice question. To address this limitation, introduce Intra-sample Contrastive Training (ICT), which em- ploys a loss function to promote the models' focus on intra-sample disparities among the synthesized ing models to under-utilize visual information and potentially develop false visual dependencies.
Different from the biases identified in VQA- short, the dataset biases we identified these text-rich datasets are significantly harder to re- move. They are affected by several reasons, cross- modal correlations, open- open-ended text generation, and heavy reliance on human artifacts during an- notation. These biases in VQA-Long with rich context are more likely to be text-dependent, caus-
factual, and counterfactual images. This approach guides models to learn the appropriate visual de- pendency pertaining to the query.
In terms of mitigating dataset biases, prior data synthesis approaches (Chen et al., 2020a; Dancette et al., 2021: Gokhale et al., 2020: Gupta et al., 2022; Niu et al., 2021; Ramakrishnan et al., 201 8) have demonstrated their effectiveness for VQA-Short; however, they are not suitable for VQA-Long. Ad- ditionally, some well-known methods (Chen et al., 2020a; Liang et al., 2020) disrupt the data distribu- tion through superficial text masking or image oc- clusions (see Figure 2). To overcome these limita- tions, we propose novel Adversarial Data Synthe- sis (ADS) method mitigate biases in VQA-Long, addressing the under-utilization of visual informa- tion and incorrect visual dependency in models. ADS generates synthetic factual and counterfac- tual text data using ADS-T and synthesizes images using ADS-I. Specifically, ADS-T generates long
Through extensive comparisons over VCR, SNLI-VE, VLEP, and even VQA-Short datasets, we empirically demonstrate the effectiveness of our methods in improving model performance under both standard evaluation and domain-shifter sce- narios. To further assess models' robustness against dataset bias, we create domain-shifee evaluation benchmarks based on VCR using ADS. These benchmarks are validated by human annotators to guarantee high quality. While our analysis and experiments primarily focus on VCR, SNLI-VE, and VLEP. it is important to note that these dataset biases frequently appear in other VLU tasks with rich contextual information.
In summary, our contributions are three-fold:
We conduct the first comprehensive study on

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data biases in VQn-Long, uncovering two preva- lent biases in VLU benchmarks.
biases across all textual components, cross-modal correlations in the input, and the process of gen- erating distractors. We identify more general bias problems and demonstrate their prevalence. Debiasing Methods. Various approaches were pro- posed to counter biases but only focus on VQA- Short. They can be categorized into two directions. training strategies and Data Symthesis (DS), and all suffer from various constraints. For instance, train- ing strategies like (Gupta al., 2022; Niu et al., 2021) and DS solutions, (Ray et al., 2019; Sel- varaju et al., 2020; Ribeiro et al., 2019; Wang et al., 2022d) only focus on a single modality. Debiased training like (Wang et al.. 2022b; Niu and Zhang, 2021; Zhang et al., 2021b) require constraints of either a specific model structure or doubling the models' complexity. Other methods (Chen et al., 2020a; Liang et al., 2020) apply occlusion boxes or maskings on images or questions and thus drasti- cally disturb data distribution, leading to nonsensi- cal synthesized answers. Gokhale et al. (2020) tries to improve the synthesized image and text quality but is limited to two specific question types. Most importantly, all of them cannot generalize to VQA-
We propose a data synthesis method (ADS) and a training strategy (ICT) to address these biases. ADS generates factual and counterfactual image- text data to mitigate biases, while ICT aids models in utilizing this synthesized data during training.
We introduce evaluation benchmarks to eval- uate the robustness of VL models against dataset biases, establishing a more fair comparison for ex- isting and future models.
2 Related Work
Biases in VL Benchmarks. Previous studies have predominantly focused biases in VQA-Short (Li et al., 2018; Hudson and Manning, 2019; Lu et al., 2021; Ren et al., 2015; Johnson al., 2017). These benchmarks lack sample-specifes candidate options, resulting in VL models being supervised to clas- sify image-question pairs using shared class la- bels consisting of short answers. In these datasets, researchers have pointed out that models often downplay visual information and instead focus learning biases in the text (Agrawal al., 2018; Dancette et al., 2021: Zhang et al., 2016; Manju- natha et al., 2019). This is exemplified by mod- els directly learning the shallow mapping between prior question words and shared class labels in the absence of sample-specific contextualized candi- date options. Consequently, models develop false visual dependency (Cao et al., 2020; Wang al., 2022b) they may succeed in resolving VQA tasks (Selvaraju et al., 2016; Chen et al., 2020a; Gupta et al., 2022) utilizing irrelevant visual cues. However, the biases previously examined may not apply to VQA-Long due to its unique character- istics, such as the absence of shared class labels, the presence of sample-specific candidate options, and the diverse question types. Consequently, it chal- lenging for models conclude the question types and determine the most popular answer choices for each question type. Given its difficulty and complex visual scenes, VQA-Long has gained pop- ularity in recent years (Zellers et al., 2019; Tapaswi et al., 2016; Schwenk et al., 2022; Zhu et al., 2016; Li et al., 2020), but bias analysis in this specific context has not been explored extensively. While a recent study (Ye and Kovashka, 2021) briefly ad- dressed bias issues in VCR, only focused on the exact matching of pronoun words. In contrast, our research delves into a comprehensive analysis of
Long. Only few works, (Wang et al., 2022b,d; Ye and Kovashka, 2021), related to VQA-Long but still fail to identify specific bias issues.
Bias Analysis in VQA-Long
In this section, we identify and analyze two distinct types of biases that commonly occur in VQA-Long and other VLU benchmarks.
3.1 Unbalanced-Matching Dataset Bias
Inspired by (Ye and Kovashka, 2021), we con- ducted comprehensive analysis of matching n- grams within candidate options against the text premise (question), and visual premise (image) v. We calculate the percentage of samples, C (C)) as following, where correct (incorrect) answers a (ad) have more matched n-grams (n S 3) against the premise information p E {u, t than the other:
(a,, p) # matched n-grams between and p
C IN ((0(a. max max.feA.-at (p)))
1 1
C. = N NE Muux, (0(a) O(af, ph).
where for each sample i, A; represents all the paired candidate options for sample 1, of is the
correct answer, af is one of the three distractors
For matching n-grams against the visual premise, we extract object labels from images.

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(incorrect answers), and pi is the premise (either image or question). (a. p) is the count of matched n-grams, and is the total number of samples.
4 Biases Mitigation in VQA-Long
This section introduces (1) Adversarial Data Syn- thesis (ADS) to synthesize factual and counterfac- tual data; (2) Intra-sample Counterfactual Training (ICT) method to exploit the data.
Our analysis reveals that the correct answer of- ten has the highest number of matched n g-gramms against the question and image among candidate answer options. Specifically, for the Q2A task² in VCR, C can be as high as 66.29% which is much higher than the percentage of distractors, Ca at 29.16%. When using image premise. C. is 42.75% which is also higher than C2+ at 10.23%. This unbalance also persists in other VLU benchmarks. For example, C. is 48. 85% (higher than C₁ 36.19% 19%) in VLEP and 69.77% (higher than Cy. 40%40%) in SNLI-VE. Besides containing fewer n-gram overlaps against the premise, we ob- served that distractors even contain more irrelevant n-grams to the given context (Details in A.8), as in 5. 5.
4.1 Adversarial Data Synthesis
ADS has two components, ADS-Tex, Toxt (ADS-T) for generating less biased answers and ADS-Image (ADS-I) for images.
4.1.1 ADS-T
ADS-T generates synthesized options for a sample, A+ and A-, to alleviate the dataset biases. Multimodal Distractor Generation. To improve distractors' diversity and relevance to the given context and correct answers, we incorporate visual premise information to improve Adversarial Match- ing (AM).
For given dataset examples, {(p), 0:) ) Pi rep- resents the premise (visual or textual), ag denotes the answer and K' is the total number of samples. Following AM, we utilize the first term in Eq. (I) to measure the relevance of the candidate answer, 0g from other questions, against the premise and the second term to measure the similarity between a; and aj. Both Symel and Saim upproximated by stylish models Further, for every example. (p,, a;), we can obtain a distractor by performing maximum- weight bipartite matching on a weight matrix W E N given by:
3.2 Distractor Similarity Dataset Bias
Many benchmarks (Zellers et al., 2019; Williams et al., 2022; Lei et al., 2020; Li et al., 2020; Liu et al., 2020) rely on automated Adversarial Match- ing (AM) for generating distractors, aiming to min- imize costs. AM generates distractors by reusing answers from other questions. selects answers related to the given question while dissimilar to the correct answer. However, prior works tend to overly emphasize the dissimilarity to the correct answer and thus ireelevance to the context using stylistic models (Details in A.7). Additionally, a significant issue arises from generating distractors without considering visual information in AM (Lei al., 2020: Zellers al., 2019: Li et al., 2020). Sur- prisingly, even in manual annotation settings (Xie al., 2019; Do al., 2020; Kayser et al., 2021), annotators are tasked with generating distracting candidates (distractors) without access to the im- age, forcing them to imagine and create excessively dissimilar distractors to avoid ambiguity. Addition- ally, insufficient premise can also cause limited diversity of generated distractors. In contrast, cor- rect answers are consistently generated with visual and textual information, ensuring accuracy. Con- sequently, the dissimilarity between correct and incorrect answers becomes exaggerated due to the different premise information used for their gener- ation.
W., = log (Strel (p., az)) + Alog - Saim (a., a;)),
(1)
where is a hyperparameter. Different from previ- ous works that only include text as the premise, we bring in visual object regions, vf, to enrich the ref- erence, where k E [0, ID] and ID is the total number of object regions extracted from the image 1, We further employ a pre-trained CLIP (Radford et al., 2021) measure the visual relevance of candidate answers against all of the object regions and only reserve the maximum score, max (Surel (v;, a;)). Thus, the first term in Eq. (1) will be substituted by: a log (Strel (4: az) + max (Sw.re (v;, as)))s where is a hyperparameter.
Distractor Refinement. Despite the improve- ment of our Multimodal Distractor Generation, distractors generated solely based on a few noisy scores or heuristic rules still fall short of perfection. To address this, we conducted in in-house annotation of over 100 samples to explore how humans can
"Results about QA2R and Q2AR tasks are in A.6.

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further refine answer candidates from Multimodal Distractor Generation. We established specific cri- teria for quality distractors and hired experienced annotators for iterative refinement (Details in A.3). Recognizing the improved quality through hu- man refinemett, we leverage the largely pre-trained ChatGPT (OpenAI, 2023) to mimic the human re- finement process. With rich context and 5 human- annotated examples input, the model can gener- alize for large-scale annotations".
object, we apply masking technique using the minimum circumscribed rectangles around its re- gion. The masked image then fed into the two SPL models for inference. Similarly, for generat- ing I-, we employ similar maskings over the rele- vant regions and infer to remove the corresponding objects. The reconstructed images from the two SPL models can be observed in Figure 2(b). To ensure fine-grained images and avoid superficial artifacts as addressed in prior works (Chen et al., 2020a; Liang et al., 2020; Gokhale et al., 2020), we adopt a coarse-to-refin, framework. After the first stage of inpainting with SPLp. we employ a tri-pass autoregression strategy during the infer- ence with SPL,- The masked input is iteratively passed through sPL, three times in an autoregres- sive manner, with smaller maskings applied in each subsequent pass. (c.f., Figure 3(a))
4.1.2 ADS-I
With UM and DS biases appearing to be dominant in text information, existing VL models are encour- aged to learn shortcuts in text and under-utilize the visual information leading false visual de- pendency, as in Figure 6a (Cao al., 2020; Wang et al., 2022c; Dancette et al., 2021; Chen et al.. 2020a). To assist models in mitigating UM and DS biases, we design ADS-I to synthesize positive images It and negative images I- to assist models' training and emphasize the correct question-related visual dependency. We introduce ADS-I in three components: region selection, coarse-to-fine region removal, and finetuning region remover.
Finetuning Region Remover. When finetuning SPL,, y, we first identify two kinds of visual regions: 1) regions within objects with no other objects on top; 2) regions in the background with no objects on top. Then, inside either kind of these regions, we create rectangular maskings of various sizes. During finetuning, apply those maskings over input images and supervise SPL, reconstruct inpaint the masked regions based on their neighbor- ing pixels and patterns (Details in A.4)).
Region Selection. To generate the synthetic factual images, denoted as It, ADS-I identifies and eliminates irrelevant visual regions referenced to the question-answer pair. Conversely, relevant image regions are removed to create I-. Following the procedures outlined in previous work (Chen al., 2020a), determine the relevance of image regions based on exact matching and soft scoring of the question-annswrr pairs.
4.2 Intra-sample Counterfactual Training
We propose ICT enhance models' training with counterfactual data to facilitate models to focus on intra-sample differentiation, which is essential in VQA-Long and other VLU tasks with rich context.
Coarse-to-Fine Region Removal. Previous studies (Chen et al., 2020a; Liang et al., 2020; Ye and Kovashka, 2021) introduce occlusion boxes masking tokens, leading to disruptive changes in data distribution and resulting in excessive ar- tifacts, illustrated in Figure 2(c). In contrast, we propose an innovative approach that transforms the data synthesis task into a visual inpainting task to generate photorealistic synthesized images. To achieve this, we leverage the SPL. visual inpaint- ing model (Zhang et al.. 2021a) as the base model, comprising two components: SPLp, pretrained on a large generalized dataset (Zhang et al., 2021 and SPL,, finetuned the specific downstream dataset. Our objective is to implement an effective coarse-to-refine framework utilizing both models to remove image regions iteratively.
4.2.1 XE Training
It is common to model VQA tasks as maximizing probability of answer label selection condition- ing the given image and question, P(a Q)- Thence, models often supervised to this proba- bility distribution with cross-entropy (XE) loss:
LxB = -, = y log (o
(2) where y² is the ground truth label, and a is the softmax function. With XE loss, directly bring I+, A+, and A- into training along with the original data.
4.2.2 Answer-focused ICT
Unlike VQA-Short, which focuses on global inter- sample differentiation, VQA-Long tasks empha- size local intra-sample differentiation. The task
To create It by removing a selected irrelevant

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Training
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ArowarRous
SPACE
Proieied Clicuder
Image-bous ICT
Gelrediou
Poss
Orgina kaage
Figure 3: (a) Diagram of Coarse-to Fine Region Removal. The left part illustrates the training of SPL, and the right part showcases the coarse- to-eettooieeee fine inferencing process. Within the triple- saple-pass autoregression strategy, the 1st pass includes a one-time inpainting step with the whole region masked, the 2nd pass includes inpainting steps with smaller masking over regions from the top left to bottom right iteratively. and the 3rd pass includes N steps with further smaller masked regions. (b) Diagram of all the combinations of (+/-I, Q, +/-A) pairs utilized training. The pairs from the top block are utilized in QA classification training.
focuses mapping the given premise informa- tion the correct answer among sample-specific candidate options. For instance, in VCR, models learn the local IQ >> A mapping, each (4i qi) pair against four specific answers, (am....., :;4); In SNLI-VE, the mapping exists between each image and specific candidate hypotheses. Unfortunately. the former methods do not address situations with sample-specific counterfactual candidate options. To incorporate them in VQA-Long tasks, pro- pose to use InfoNCE (Van den Oord et al., 2018) to measure the intra-sample contrastive loss, LA- ICr between each (it, 9:) against (a,, aataaaaaaa a;)):
We address this issue by transforming the IQ A mapping problem into QA I mapping problem, which we further narrow to the intra- sample pairing between each (gi, (4) pair and (4,, (-) similarly utilizing Eq. (3). Existing VL models often underuse visual information, lead- ing to poor visual explainability. By contrasting sample-specific (1,, i 4-), we highlight the sig- nificance of relevant visual regions the question- answer pair. This approach, exemplified in Figure 2, promotes recognition relevant entities, like the "bottle", and fosters the learning correct visual dependencies linked to the question- question-answer answer pair. Finally, after combing LA-ICT and LI-ICT, the overall objective is:
exp ($ (2.
- log ( ( (2, (T) + exp ( (2, /T)
6, Lxx + 82 (LA-IoT + Le-ser).
(4)
where measures the cosine distance, T is a hy- perparameter temperature, 2p is the [CLS] token feature for a positive pair, (, A) or A+), and nn is for a negative pair, is (I, A-). Synthesized answer candidates can have more balanced matched n-grams and more diverse dis- tractor distributions, requiring stronger model capacity to distinguish (Figure 3(b)). Therefore, Answer-focus ICT can encourage models to focus on this challenging intra-sample differentiation.
where dj and 62 hyperparameter weights. Experiment
Base models. Since ADS-ICT is generalizable and can be applied to various models, we evaluated it on several backbones: UNITER, (Chen al., 2020b), VL-BERT, (Su al., 2019), VILLA, (Gan et al., 2020) and LMH (Clark et al., 2019).
Datasets. We conduct bias analysis and valida- tion experiments over VCR (Zellers et al., 2019). SNLI-VE (Xie et al., 2019)3. Our identified bias problems also apply to other VLU benchmarks with long candidate options, and our methods even generalize VQA-Short datasets like VQA v2 and VQA CP2 v2. 5.1 Bias Verification
4.2.3 Image-focused ICT
To be aware, the direct augmentation of counter- factual images, in training through the afore- mentioned two training losses still remains unclear, we cannot find the paired answer choices for I-. Previous approaches in VQA v2 (Goyal et al., 2017) require generating new pseudo or soft answer labels (Chen al., 2020a; Gokhale al., 2020) for I-. However, they are not feasible for VQA-Long tasks such as VCR, which require sample-specific sentence answer options.
This analysis verifies how the aforementioned two biases affect models' learning and to what extent existing models can take the shortcut for advantage.
RRssult over VL.EP (1.ci et al., 2020) are provided in the appendix.

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Models
VCR
SNLI-VE
Models
VQA
v2
Val.
Fair
Adv.
Test
All
Y/N
Num
Other
Heuristics-Oni
66.29
48.70
43.93
69.77
69.30
LMH
56.34
65.05
37.63
54.68
VL-BERT
75.51
72.84
70.46
74,66
74.71
+CSS
59.91
73.25
39.77
55.11
+Answer Masking
75.89
73.48
71.59
75.09
75.21
+CC
57.29
67.27
38.40
54.71
+Question Masking
75.67
73.14
71.10
+SimpleAug
62.63
79.31
41.71
55.48
+ADS-T
76.23
73.69
72.18
75.52
75.60
+KDDAug
62.09
79.26
40.11
54.85
+Occlusion Box
76.07
73.67
71.83
75.24
75.52
+ADS-ICT
62.40
79.55
41.93
55.29
+ADS-I
76.88
75.03
73.00
75.90
75.96
+CSS
75.94
73.85
71.35
Table 4: Accuracies (%) VQA v2. Our method
+CC
76.04
74.29
71.72
can generalize to VQA-Short tasks and consistently improves over base models. indicates we only apply ADS-I and I-ICT over the base model.
+ADS-ICT
77.33
76.12
73.72
76.27
76.33
UNITER
76.72
74.99
72.48
79.02
79.19
+ADS-ICT
78.23
77.36
74.74
80.14
80.23
VILLA
78.28
76.67
74.05
79.64
79.32
become extremely high at 78.11% and 64.05%. DS bias. Like the identified hypothesis-only bias in text-only benchmarks (Belinkov al., 2019; Stacey et al., 2020), DS bias enables models to at- tain the ground-truth labels without visual and ques- tion inputs. To verify it, we train an Answer-only model, a RoBERTag (Liu et al., 2019) with only candidate options (both the correct and incorrect answers) input in VCR, and it achieves 51.84% Q2A accuracy (69% on SNLI-VE, and 61% on VLEP5). This verifies that the DS bias indeed exists. Secondly, using common feature space* (Reimers and Gurevych, 2019), we realize the average intra- sample similarity score between the correct answer and distractors within a sample is 0.31, and the av- erage iater-sample similarity score of every correct answer against its 1000th ranked similar answer candidate (a correct answer from different ques- tion) is 0.34. Moreover, the average intra-sample similarity score among distractors within the same sample is 0.36. This implies that (1) the correct answer can be overly dissimilar to the distractors within the same sample but much more similar to the correct answers to other questions: (2) distrac- tors are also overly similar to each other within the same sample.
+ADS-ICT
78.98
77.93
75.38
80.28
Table I: Accuracies (%) on VCR (Q2A) and SNLI-VE
based on our re-implementation. "Val" indicates the validation of VCR. indicates training with ICT to utilize counterfactual images. The results of Heuristics- Only are obtained by taking the best performance from mix of heuristic rules utilizing the two biases, e.g., the method always selects the option with the most matching n-grams.
A+ A- A-ICT It I- I-CT VCR
VCR
75.51
72.84
75,80
73.05
76.23
73.69
76.85
74.46
76.93
75.08
77.33
76.12
Table 2: Ablation study on UNITER,, A-ICT is answer- focused ICT, and I-ICT is image-focused ICT.
Models
VQA-CP CP v2 test
All
Y/N
Num
Other
LMH
52.01
72.58
31.11
46.96
+CSS
58.95
84,37
49.42
48.21
+CC
59.18
86,99
49.89
47.16
+SimpcAAug
53.70
74.79
34.32
47.97
+KDDAug
59.54
86.09
54.84
46.92
+ADS-ICT
61.03
87.94
57.02
48.29
Table 3: Accuracies (%) on VQA-CP v2. Our method can generalize VQA-Short tasks and consistently improves over base models. indicates we only apply ADS-I and I-ICT over the base model.
5.2 Debiased Evaluation
Debiased Evaluation Setting. We inference trained I-A, Q-A, and Answer-only models over the VCR validation set and extract samples that meet the following criteria: 1) None of the three models can predict correctly with confidence higher than 25% 2) The correct and incorrect answer choices have similar number of matched n- n-grams. We obtain subset of approximately 2.7K image-text pairs by filtering these conditions, and we consider this subset as a debiased evaluation set, VCRpair- without direct data augmentation. Lastly, we also
UM bias. We train two separate UNITERB mod- els in VCR: a Q-A model taking only questions and answer options as input, and an I-A model taking images and answer options as input. We find that the Q-A model and I-A model can achieve Q2A validation accuracy of 67. 20% and 59. 28%, respec- tively, which is much higher than random guessing. This validates the existence of shallow mappings inside (Q, A) or (I, A). We extract a subset of data where Q-A and I-A models have more than 90% confidence in predictions and find that Cf and Ce

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apply our ADS method top of this subset so that, on average, for each original {Q. A). we can generate four additional types of synthesized data, i... (I + /-, + /-}. This leads us to obtain around I I1K I-Q-A pairs for a domain-shifted eval- uation set, VCRAdv- To ensure the integrity of the data, we hired experienced MTurkers verify the correctness of every synthesized data in VdR
Cg
C2
C2
Cj
Simed
S
VCRtaa
66.29
29.16
42.75
40.23
0.31
0.36
VCR
48.70
32.05
42.36
40.01
0.32
0.34
VCRaa
43.93
41.09
39.28
38.94
0.35
0.33
Table 3: Analysis of UM and DS biases on VCR. Sime,d indicates the average semantic similarity between the correct answer and three distractors within a sample and Sima,d indicates the similarity within the distractors only, in Sec. 5.1.
Bias Mitigation. We re-analyze UM and DS biases to verify the dataset biases are mitigated. From Table 5, we observe that correct answers have a much similar frequency of obtaining match- ing n-grams than distractors against the premise information in VCR This improved balance becomes more noticeable when ADS is applied in VCR Moreover, the similarity between the correct answers and distractors has also increased, and the distractors become more diverse.
D: Why is evarynninn on the ports staring persond There mare peopa
A3 Enapssean the porzh surpteed socpamann. A4 Miaioonn and ohera are oraldo
Figure 4: An example from VCR and the paired visual Grad-CAM (Selvaraju et al.. 2016) result from fine- tuned VI.-BERT, (Su al., 2019). Based on the image, question, and correct answer, the most relevant entities are "person6" and then everyone on the porch.
5.3 Debiased Training
Benehmark Comparison. Results from Table indicate several key observations: (1) ADS-ICT can generalize to various VL models and consis- tently improve performance across different eval- uation settings; (2) The addition of ADS-ICT can bring even much more performance improvement on domain-shifted and debiased settings.
Models
Recall
Recall
Recall@3
VL-BERT,
46.83
59.35
67.75
+ADS-ICT
58.92
70.68
77.62
Table 6: Comparison of recall accuracy(%% (%) for recog- nizing the most question-relatee visual objects.
Other Dataset. ADS-ICT can generalize and improve performance over VLU tasks with long candidate options like ANLI-VE, as Table 8, and even VQA-Short tasks like VQA v2, as in Table 4. Debiased Method Comparison. Despite that former methods lack generalization to VQA- Long tasks, re-implement former techniques like masking and occlusion boxes, and methods like (Chen al., 2021; Liang et al., 2020) as in Table To ensure fairness, we even apply ADS- ICT over VQA v2 and VQA-CP v2 for a thorough comparison, as Table 4. We observe ADS-ICT delivers more significant gains over both datasets.
tioning on ADS-ICT, As Figure the Grad- CAM (Selvaraju et al., 2016) result indicates that the base model ignores the most relevant entity, "person6". However, after adding ADS-ICT, the model relies more on the relevant regions like "per- son6" and "person8". We further calculate the call accuracy of the model for retrieving the most relevant entities by comparing its attention values against object labels As in Table 12, we observe that the recall accuracy is significantly increased with ADS-ICT indicating that the model's visual explainability (dependency) has improved.
Ablation Study. Table 2 verifies consistent im- provement by adding components of our method. Notably, we find that augmenting counterfactual text data can bring greater improvement than fac- tual ones. This also emphasizes the importance of distractors in VQA-Long tasks.
Time Consumption
Our coarse-to- region removal method is flex- ible and generalizable as the number of runs of the image inpainting process can be adjusted de- pending on the scenarios to decrease the time con- sumption. After selecting the region to be removed, our proposed coarse-to-fine region removal will be conducted by two main steps: 1) Initial one-pass full region removal/ipaainting by S 2) Triple- pass autoregression region removal/inpainting by SPL,. The triple-pass autoregression strategy en-
5.4 Visual Explainability
We quantitatively and qualitatively verify the mod- els' visual explainability dependency condi-
"To save space, more details are presented in the appendix.

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Exp Index
Runs of
Time Consumption (m7s))
Accuracy (%)
SPL, (0
N)
1st (O)
2nd Pass (M)
Pass (N)
0
0
0
0
10
76.07
0
0
0
102
76.18
3
1
1
1
0
2 x 10a
76.30
1
14
1
9
1.3 x 10a
76.51
5
1
21
16
2.2 x 10
76.88
6
1
30
25
3.5 x 10
76.87
7
26
16
3 10a
76.88
Table 7: Time consumption comparison among variations of our coarse-to-tine region removal method. During this comparison study, the base model is VL-BERT (Su et al., 2019) running on one NVIDIA TITAN RTX GPU of GB. The input image is of size 224 x 224.
sures fine-grained images and avoids superficial artifacts, as addressed prior works. After the 1st run of full region inpainting by SPL,,, we split the region into smaller M regions evenly and run the inpainting process for each smaller region, respec- tively. A similar procedure applies to the third pass of N runs. Triple-pass autoregression is flexible solution, both M and can be trary positive integers (2 < M < .) depending on the situation decrease the overhead time con- sumption. Hence, The total runs of the inpainting process in the tr iple-pass strategy is 1 + M + N.
Conclusion
This paper analyzed dataset biases and their un- derlying causes in VQA-Long. Our findings shed light on how these biases can impact the evaluation of VL models and the importance of mitigation. We hope our work will inspire future research in developing rigorous data annotation processes and strategies to mitigate the influence of dataset biases.
any arbi-
Limitations
First of all. our proposed method, ADS-I, is de- signed to remove pertinent parts of visual regions to generate synthetic factual images, I+, and irrel- evant regions create I-. We adopted techniques from existing study (Chen et al., 2020) to complish this. However, some noise still persists, which might impact the accuracy of determining the relevant region. A promising next step might involve enhancing the quality of the generated im- ages by addressing these noise issues.
Based on Table 7, we have four observations: 1) As in Exp 1, If we do not conduct coarse-to-fine region removal (neither of the two steps will be applied), we essentially apply occlusion box over the selected region. This is the same approach the prior works and will cause relatively low downstream QA accuracy of 76.07 %; Com- paring Exp 1-3, we observe that adding the image inpainting process by SPLp and SPLy will both consistently improve the downstream performance; 3) Based on Exp 3-5, we can see a performance im- provement trend we apply more refined region inpainting via increasing the value of M and N; 4) Our method is tolerant to the variations of O, M and N and can still achieve noticeable performance improvement even when the overall number of runs is low as with time consumption of 2 x 102ms for one image. In practice, we found that setting M and N to and 16, respectively, generally achieves optimal performance while maintaining reasonable inference time consumption of 2.2 x 10d ms for one sample image However, as mentioned, the triple- pass autoregression strategy is flexible and gen- eral solution. Thus, O, M and N can be adjusted according to the actual situation to decrease time consumption and our method can still provide sim- ilar results.
Besides, to ensure the high quality of our con- structed debiased evaluation benchmarks, opted for manual verification, which consequently in- creased the overall cost of our research study. We anticipate that some cost-efficient yet reliable pre- selection procedure could developed to mitigate these costs. Additionally, the manual selection pro- cess could introduce a certain level of subjectivity into the dataset, which needs to be considered.
Ethics Statement
ChatGPT is pre-trained on the colossal corpus which is likely to contain potential racial and gen- der bias. Therefore, if someone finds our work interesting and would like to use it in a specific en- vironment, we strongly suggest the user check the potential bias before usage. In addition, it is hard to control the generation of LLMs like ChatGPT. We

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should be aware of the potential problems caused by incorrect predictions.
Processing (EMMLP-IJCNLP), pages 4069-4082, Hong Kong, China. Association for Computational Linguisties.
Acknowledgements
Corentin Dancette, Remi Cadene, Damien Teney, and Matthieu Cord. 2021. Beyond question- biases: Assessing multimodal shortcut learning in visual question answering. In Proceedings the IEEE/CVF International Conference on Computer Vision, pages 1574-1583.
This work is supported by DARPA MCS program under Cooperative Agreement N66001-19-2-4032.
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A
Appendix
A.1
Difference between VQA-Short (Classification) and VQA-Long (MCQ)
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Visual Question Answering (VQA) (Antol et al., 2015; Zellers et al., 2019; Lei et al., 2020) is a pop- ular VLU task where the premise information is provided, and the objective is to answer the ques- tion correctly. It is challenging as it requires rea- soning with the integrated information from visual
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'In this paper, "premise" refers to the given context or arguments. For text-only QA, the premise is the question. But for VQA, it consists of both the image and the question.

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Classification
Mulitple-choice question with the LONG answer (VCR, VLEP, etc.)
with the SHORT answer (VQA, GQA, etc.) What color the truck?
Why person2 looking down?
person
person
bottl
dining tables
White
Ale Personi is unhoppy about something miserable in relationship.
AZ: Person2 is drunk and looking down at dining tablee fintishig bottle
Static Class Label List Consisted by
Red
oo
Short Phrase Answers
A3: Person2 mourning the loss of loved one at dimal. type down
comteputingg
Matching v.s.0
Matching v.s.l Not Relevant I-O
Matching Score
Correct
Incorrect
0.25
0.5
(a)
(b)
Figure 5: (a): Prior studies examined dataset bias in the distribution of short- answers (e.g. "white" is often the answer when asking about color). (b): Our work investigates the biases in VQA with long answer choices, where the correct answer has more n-grams overlapped with the image and question (as shown in BurntOrange and ForestGreen). Meanwhile, the incorrect answers contain more irrelevant n-grams to the scene (as shown purple).
Porson6
Person8
Q: Why is everyone the porch staring at person6 ?
Person
A1: There are many people outside to watch the sky.
A2: The street cleaning.
A3: Everyone on the porch are surprised person6.
0.2
A4: Person8 and others are outside.
0.0
Baseline
Baseline
Baseline ADS-ICT
Figure 6: An example from VCR and the paired visual Grad- (rad (Selvaraju al., 2016) result from a finetuned VL-BERT (Su et al., 2019). Based on the image, question, and correct answer, the most relevant entities are "person6" and then everyone on the porch. With training, we expect the VL model to integrate multimodal information and commonsense when selecting the correct answer (labeled by green check). For instance, answer this question, we expect the model to focus on [person6] on the left and people in the center. However, the model fails to pick the correct visual clue and focuses on irrelevant entity, the window overlapping words between the correct choice and the question.
background. The orange words are the

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and text modalities. Most of the existing VQA benchmarks conventionally use either of the two settings: I) Classification: As in the top of Figure 5, in this setting, different questions share one set of context-free answer choices, egg. VQA v2 (Goyal et al., 2017), GQA (Hudson and Manning, 2019), and OK-VQA (Marino et al., 2019), and models are supervised for classification using the shared answer choices as fixed class labels. Accordingly, generate a common answer dictionary, the an- swer choices are limited to be "short" words or phrases, and the questions tend to focus on visual perception. 2) Multiple- Question (MCQ): As in the bottom of Figure 5, every question has a unique set of context-dependent answer choices, e.g. four sample-specific sentence-level answer choices in VCR. Models are trained with general- ized objective, cross-modality matching, predicting the degree of match between each answer choice and the premise information. This is similar to other VLU tasks, such as visual reasoning (Suhr et al., 8) and visual entailment (Xie et al., 2019). Due to this flexibility, the questions tend to be di- verse, highly semantic, and centering around hu- man activitics, e.g. VCR (Zellers et al., 2019), VLEP (Lei al., 2020), MovieQA (Tapaswi et al., 2016). TVQA (Lei et al., 2018), and TVQA+ (Lei et al., 2019). Accordingly, the answer choices tend to be "long" and include rich context, and the im- ages are more complex with multiple entities and diverse scenarios. The second setting is analogous to the MCQ problems humans face and, thereupon, can more likely generalize to real-world scenarios. Recently, the field has advanced towards generating highly semantic and challenging VL benchmarks mimicking real-world challenges, and the second setting is becoming increasingly important.
question pairs into fixed list of class labels con- sisting of short-word answers. Hence, they can easily divert to capture shallow correlations focus- ing on different question types and their mapped label distributions for it is easier for models to learn shallow correlations within the same modality, text. As the analysis at the top of Figure 5, we find that questions starting with "What color is the" will often be classified to the label, "white" regardless of the actual context in VQA v2 (Agrawal et al., 2018).
However, these bias problems may not apply to the MCQ setting. Some of the main reasons in- clude the following: The questions are much more diverse and difficult to be categorized into basic types; The answer choices are not only dynamic and context-dependent but also contain much more information even than the questions. Thereupon, they have to be in the input not as fixed class labels. Yet, in this work, we argue that, in the MCQ setting, some other bias-related problems are more likely to form and supervised models are more vulnerable With more input types, i.e. (Image, Question, An- swer), more implicit correlations in between may likely form. On the other hand, the MCQ setting re- quires generating long context-dependent incorrect choices (distractors) for each sample, and this fur- ther brings in much more complexity and artifacts. As verified by our study, we discover that bias- related problems in the MCQ setting are centered around the answer choices and their relationships against the premises. In addition, we believe it is very important to encourage future works to focus on analyzing the bias problems within VLU tasks with the MCQ setting.
The former work (Ye and Kovashka, 2021) points out that the correct answers have more exact matching entities (mainly pronouns like "he/she, 'they'. etc.) against the questions in VCR, re- ferred the bottom of Figure 5 where the orange- highlighted text represents the overlapping enti- ties against the question. Despite how shallow this may seem, we verified that a simple heuris- tic rule of picking the choice with the most over- lapping entities could deliver 66.15% Q2A accu- racy and 63.04% QA2R accuracy on VCR. Nev- ertheless, (Ye and Kovashka, 2021) mainly con- strained to resolve the explicit advantage of the correct answers containing more exact matching pronouns in the text modality solely (between ques- tion and answer choices) of VCR. Therefore it does
Although recent VL models (Li et al., 2023, 2022: Wang et al., 2022a) can achieve good per- formance on many above-mentioned benchmarks, they still suffer from bias issues. Many studies have discussed the bias issues in Classification-VOM tasks (Chen et al., 2020a; Dancette et al., 2021; Gokhale et al., 2020; Gupta et al., 2022; Niu et al., 2021; Ramakrishnan et al., 201 8), but none of them investigate in the MCQ setting, with the exception of recent study (Ye and Kovashka, 2021). In the classification setting, as in VQA v2, supervised models do not actually see the answer choices at all in the input but only the images and questions. In other words, they are trained to classify image-

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not investigate and generalize to problems about the correlation among general components in the text, other cross-modal correlations between the premise and ground-trune trtttth-tthhhuo annotations(, egg. the green-underscored text at the bottom of Figure 5), other VLU benchmarks with the MCQ setting and, most importantly, the fundamental problems cen- tering around the generation process of distractors.
choices and under-utilization of visual information leading to incorrect visual dependencies, we pro- pose a novel Adversarial VL Data Synthesis (ADS) method consisting of ADS-T to generate synthetic factual and counterfactual text data and ADS-I for image data. ADS-T can directly assist the gen- eration of long-sentence answers and distractors. and ADS-1 can generate synthesized images closer to real images with minimized data distribution via the semantic focus of the query. Addition- ally, former debiasing methods (Chen al., 2020a; Dancette et al., 2021; Gokhale et al., 2020; Gupta et al., 2022; Niu et al., 2021; Ramakrishnan et al., 2018) directly augment the synthesized counterfac- tual questions and images, as 7- and Q- in Figure. 2, in the input for model training, as they do not need to find explicit paired answers. However, this does not apply the MCQ setting because explicit answer choices are required as the paired input data for I_ and Q- Nonetheless, is impossible to find paired answer choices without additional manual annotations, as referred to and Q in Figure. 2. In this work, we successfully resolve this challeng- ing problem by proposing ICT strategy to utilize the synthesized counterfactual data in the MCQ set- ting. Lastly, with human verification, we employ ADS to create domain-shifted evaluation bench- marks based on VCR to test existing and future VI. models' robustness. Although we primarily present analysis and experiments across VCR, SNLI-V VE, and VLEP benchmarks, our identified problems and proposed solutions can be generalized to other VLU tasks with rich context.
To fix these issues, we first conduct a compre- hensive amalysis across several VLU benchmarks iee. VCR (Zellers et al., 2019), SNLI-VE (Xie et al., 2019), and VLEP (Lei et al., 2020). Among them, we identify two common but severe prob- lems: (1) The Unbalanced Matching (UM) prob- lem: Answer choices have an unbalanced matching of n-grams against the premises. This includes the problem of the correct answers containing more matching nouns, adjectives, etc. and more n-grams like phrases. On the other hand, distractors have not only fewer matching n-grams but also much more n- n-grams not related to the scene at all; (2) The Answer-only Bias problem: We realize that distractors are often generated without sufficient visual premise as the correct answers, filtered by simple metrics and modified by the same set of heuristic rules, which leads to lack of diversity and frequent usage of certain words phrases. Con- sequently, distractors are excessively dissimilar to the correct answers and over-similar to other dis- tractors within the same sample. We systematically analyze these problems and their causes backed with statistical evidence and further conduct experi- ments to demonstrate their effect on models' learn- ing of biases. Furthermore, due to the bias-related problems heavily centering around answer choices and distractors also tend to reference entities or concepts not related to the scene, we discover that existing VL models may under-utilize visual infor- mation and struggle to learn query-related visual dependency, as shown in Figure. 6.
A.2 Implementation Details
For object labels in VCR, we combine the provided annotated object labels with the generated ones as in (Anderson et al., 2018). When training SPL, on downstream dataset, e.g. VCR(Zellers et al., 2019), we skip visual regions whose area is smaller than 1/64 of the whole image. In Eq. 1, A is set to be I and would be set to be 0.4.
The former data synthesis approaches (Chen et al., 2020a; Dancette et al., 2021; Gokhale et al., 2020; Gupta et al., 2022; Niu et al., 2021; Ramakr- ishnan et al., 2018) 1) fail to address the bias problems in the MCQ setting. Moreover, they either only fo- cus on the text modality, cannot resolve problems related to long-sentence distractors, severely dis- turbed the data distribution via superficial masking on text or occlusions on images, as in the right of Figure 2. Differently, to assist models in coun- tering the bias problems centering around answer
In training with ICT over our synthesized data, for each (I, Q, A) pair, with ADS, each image would result in an average of one positive and one negative synthesized image; The original four an- swer choices would also result in one synthesized positive answer choice and three synthesized nega- tive answer choices. As shown in Fig. 7, every orig- inal (1, Q, A) sample data would result in seven additional samples for training with classification

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loss and three for contrastive loss.
A.4 Coarse-to-Fine Region Removal
After determining the relevant regions in images, following the structure of SPL (Zhang et al., 202 we design a framework remove rele- vant and irrelevant regions to generate realistic nat- ural images, I+/- In this framework, we reserve two SPLs, an Lp pretrained on Places2 (Zhou et al., 2017) as in (Zhang et al., 2021a) and amother S finetuned on VCR. We first retrieve the seg- mented polygons (if the entity is provided by VCR annotation) bounding boxes (if generated) of the selected relevant entities. After determining the dimensions of the maximum inscribed rectangles within the polygons or boxes, we create correspond- ing rectangle maskings in ratios, (0.7, , 0.5, 0.3) of the maximum dimensions. Similarly, we also cal- culate the maximum dimensions of inscribed rect- angles in regions that have no entity overlapped on top at all and create maskings of different ratios within those regions. When fine-tuning SPL, on the VCR training set, we input images with regions masked by one of those maskings and supervise SPL, to reconstruct the masked region. There- fore, essentially SPLy is trained to reconstruct the interior of either an entity or an open background region based on its neighhoring non-masked pixels and patterns. In order to create It, in inferencing, filter irrelevant regions, R (s:) R E L and create maskings of the minimum circumscribed rectangles around the boxes or polygons. We then feed images masked by those maskings into SPLs to remove the irrelevant entities. Similarly, For creating 1-, we create similar maskings over the relevant regions from REL to inference to remove the relevant entities. Some examples of the recon- structed images by SPL are shown in 7.
A.3 Multimodal Distractor Refinement.
After obtaining more diverse candidate answers from Multimodal Distractor Generation, we con- ducted an in-house study of over 100 samples to test how humans can polish those candidate an- swers into quality distractors. We determine the criteria of quality distractors in each sample of VQA-Long to be: (1) Similar the correct answer and containing a comparable amount of matched n-grams; (2) Relevant to the given context and not containing irrelevant n-grams to the scene; (3) Not false negative to cause ambiguity; (4) Having de- scent diversity to be different from each other. With those criteria, for each sample, we have group of three experienced annotators and ask each to anno- tate three quality distractors. Following, for each annotated sample, we ask another group of 2 expe- rienced annotators to rate the distractors based on the four criteria and send back the unqualified ones to the first group to redo the annotation until they suffice. For each sample, we provide annotators rich references as the input information, including (1) The original sample image, question, and an- swer; (2) The retrieved available caption for each image; (3) The matched n-grams between the cor- rect answer and the premise information; (4) The top 10 most salient extracted object labels; (5) The top 10 ranked answer candidates by Multimodal Distractor Generation.
With this study, we realize that quality distractor generation is challenging and much harder than answer generation, as it requires complex common- sense reasoning and knowledge. Thus it is diffi- cult to rely on one or two noisy scores or heuristic rules as the former solutions to secure quality dis- tractors. With these insights, we employ largely pre-trained ChatGpt (OpenAI, 2023) and expect to mimic the generation process of humans. Ac- cordingly, we provide the input with rich refer- ences to the model as to humans via the prompt. We also handpicked 30 human-annotated examples and each time randomly select 5 of them as the examples. Thence ChatGPT has the five types of references as the input and the three qualified dis- tractors the output. With sufficient multimodal references and examples as constraints, we find this approach effective in generating quality distractors with the two biases mitigated and can apply for large-scale annotations.
In order to produce fine-grained images and avoid drastically disturbing existing image distribu- tion and bringing obvious artifacts/biases as occlu- sion boxes do in (Chen et al., 2020a; Liang et al., 2020; Gokhale et al., 2020). in practice, during in- ferencing, we apply a coarse-to-refine strategy by first passing the masked images into SPLp that was pretrained on a larger dataset and then feed the reconstructed output to SPI. that was fine- tuned more specifically to refine the reconstruction. As inferencing in SPL,, we additionally refine the image via triple-grid mechanism. As in Fig. 3, for a given masked region, we evenly split it into M blocks and N blocks respectively where 2 < M < N. In the first pass, we allow SPL, to

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Answer-focused ICT
(Ac,
Original
(AA)
P
(Ac,3A-)
tarch
33-)
Inaue-focused ICT
Ours
(Ac, 3Ad)
Original
Q: What is [person2] doing?
Ac: He is going to burn [penson3]
friend of [person3]
Ours
Q: What is [person2] doing?
Ac: He will light a fire on [person3]
Presious
Air He is an acquaintance [person3]
Previous
Q: What is [person2] [MASK]]
Ac: He will [MASK] fire on [person3]
Ai: He is an [MASK]
Figure 7: Diagram of combinations of (I, Q, A) pairs utilized in training. The pairs from the top block are utilized in QA classification training. The pairs in the bottom block are used in intra-sample contrastive learning. The orange lines indicate the overlapping/repeated pairs. In practice we only pass those pairs once in the feedforward operations. Ac represents the correct choices. Ai represents the incorrect choices.

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reconstruct the whole masked region in one pass and then revisit the same region with smaller mask- ings in the following two passes. In the second pass, we take turns to turn each of the M blocks in order into a smaller masking, from the top left to the bottom right, and accordingly reconstruct each masked region to refine. Note that when we reconstruct the first block from the top left of M blocks, the visual regions of the rest M 1 blocks are not masked but in-painted with results from the former pass as placeholders. Therefore, we cu- mulatively inference M times to refine the whole region in the 2nd overall pass. similar proce- dure is carried out for the third pass with N blocks. This method allows the framework to maintain the global consistency in reconstructed visual patterns while obtaining the flexibility in refining smaller regions.
every synthesized data in VCRady to avoid any superficial errors and artifacts.
A.6 Benchmark Evaluation
Referring to Table. 8.
A.7 Problem with Lexical Similarity in Adversarial Matching
The difficulty of generating long sentence distrac- tors is prominently higher than short noun dis- tractors. For facilitating it, Adversarial Matching (Zellers al., 2019; Williams et al., 2022; Lei al., 2020), where positive answers are recycled to serve as negatives for other questions, has been recently adopted as a popular method. generally leverages the difference between two language models' pre- dicted probabilities to represent the relevance score, S,el (p. r) of a response r against the text premise and the similarity score, Ssim (c, r) between the response against the correct answer The differ- ence between these two would be used for the final ranking, A (f(r ensure the selected distractors dissimilar to the cor- rect answer but relevant to the text premise, where À and a are hyper-parameters and f as well as g are fixed functions.
A.5 Domain-shift and Debiased Evaluation Benchmarks
(Semm(c, r)) a g(Semm (c, r))),
We first adopt the formerly published evaluation setting via changes of pronouns (Ye and Kovashka, 2021), which we refer to as VCRpronoun Shift. Further, we then use previously finetuned stylish models, I-A, Q-A, and A-only models, perform inference on the standard VCR validation set. using the confidence scores of these models, we extract samples that meet the following criteria: 1) Neither the I-A nor Q-A model can predict correctly with confidence higher than 50% over these samples, 2) the A-olyy model also cannot predict correctly with confidence higher than 25%, and 3) the cor- rect and incorrect answer choices have similar number of matched n-grams. Filtering using these three conditions, we get subset of approximately 2.7K image-text pairs. This adversarial filtering mitigates the effect of UM and Answer-Only bi- ases on this subset, and we consider it as a debiased evaluation setting, referred to as VCR without direct domain shift.
Despite the claimed effectiveness, profound problems exist with this method. Insufficiency of Scores: First of all, the two scores come from different sources and are not normalized, thus ob- viously causing integrity. Secondly, the scores are not fine-grained and semantically reliable, as Fig. 8, for the false positives, despite the fact they may possess critical conflicts against the text premise, those high-qualig, distractors would be eliminated. In contrast, the first false negative, para- phrasing the original text premise, incorporates the exact meanings but is deemed less "similar." To avoid false negatives, in practice, the second term, a g (S.im (c. r)) would be profoundly emphasized, resulting in a specific selection window favoring lower-ranked candidates and unavoidably eliminat- ing high-quality distractors. Owing to the same window's origin, these distractors lean to share similar traits, especially after the similar heuristic modification with templates.
Lastly, we apply our ADS method on top of this subset so that, on average, for (Ae, each original (Ac, At)}, we can end up with four combinations of synthetic data, (Q,4, (Ac, As)}. i.e. (A, I, A-)). This leads obtain around 11K I-Q-A pairs for another domain-shift evaluation, VCRAdv- To ensure the integrity of the data, we hired experienced Amazon Turkers to verify the correctness of
A.8 Bias Analysis
As shown in Table. 9, it is obvious that the iden- tified two types of biases, UM and Answer-only biases are common across benchmarks. With the aid of ADS, we observe significant improvement

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Model
VCR
VCRp Jain
VCRFai
VCRMd
SNLI-VE
Q2A
QA2R
Q2AR
Q2A
Val
Test
Heuristics-Only Only
66,29
65.98
43.74
49.75
48,70
43,93
69.77
69.30
VL-BERT2
75.51
77.95
58.86
71.13
72.84
70.46
74.66
74.02
VL-BERTH, ADS-ICT
77.33 [+1.82]
79.93
61.80 [+2.94]
74.26 (+3.13]
76.12 [+3.28]
73.72 [+3.26]
76.27 [+1.66]
76.33 (+2.31]
UNITER
76,72
80,01
61.38
73.84
74.99
72.48
79.02
79.19
UNITERs ADS-ICT
78.23 [+1.51]
82.29 [+2.27]
64.37 [+2.99]
76.81 [+2.97]
77.36 [+2.37]
74.74 [+2.26)
80.14 [+1.12]
80.23 [+1.04)
VILLAz
82.20
75.43
77.01
74.05
79.32
VILLAg ADS-ICT
78.89 [+0.61]
82.77 [+0.57]
68.30 [+0.966]
76.80 [+1.377
77.75 [+0.74]
75.38 (+1.33)
80.87 [+1.233
80.28 [+0.96]
Table 8: Our comparisons against benchmark methods are based on our own re-implementation. QA accuracy is adopted for VCR-related benchmarks over Q2A, QA2R, and Q2AR tasks. For the SNLI-VE benchmark, we accuracy based on classification over three labels: entailment, neutral, and contradiction. The results of Heuristics-Onlyy are obtained by taking the best performance from a mix of heuristic rules utilizing the two biases. For example, the method always selects the option with the most matching n- n-grams
Dataset
Percenlage of ffighest Matching Tokens
Aug. Matching Tokems Question-maage
Percenlage of Highest Matching Tokens
Avg. Matching Tokens Question-Image
Question
vss. Image
Question
Image
YCR
Corrai
66.29
42,75
242
43.93
39.28
1.95
29.16
40,23
1.
41.09
38.94
VA. Caption
V.A Image
vs. Caption-Tnage
V& Caplion
Yo. Inaage
v.s. Caption-Imape
SNLI-VK
Emsiil
09:77
57.77
4,11
57.41
52,59
3.82
Contradict-Nuut
3.23
T.S. Subtitles
VLEP
Incecreet
$5.19
11.76
39,01
11.26
Table 9: Bias Analysis over three benchmarks with ADS
Fashion Model
Datuset
ADS
w/ ADS
$1.84
52.53
VCR
(Q2A)
(QA2R)
(Q2A)
(QA2R)
A-Only
SNLI-VE
69.36
54,22
VLEP
61.18
53.37
67.21
69.4
61.98
63.89
QA-Only
SNLE-E
76.93
IA-Only
VCR
59.28
64.02
62.95
Table 10: Evaluation of fashion models over bench- marks with ADS. A A-Only stands for answer-only. QA- Only stands for Question-Answer-Onlyy IA-Only stands for Anwaee--nsse--Oly.
w/oo ADS
W/ ADS
Dataset
# Irrelevant i-grams v.s. Premise
Correct
0.18
0.18
VCR
Incorrect
1.14
0.8
0.15
0.15
SNLI-VE
Or Viiinll Text
A Burber with g glsses standing al his counter while
Contradict neutral
1.49
0.74
snother waiting
Correct
0.68
0.68
False Positives
A Baber with gloses standing al his counter while
another waman wakking bebber
0,821
VLEP
Incorrect
1.03
0.75
A Barber with sunglasses standing si his counter whtl cher.
0.914
snother man weiling in
Table 11: Analysis of irrelevant n-grams over bench- marks with ADS
A Borber with computer glasses is standing at his counter
0.903
while another man walling in the gaming chair.
False Nogatives
A male oustomer is sttingg at chair walting for the hatrout
0.505
stylist will glasses standing next to him
0.361
Indoor scene of two men, one which looks
over the overlapping n-grams between the correct answer choice and the incorrect ones against the premise information (including both visual and text premise information).
younger mustache while
older
baid head
Figure 8: Analysis over the lexical similarity between hypotheses (answer choices) against an example image from SNLI-VE.
If we further re-train the three fashion models across the three benchmarks in Table. 10, the performance of the three fashion models all drop consistently. This further verifies the mitigation effect of ADS over the two biases.

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As in Table 11, we conduct a preliminary exper- iment by first hiring three experienced annotators and randomly sampling 100 samples for each of the three benchmarks. For each sample with the vi- sual and text premise (image and question in VCR) and the answer choices, we extract all the possi- ble n-grams from the answer choices. Following this, we ask each annotator to judge how many of the n-grams are relevant to the visual and text premise information. The overall averaged result is presented in Table 11.
son6". After adding ADS, the model appears to rely more on the regions of "personó" and "person8". If we further apply ICT, the model demonstrates a more obvious (confident) visual dependency over the most relevant entities. To quantify the improve- ment in the model's visual explainability and its ability to capture correct visual dependency, we calculate the recall accuracy of base models for rec- ognizing the most question-related visual objects. We retrieve the grounded entities in the questions provided in VCR as the ground-truth labels, extract the attention values base models over each vi- sual token in the last hidden layer's output, and compare the attention values against the labels to calculate the recall accuracy. As shown in Table 12, we observe that the recall accuracy is signifi- cantly increased with ADS-ICT', indicating that the model's ' visual explainability has improved, and has learned the correct visual dependency.
Distractors Have More Irrelevant n-grams. In addition to lower n-gram overlap with the premise, we observed that distractors also contain more grams that are irrelevant to the given context. As shown in Figure 11, some n-grams, like "ritual" and "ccoppter", have no clear association with the image or question. Our analysis, employing both human and pre-trained models, reveals that, average, distractors possess 1.14 n-grams that are irrelevant to the premise, which is significantly higher than the 0.18 n-grams for the correct answer Distractors with Limited Diversity. Since dis- tractors tend to be generated without visual premise information in AM (Lei et al., 2020; Zellers et al., 2019; Li al., 2020) but only the text, there is limited information as the reference for selecting diverse distractors. This becomes more severe in cases where the text premise has searce informa- tione.g., the questions with short stems in VCR like "Why is person2 looking down?" in Figure 5 or "What is going to happen next?". Besides, in AM, the top selected candidate answers would then be modified based on heuristic rules, evolving token-level replacement, and these invariant rules can further lead to over-similar production. On the other hand, without being given the image in the manual annotation in (Xie et al., 2019; Do et al., 2020; Kayser et al., 2021), annotators are forced to come up with imaginary content to add to the distractors. The diversity solely relies on the anno- tators' discipline and imagination abilities with no constraints, and this, unfortunately, leads lazy annotations with distractors containing repeated and irrelevant n-grams.
Model
Recall@
Recall Recall@2
VL- VL-BERT, VL-BERT, ADS-ICT
46.83
59.35
67.75
58.92
70.68
77.62
UNITERg
49.93
66.86
71.55
UNITER ADS-ICT
60.90
75.93
80.47
Table 12: Visual explainability. We calculate the recall of models by retrieving the most relevant entities response to given image, question, and answer pair.
A.9 Visual Explainability
Figure 6 visualizes the Grad-CAM (Selvaraju et al., 2016) results of a base model. The Grad-CAM result of the baseline model indicates that does not pay attention to the most relevant entity, "per-


Reviews:
[{"review_id": "hqY7DHpCSO", "reviewer": "Reviewer_4SCZ", "summary": "", "questions": "A. what is the specific time overhead for the triple-pass autoregression strategy?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Time Consumption:**\nOur coarse-to-fine region removal method is flexible and generalizable as the number of runs of image inpainting process can be adjusted depending on the scenarios to decrease the time consumption. In practice, we found that setting M and N to 4 and 16, respectively, generally achieves the optimal performance. The time consumption is 2.2x10^3 ms for one sample image of size 224x224 with a VL-BERT_Large model running on one NVIDIA TITAN RTX GPU of 24 GB memory. \n\n**Time Consumption Analysis:**\nAfter the region is selected for removal, our proposed coarse-to-fine region removal will be conducted via two main steps: 1) Initial one-pass full region removal/inpainting by SPLp; 2) Triple-pass autoregression region removal/inpainting by SPLf. The triple-pass autoregression strategy ensures fine-grained images and avoids superficial artifacts, as addressed in prior works. After the 1st run of full region inpainting by SPLf, we split the region into M evenly split smaller regions and run the inpainting process for each smaller region, respectively. A similar procedure applies to the third pass of N runs. However, in reality, triple-pass autoregression is a flexible solution as both M and N can be set to any arbitrary positive integers (2<=M<=N) depending on the situation to decrease the overhead time consumption. Hence, The total runs of the inpainting process in the triple-pass strategy is 1 + M + N. \n\nFor instance, we conducted the following settings during our study to figure out the trade-off. The base model is a VL-BERT_Large model running on one NVIDIA TITAN RTX GPU of 24 GB. The input image is of size 224x224.\n\n| Exp Index | **# Runs of SPLp** | **# Runs of SPLf (1 + M + N)** | 1st Pass | 2nd Pass | 3rd Pass | Time Consumption (ms) | Accuracy(%) |\n|-----------|:------------------:|:------------------------------:|----------|----------|----------|-----------------------|-------------|\n|           |                    |                                |          | M        | N        |                       |             |\n| 1         | 0                  | 0                              | 0        | 0        | 0        | 10^1                  | 76.07       |\n| 2         | 1                  | 0                              | 0        | 0        | 0        | 10^2                  | 76.18       |\n| 3         | 1                  | 1                              | 1        | 0        | 0        | 2x10^2                | 76.30       |\n| 4         | 1                  | 14                             | 1        | 4        | 9        | 1.3x10^3              | 76.51       |\n| 5         | 1                  | 21                             | 1        | 4        | 16       | 2.2x10^3              | 76.88       |\n| 6         | 1                  | 30                             | 1        | 4        | 25       | 3.5x10^3              | 76.87       |\n| 7         | 1                  | 26                             | 1        | 9        | 16       | 3x10^3                | 76.88       |\n\nBased on the above experiments, we have four observations:\n1. As in Exp 1, If we do not conduct coarse-to-fine region removal (neither of the two steps will be applied), we essentially apply an occlusion box over the selected region. This is the same approach as the prior works and will cause a relatively low downstream QA accuracy of 76.07.\n2. Comparing Exp 1-3, we observe that adding the image inpainting process by SPLp and SPLf will both consistently improve the downstream performance.\n3. Based on Exp 3-5, we can see a noticeable performance improvement trend as we apply more refined region inpainting via increasing the value of M and N. \n4. In practice, we found that setting M and N to 4 and 16, respectively, generally achieves optimal performance while maintaining reasonable inference time consumption.\n\nWe will add these parts of the experiment results and expand our discussion on the experiment section, given an extra page upon acceptance. As mentioned, the triple-pass autoregression strategy is a flexible and general solution. Thus, M and N can be set according to the actual situation to decrease time consumption. We also admit that achieving optimal performance while maintaining minimal time consumption is still a challenging and not fully resolved problem for future work.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper analyzed the biases in the VQA tasks with long answer formats in detail.\nThe paper proposed ADS-ICT to address the data bias problem with generative adversarial samples and enhanced loss function. These design is validated by extensive experiments for VQA-Long tasks.", "reasons_to_reject": "The triple-pass autoregression strategy seems to be time-consuming and lead to inference overhead.\nThe general performance improvement is not obvious against previous method.", "questions_to_authors": "A. what is the specific time overhead for the triple-pass autoregression strategy?", "paper_topic_and_main_contributions": "The paper proposes a novel method, ADS-ICT, to address biases present in Visual Question Answering tasks with long answer formats (VQA-Long) that consists of two parts, ADS and ICT. ADS comprises two components, ADS-T and ADS-I, which generate synthetic factual and counterfactual text data and images, respectively. ICT utilize a contrastive InfoNCE loss and generated factual and counterfactual samples to train the VQA model. The components together improved the performance on VQA-Long tasks and a new benchmark proposed to evaluate the bias-robustness of models for VQA-Long tasks.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "eFN7JZ8idE", "reviewer": "Reviewer_PvtF", "summary": "", "questions": "1. Why you do not select some Sota models, such as ofa or blip2?", "limitations": "", "rating": 3, "confidence": 2, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**SOTA Models:**\nOur method is a generalized data synthesis method that is model-agnostic and can be applied over multiple different base methods. Hence, our objective of this work is not to achieve the SOTA performance on any specific benchmark task but to prove the effectiveness and generalizability of our method. In our initial submission, we selected those high-performing base models specified in the paper as they can be generally applied over VQA-Long benchmarks like VCR, SNLI-VE, etc. The obvious and consistent improvements over multiple high-performing base models have already validated the value of our method. \n\nTo further illustrate the effectiveness of our solution, we also conducted additional experiments by applying ADS-ICT over SOTA models like OFA_Large and blip2_flanxxl-t5. We find that OFA_Large can achieve the improved performance of 81.48 with ADS-ICT over the base performance of 80.13 on VQA v2 val and the improved performance of 91.08 with ADS-ICT over the base performance of 90.03 on SNLI-VE dev. Meanwhile, BLIP-2 ViT-G FlanT5 XL can achieve the improved performance of 82.14 with ADS-ICT over the base performance of 81.20 on VQA v2 val dataset. These experiments further verify the advantage of ADS-ICT. \n\nWe will provide more details of all the experiment results with SOTA models and expand our discussion on the experiment section, given an extra page upon acceptance.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This paper analyzed the dataset biases problem in vqa-long, and provide some methods to address the problem.", "reasons_to_reject": "1. The backbone model is somehow out of date.\n", "questions_to_authors": "1. Why you do not select some Sota models, such as ofa or blip2?", "paper_topic_and_main_contributions": "This paper proposes Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data and introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing the synthesized training data.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "nxwl5MqxIn", "reviewer": "Reviewer_32Fg", "summary": "", "questions": "1. Can you provide the time consumption of your proposed method with other baselines?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Noises in Synthetic Images:**\nCompared with prior works, our method actually tremendously decreases the artificial noises and minimizes the disturbance of image distribution. Specifically, unlike previous solutions of naively applying occlusion boxes, we innovatively create factual and counterfactual images via image inpainting. Even trivial noises may still exist, but the apparent performance improvements by augmenting our synthetic images in training over multiple base models can already prove the quality and value of our generated images. Meanwhile, we also conducted human verification over the generated synthetic images (3 annotators for each sample) in the validation set to ensure the evaluation quality further.\n\n**Time Consumption:**\nOur coarse-to-fine region removal method is flexible and generalizable as the number of runs of image inpainting process can be adjusted depending on the scenarios to decrease the time consumption. In practice, we found that setting M and N to 4 and 16, respectively, generally achieves the optimal performance. The time consumption is 2.2x10^3 ms for one sample image of size 224x224 with a VL-BERT_Large model running on one NVIDIA TITAN RTX GPU of 24 GB memory. \n\n**Time Consumption Analysis:**\nAfter the region is selected for removal, our proposed coarse-to-fine region removal will be conducted via two main steps: 1) Initial one-pass full region removal/inpainting by SPLp; 2) Triple-pass autoregression region removal/inpainting by SPLf. The triple-pass autoregression strategy ensures fine-grained images and avoids superficial artifacts, as addressed in prior works. After the 1st run of full region inpainting by SPLf, we split the region into M evenly split smaller regions and run the inpainting process for each smaller region, respectively. A similar procedure applies to the third pass of N runs. However, in reality, triple-pass autoregression is a flexible solution as both M and N can be set to any arbitrary positive integers (2<=M<=N) depending on the situation to decrease the overhead time consumption. Hence, The total runs of the inpainting process in the triple-pass strategy is 1 + M + N. \n\nFor instance, we conducted the following settings during our study to figure out the trade-off. The base model is a VL-BERT_Large model running on one NVIDIA TITAN RTX GPU of 24 GB. The input image is of size 224x224.\n\n| Exp Index | **# Runs of SPLp** | **# Runs of SPLf (1 + M + N)** | 1st Pass | 2nd Pass | 3rd Pass | Time Consumption (ms) | Accuracy(%) |\n|-----------|:------------------:|:------------------------------:|----------|----------|----------|-----------------------|-------------|\n|           |                    |                                |          | M        | N        |                       |             |\n| 1         | 0                  | 0                              | 0        | 0        | 0        | 10^1                  | 76.07       |\n| 2         | 1                  | 0                              | 0        | 0        | 0        | 10^2                  | 76.18       |\n| 3         | 1                  | 1                              | 1        | 0        | 0        | 2x10^2                | 76.30       |\n| 4         | 1                  | 14                             | 1        | 4        | 9        | 1.3x10^3              | 76.51       |\n| 5         | 1                  | 21                             | 1        | 4        | 16       | 2.2x10^3              | 76.88       |\n| 6         | 1                  | 30                             | 1        | 4        | 25       | 3.5x10^3              | 76.87       |\n| 7         | 1                  | 26                             | 1        | 9        | 16       | 3x10^3                | 76.88       |\n\nBased on the above experiments, we have four observations:\n1. As in Exp 1, If we do not conduct coarse-to-fine region removal (neither of the two steps will be applied), we essentially apply an occlusion box over the selected region. This is the same approach as the prior works and will cause a relatively low downstream QA accuracy of 76.07.\n2. Comparing Exp 1-3, we observe that adding the image inpainting process by SPLp and SPLf will both consistently improve the downstream performance.\n3. Based on Exp 3-5, we can see a noticeable performance improvement trend as we apply more refined region inpainting via increasing the value of M and N. \n4. In practice, we found that setting M and N to 4 and 16, respectively, generally achieves optimal performance while maintaining reasonable inference time consumption.\n\nWe will add these parts of the experiment results and expand our discussion on the experiment section, given an extra page upon acceptance. As mentioned, the triple-pass autoregression strategy is a flexible and general solution. Thus, M and N can be set according to the actual situation to decrease time consumption. We also admit that achieving optimal performance while maintaining minimal time consumption is still a challenging and not fully resolved problem for future work.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper addresses an important issue, i.e., dataset bias in VQA tasks, and provides a different perspective to mitigate these dataset biases.\n\n2. The proposed ADS method effectively synthesizes factual and counterfactual data to mitigate biases. Meanwhile, the ICT method enhances models' training by focusing on intra-sample differentiation.\n\n3. The paper is well-structured and provides extensive empirical results to demonstrate the effectiveness of ADS and ICT in consistently improving model performance across different benchmarks.", "reasons_to_reject": "1. The synthetic factual image generation will inevitably introduce noises, and controlling the quality of the synthetic images need extra human supervision.\n\n2. The overall inference process of this method seems to demand a higher time consumption. There is a lack of in-depth analysis regarding the comparison of time consumption with other baselines.", "questions_to_authors": "1. Can you provide the time consumption of your proposed method with other baselines?", "paper_topic_and_main_contributions": "This paper focuses on the analysis and mitigation of dataset biases in Visual Question Answering (VQA) tasks with long answer choices. The authors identify two types of biases, Unbalanced Matching bias and Distractor Similarity bias, and propose an Adversarial Data Synthesis (ADS) method and an Intra-sample Counterfactual Training (ICT) method to mitigate these biases. The proposed methods are evaluated on multiple benchmarks and demonstrate consistent improvements in model performance.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper focuses on the analysis and mitigation of dataset biases in Visual Question Answering (VQA) tasks with long answer choices. All reviewers appreciated the detailed analysis of bias in this task. But the reviewers also raised concerns on the inference process of the proposed method.
Decision: Accept-Findings