Debiased Visual Question Answering from Feature and Sample Perspectives
Authors: Zhiquan Wen
Guanghui Xu
Mingkui Tan
Qingyao Wu
Qi Wu
Venue: NeurIPS 2021 Poster (2021)
Keywords: Visual Question Answering
Debiased
Bias Detection

Abstract: Visual question answering (VQA) is designed to examine the visual-textual reasoning ability of an intelligent agent. However, recent observations show that many VQA models may only capture the biases between questions and answers in a dataset rather than showing real reasoning abilities. For example, given a question, some VQA models tend to output the answer that occurs frequently in the dataset and ignore the images. To reduce this tendency, existing methods focus on weakening the language bias. Meanwhile, only a few works also consider vision bias implicitly. However, these methods introduce additional annotations or show unsatisfactory performance. Moreover, not all biases are harmful to the models. Some “biases” learnt from datasets represent natural rules of the world and can help limit the range of answers. Thus, how to filter and remove the true negative biases in language and vision modalities remain a major challenge. In this paper, we propose a method named D-VQA to alleviate the above challenges from the feature and sample perspectives. Specifically, from the feature perspective, we build a question-to-answer and vision-to-answer branch to capture the language and vision biases, respectively. Next, we apply two unimodal bias detection modules to explicitly recognise and remove the negative biases. From the sample perspective, we construct two types of negative samples to assist the training of the models, without introducing additional annotations. Extensive experiments on the VQA-CP v2 and VQA v2 datasets demonstrate the effectiveness of our D-VQA method.

Debiased Visual Question Answering from Feature and Sample Perspectives
Zhiquan Wen 1.2 Guanghui Xu', Mingkui Tan
Qingyao Wu'", Qi Wu²
School of Software Engineering, South China University of Technology, China
*PengCheng Laboratory, China
Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education
Sccooo of Computer Science, University of Adelaide (sewenzhiquan, sexuguanghui @ mci...cct..dd..om (mingkuitan, qyw | @ edu.cn, qi.wu01 @adelaide.edu.au eduu.au
Abstract
Visual question answering (VQA) is designed to examine the visual-textual rea- soning ability of intelligent agent. However, recent observations show that many VQA models may only capture the biases between questions and answers in dataset rather than showing real reasoning abilities. For example, given question, some VQA models tend to output the answer that occurs frequently in the dataset and ignore the images. To reduce this tendency, existing methods focus weakening the language bias. Meanwhile, only a few works also consider vision bias implicitly. However, these methods introduce additional annotations or show unsatisfactory performance. Moreover, not all biases are harmful to the models. Some "biases" learnt from datasets represent natural rules of the world and can help limit the range of answers. Thus, how to filter and remove the true negative biases in language and vision modalities remain a major challenge. In this paper, we propose method named D-VQA to alleviate the above challenges from the feature and sample perspectives. Specifically, from the feature perspective, we build a question-to tnnestiontto-asserr to-answer and vision-to-answer branch to capture the language and vision biases, respectively. Next, we apply two unimodal bias detection modules explicitly recognise and remove the negative biases. From the sample perspective, we construct two types of negative samples to assist the training of the models, without introducing additional annotations. Extensive experiments on the VQA-CP v2 and VQA v2 datasets demonstrate the effectiveness of our D- D-VQA method.
1 Introduction
43 45 require an agent to perform visual-textual reasoning, and play important role in helping humans intelligent robots understand the physical world. As a typical case of the ssiion-ndd-anguual aims to answer textual question based on given image. In this sense, VQA models should master and apply the strong reasoning ability to address the sophistical questions. However, recent studies 3 23 36 demonstrate that many VQA models answer the questions without reasoning. Instead, they excessively rely on superficial correlations (i.e., bias) between the question and answers without considering the image. For example, some VQA models tend answer "2" for the questions "How many since most corresponding answers in the dataset are "2". In this case, the VQA models memorise the bias but ignore the image, resulting in a poor generalisation ability.
lasin-a tasks 5
task, visual question answering (VQA) [8] 35 42
"Corresponding author
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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Vision Blas
What lhe food in the cup?
What is sie pouring?
Q: What kind balls ace shown?
Q: What coler bid??
What the sworts glass callod?
Q Wlich item in the middle?
Q: W hat kind fruits can be scen?
Q What color on the bind' ches??
Ans: candy
Ans ketchup
Ans oranges
Ans: yellow
Figure 1: Examples of vision biases. From these examples, most of the answers to the questions usually correspond to the most salient objects/atriibutss in the image.
As is widely known, most machine learning datasets inevitably have biases. Thus, how to train robust model from the biased dataset remains major challenge. To alleviate the challenge in VQA, most existing methods mainly focus on weakening the language bias 19] 33). Recently, Niu et al. 29 applied a cause-effect to alleviate language bias. However, the performance is not sufficiently encouraging, and it introduces additional parameters in the inference phase. Note that the biases also exist in the vision modality. As shown in Figure many answers to the questions are usually the most salient objects/atributes in the images. In this sense, the VQA models may capture this vision bias to output an answer regarding the salient object in the image, without considering the questions. Thus, alleviating the negative effect of vision bias is also crucial. Recently, few works [TO, [7] generated counterfactual training samples by masking or transforming critical objects and words to train the models to alleviate language and vision biases. However, they require expensive annotations.
More critically, some "biases" captured from the dataset may represent the natural rule in real world, i.e. commonsense knowledge. They are not harmful to the models. Instead, models benefit from them. For example, "dog" is kind of "animal", and the colour of "oranges" normally "orange". Thus, how to filter and remove the true negative biases in language and vision modalities is challenging.
To alleviate the above issues, we propose a method named D- D-VQA overcome the negative biases in language and vision modalities from feature and sample perspectives. Specifically, (1) from the feature perspective, we first build question-to-answer and vision-to-answer branch capture language and vision biases, respectively. Since not all biases are detrimental to the model, two unimodal bias detection modules are devised filter and remove negative biases only. To maintain the inference efficiency, following existing methods 9 14 33 we do not expect to introduce additional parameters in the inference phase. To realise this expectation, we adopt a contrastive loss to approach the multimodal features to the debiased feature as close as possible. In this way. in the inference phase, we simply remove all of the additional branches, and adopt the multimodal features that have similar representation as the debiased features to obtain accurate predictions. (2) From the sample perspective, the VQA model should answer the questions making full use of the information of questions and images, which can be achieved by improving the sensitivity of the model in both the images and questions. To this end, for each sample in the dataset, we construct two types of negative samples to assist the training and improve the sensitivity of the model.
Our contributions be summarised follows: (1) In VQA, alleviate the biases in both language and vision modalities, from feature and sample perspectives. (2) Since not all biases are harmful, we explicitly devise the bias detection modules filter and remove the negative biases. (3) Extensive experiments on the VQA-CP v2 and VQA v2 datasets demonstrate the effectiveness of our D-VQA.
Related Work
Biases in Visual Question Answering (VQA), In the vision-and lan--auu field, biases are ubiqui-
tous. For example, in referring expression comprehension (REF) task [[5] 45 48) Cirik er al. [13 argue that the benchmark datasets such as RefCOCO 24 28 contain biases which the referent objects are usually the most salient in the image. Thus, the models can capture the biases and obtain high accuracy without the queries. Inevitably, the issues of bias also exist in VQA. As demonstrated
by 12 3 21 36), existing VQA benchmark datasets such as the VQA v2 [18 have biases, namely,

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shortcut exists between the questions and answers. For example, one simply answering "tennis" for the question "What sports ... will obtain high accuracy, because the model is trained and tested on the COCO image dataset that has many "tennis" related images. Moreover, most methods consider language bias only, while ignoring the vision bias. As discussed in [29], the questions usually involve the objects seen in the image. In addition, as shown in Figure [ the most salient objects/attributes are usually chosen to be the answers. In this sense, the VQA models may also capture this vision bias.
Thus, in this paper, we consider alleviating the biases in language and vision modalities.
Overcoming biases in VQA. Recently, many methods 9 TO 16
T7
22
26
27
29
3
30
39
40, 44 49 have been proposed to overcome the biases in VQA. Formally, these methods can be categorised into two classes, namely, with and without data augmentations. Specifically, non-
augmentation-bed based methods (9] [6 22 26 29 [3 36 44 seek to reduce the language biases
explicitly or improve attention on the image, while augmentation-bast methods
[ 10 17
40
49
seek to balance the biased dataset for unbiased training.
For the non-augmentation-baseeee methods, Ramakrishnan al. [33] adopt adversarial learning between the base VQA model and the question-only model to prevent the VQA model from capturing language bias as much as possible. Inspired by [33, Cadene al. p dynamically adjust the weight of the samples based on how biased the samples are. Moreover, some methods introduce human based visual [6 and text 44 explanations to strengthen the visual grounding. However, these methods require human annotations that are hard to obtain. Apart from the above, Niu et al. [29] introduce the cause-effect to look at language bias and propose a counterfactual inference framework to reduce the biases, which unfortunately introduces additional parameters in inference.
For the augmentation- based methods, promote the VQA model to focus the critical objects and words, Chen et al. [10] propose a CSS method to produce massive counterfactual samples by masking the critical objects and words, and assign corresponding ground-truth answers. To make full use of the samples, Liang et al. [27) model the relationships among original. factual and counterfactual samples promote learning high-level features. Moreover, the Mutant [17 generates the samples by semantic transformations of the original images or questions. Without introducing additional annotations, some methods 38 49] build negative samples from the samples available to balance the dataset. In this paper. we overcome the bias from sample and feature perspectives. Specifically, from the sample perspective, inspired by [38 49 samples to assist the training of the model. From the feature perspective, we build question-to-anse tonsswer and vision-to sooo-to--nsswr branches to capture the language and vision biases, respectively. Next, we devise two unimodal bias detection modules to recognise and remove the negative biases.
we construct two types of negative
Contrastive Learning 41 aims learn the high-level representation by maximising the mutual information between the input samples and positive samples, which has been applied in many fields 27 32 47). Specifically, some contrastive learning methods [I] 19] adopt contrastive learning to train the models by decreasing the distance between the feature representations of different augmented views of the same images, while increasing the distance between different images. Liang et al. (27) adopt contrastive learning to approach the feature representations of the original samples to the factual samples, while keeping away from that between the original and counterfactual samples. In this paper, we adopt contrastive learning to push the multimodal feature to approach the debiased features, and meanwhile to far away from the negative biased features.
3 Proposed Method
As demonstrated by 3 23 36 many VQA models address the questions by exploiting the superficial correlation (i.e., bias) between the questions and answers without considering the images, resulting in poor generalisation ability. (i.e,, performing worse
out-of-distribution (OOD) dataset, e.g., VQA-CP (3). Moreover, bias also exists between the images and answers (as shown in Figure I which is ignored by most previous methods [9 33 38 49). However, some "biases" learnt from the dataset indicate the natural rule of the world, such as commonsense knowledge. Thus, how to filter and remove the negative biases in language and vision modalities is challenging.
In this paper, we alleviate the negative effect of both language and vision biases from feature and sample perspectives. An overview of our D-VQA method is shown in Figure Specifically, from the feature perspective (see Section 3.2). we seek to obtain multimodal features that reduce the negative biases as much as possible. To this end, construct a question-te to---ttt----rrrrrrr and vision-to-answer
3

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Feature Perspective
Sample Perspective
Ld
Lu
VQA Modal
D
Simmon
BDM
Debiased Model
MIP
BDM
Incks
VQA Model
QA Modal
Modil
VQA
TO
Model
bacquro
9:
Figure Overview of our D-VQA method. Our method alleviates the biases from feature and sample perspectives. From the feature perspective, we introduce question-to-answee answer, vision-to-answer and debiased branches to help reduce the negative biases. BDM is the bias detection module. Note that Ca does not optimise the VQA model. From the sample perspective, we construct two types of negative samples to assist the model's training. (vi, qi) denotes positive sample, while (T,, qi) and (o;, )i) negative samples. GT is the ground-truth answer of (t, 4.). h and g are the feature extractors.
branch to capture the unimodal biases. Moreover, we devise two unimodal bias detection modules to explicitly recognise and remove the true negative biases. That is, we obtain debiased features by simply removing negative biased features from multimodal features. However, using the debiased feature in inference will introduce additional parameters. To address this issue, inspired by contrastive learning [II [9]] adopt contrastive loss to approach the multimodal features the debiased features while keeping away from the negative biased features. In this way, in the inference phase, we discard all of the additional branches and adopt the multimodal features to obtain accurate predictions.
From the sample perspective (see Section 3.3). the VQA models are supposed to simultaneously focus on language and vision modalities when answering the questions. Specifically, given a question- image pair (positive sample), we construct two types negative samples by randomly sampling an image question in mini-batch data. The predicted possibility for the ground-truth answer should be minimised if negative samples are used. In this way, VQA models are able to capture the two modalities information to generate the answer instead of focusing one modality only. Based on the above discussion, our D- VQA method is able to alleviate the negative effect the biases in language and vision modalities and improve the model performance on the OOD dataset (e.g.. VQA-CP v2 (3).
3.1 Preliminaries
Visual question answering (VQA) seeks answer the textual questions based on the given images. Following traditional VQA methods [4] 7 g 25 46), we regard the VQA task as a multiclass classification problem. Specifically, given a dataset D {(r,. qa a;) M with N triplets, where the i-th image U; E V (image set), question q: Q (question set) and answers E A (answer set), one seeks to train a model to learn mapping function FF x - R to generate an accurate answer distribution over the answer A. To be specific, F usually contains three parts: (1) Feature extraction. It requires building two feature extractors h and g to extract the features of the images and questions, respectively. For one triplet in D, the image extractor n. vectors with a dimension de, and the question extractor
Us I m. xd outputs 9 - Ra, xd, outputs ny vectors with a dimension dg. (2) Multimodal feature fusion. Since the image and question features come from different modalities, it requires designing a feature fusion function I to fuse both of the image and question features, where Xdv
devises classifier dR Al for producing the answer prediction. Note that most of the classic VQA follow this paradigm, which can be formulated as:
xdv x Xd, - R.d., (3) Answer prediction.
models
g 25 46
F(Ale,, 4:) c(f). where f = f((())),))))))))))))(()) g(g:).
(1)
Formally, the parameters of the VQA model can be optimised by binary cross-entropy loss:
Luga
y:logoo(F(Alee) 9))) (1 y4)log(1 (F(())ss))))
(2)

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where (-) is the sigmoid activation function, and y; is the target score of each answer for the i-th sample, denoted y, min( # 1), where #votes is the number of each answer that humans annotated for the question qı However, Agrawal et al. [3] show that many VQA models tend to capture unimodal biases (e.g., the shorteut between the questions and answers) from the dataset. To alleviate this issue, we consider reducing the negative biases from feature and sample perspectives.
3.2 Reducing biases from the feature perspective
In this part, we seek to reduce the negative effect of the biases from the feature perspective, namely, decrease the negative biases from the multimodal features f. Before that, we first need to capture the biases in both language and vision modalities.
Unimodal model capture the biases. To capture the biases in a VQA dataset, one intuitive way is to train a unimodal model as a branch that takes only one modal feature as input. Specifically, we construct guestion-to-answe
and vision-to-answer branch to capture language and vision biases, respectively (we conduct the ablation studies on each branch in Table 2). Since the structures of the two branches are similar, we describe the prestion-to-answee branch only and obtain the vision-to-anse to-answer branch by simply replacing the subscript of with v.
Specifically, our question-to-anse to-awwe model Fq q + RaAAl contains a multi-layer perceptron (MLP) my : R"y x + Rdh and a classifier cy IRdh - R RAA Based on the above, the question-to-anwwee branch can be formulated as:
(Al:)) = Co (fg), where = -=[()))))))
By training the question-too to answer model using binary cross-entropy loss Ly VQA dataset, the model is able to capture the biases between the questions and answers in the dataset. Thus, the feature f, contains language biases. We can obtain the vision biased feature f, in similar way.
Recognise and obtain the negative biases. We argue that not all biases in the dataset are harmful to the VQA models. Some "biases" captured from the dataset contain commonsense knowledge that may be helpful for the VQA models. Thus, we devise a simple but effective bias detection module (we evaluate the bias detection modules in Table to detect the negative biases that need to be removed in the feature fg. Formally, our bias detection module is constructed by an MLP my : IRd + R and sigmoid activation function (.), which can be formulated as:
by (mm
(4)
Based on the weight by. the negative language biased features can be represented as fii =, f.. The negative vision biased feature fy.. is obtained similarly. To obtain the global negative biased feature feias. adopt attention mechanism to fuse two negative unimodal biased features:
softmax( m. (1 (iiss)) faius a fhinsl:
where m. 2x R2 is MLP, and [::-] denotes for stacking.
Obtain debiased features. Intuitively, the debiased features can be obtained by removing negative biased features fasus from the multimodal features f, which can be represented as fg = f faias- With the feature f, as input, we also introduce another debiased branch that contains an MLP my : dd dd and an independent classifier ca : Rd R|AI, which can be formulated as:
Fdebias (Alu;, g:) = cff).) where f. mf(fs).
The parameters of the debiased branch are optimised by the binary cross-entropy loss Ca. However, if we adopt the debiased branch as the target model, it will introduce additional parameters in the inference phase. To address this issue, an intuitive idea to make the multimodal features f and debiased features f. generated from the debiased branch as similar as possible. To accomplish this task, inspired by the contrastive learning 11 19, we consider forcing the multimodal features to near the debiased features f, and away from the negative biased features faias-
To achieve the abovementioned aim, our contrastive loss can be formulated as follows:
N
(f..fe)
f.. f
Le =
log( (f(..fc) ) (((Pifoiaa) where s(f1. fz) =
N
[
[
5

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s(:--) denotes a scoring function, of which the higher the value is, the higher the similarity between two features. practice, we adopt the cosine similarity as the scoring function. By minimising the loss Le. the multimodal features f are able to approach the debiased features f. while avoiding the negative biased features hjas- In this way, the multimodal features f have similar representation with the debiased features for to some extent. Note that all of the additional branches participate in the training phase only. In the inference phase, we remove all of the additional branches and adopt the base VQA model to obtain accurate predictions.
3.3 Reducing biases from the sample perspective
Since the biases usually embody in capturing the shortcut between one modal information and the answer, it is necessary to improve the attention of the VQA models on language and vision modalities when answering the questions. That is, increasing the sensitivity of the VQA models on the information of both modalities. To achieve this, we consider constructing two types of negative samples for each sample the dataset to assist the training of the model. Specifically, inspired by [38, 44), for each positive sample (ii qi, Qj), we construct the negative samples by randomly sampling question d and image U; in mini- data as (0;, q: az) and (v, 9s. az). Note that ü; and Zs are sampled separately. The ablation studies regarding the negative samples are in Table
Intuitively, in VQA, one can answer the question only if the question and image are corresponding. In contrast, when taking the negative samples as input, the VQA model cannot answer the question correctly, which is able to be achieved by minimising the possibility of predicting the ground truth answer of the positive sample. Since the training procedure of these two types of negative samples is the same, we only describe the training details of (Ü:, q: a.). Formally, the loss can be defined as:
Camm softmax ( (F(A)ss [k)]
where k is the index of the answer a; in the answer set A. Minimising 4%am encourages the prediction of the VQA model keep away from the ground truth answer of the positive sample, because of the lack of supporting visual information. In this way, this type of negative samples is able to promote the VQA model to focus on the images when answering the question, which improves the sensitivity of the model on the images, Similarly, another type of negative samples (2), qt, a,) is able to improve the sensitivity of the model on the questions. The loss of the sample perspective can be defined as fesam = XgL% + Xp.Caam (the ablation studies on X and X are in Table 5). Note that we do not introduce additional annotations, instead, we make full use of the training samples available.
3.4 Learning D-VQA with overall loss
In total, our method contains three types of losses, namely, traditional VQA loss (i.e., binary cross- entropy loss), contrastive loss and sample perspective loss, which can be formulated as:
C Logo + L, + C, + La + C. + Lsam:
(9)
where L(vga.q.udd) are the binary cross-entropy loss based on the same ground-truth answer, corre- sponding to the base, question-to-ana answer, vision-to-answer, debiased) branches, respectively.
4 Experiments
4.1 Datasets and compared methods
Datasets. To demonstrate the effectiveness of our D-VQA method, we evaluate it on the out-of- distribution benchmark dataset VQA-CP (Visual Question Answering under Changing Priors) v2 B] and IID dataset VQA v2 [18] validation set based on the standard evaluation metric [6]. Since the VQA v2 [8] dataset has strong biases, to evaluate the generalisation ability of the VQA model, Agrawal et al.
3 created the diitt---iittitttion (OOD) dataset named VQA-CP v2. Specifically, they constructed VQA-CE CP v2 by re-organising the training and validation sets of the VQA v2 dataset, where the answer distributions in training and test sets of VOA-CP v2 are different. The training set of VQA-CP v2 contains approximately 121k images and 245k questions, while the test set contains approximately 98k images and 220k questions.
Compared methods. We regard both augmentation- based and aom--agmentation-aaseeeeee methods as compared methods. Specifically, (1) for the non-augmentation-be based methods, we compare our

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D-VQA method with AdvReg [33. RUBI 9I, LMH 14]. DLR 122). VGQE 26). HINT [3).
SCR 44 RMFE [6 and CF-
VQA
29 Moreover, (2) augmentation basee methods such as
RandIng 38) SSI-VVQA 49, CSS+CI. [27. and Mutant [7 are considered compared methods. Note that some augmentation based methods such as CSS and Mutant construct the counterfactual samples using additional annotations, while our D-VQA does not introduce additional annotations.
CVL I. Unshuffling 40, CSS 10
4.2 Implementation details
Our method is model-agnostic and can be applied to different backbones of VQA models. To better demonstrate the effectiveness of our D-VQA, we conduct experiments based different backbones,
including UpDn 44, SAN 46] and LXMERT [37]. Following previous works ( 33 49), we adopt extract the top-36 object features for each image and the dimension of each object feature is 2048. Moreover, all the questions
Faster-RCNN B4] that are pretrained by 4 to extract the object features. Specifically,
processed into the same length (i.e. 14), and then each word in the question is encoded by Glo [3 embedding with a dimension of 300. A single layer GRU [2 is used to obtain the feature of the question with a dimension of 1280.
Inspired by SSL-VVQ [4)], we add one Batch Normalisation 20 layer before the classifier of UpDn 44. and then train our D-VQA for 25 epochs. Specifically, we train all the branches with the binary cross-entropy loss and contrastive loss over the training process, and the sample perspective loss is introduced at the 13-th epoch. We adopt Adam optimiser with the initial learning rate of 1e-3, and the learning rate decreases by half every 5 epochs after 10 epochs. The batch size is set to 256.
For the backbone of LXMERT (37,, we load the pre-trained LXMERT model from the official GitHub repository 2 and then perform the downstream VQA-CP task with our D- D-VQA method. Specifically, we train LXMERTT + D-VQA for 10 epochs, and the sample perspective loss is introduced at the 7-th epoch. The batch size is 32, and the learning rate is 1e-5. We implement our entire method based on PyTorch 30), and the model is trained with one Titan Xp GPU. The source code and the pre-traue trained models are available at httts://gihub..om/Z/iquuaa Wen/D.
4.3 Evaluation on the VQA-CP v2 and VQA v2 datasets
Quantitative results. We compare our D-VQA method with the state-of-the-an methods on the VQA-CP v2 test set and VQA v2 validation set, and report the experimental results in Table [ From these results, we have the following observations. On the VQA-CP v2 dataset, (1) augmentation- based methods surpass non-augmmentation-bassed methods by a large margin (57.59% vs. 54.55% and 61.72% vs. 54.55%). These results demonstrate that reducing bias by focusing on data is more effective than other non non-augmentatiin- based methods. (2) Our D-VQA outperforms all the compared methods, either augmentation- based or non-augna aan--mmmenttiionn based. Specifically, our D-VQA surpasses CF-VQA 29, SSL-VQA [49] and Mutant [17 by approximately 8%, 4%, and 0.2%, respectively. Note that some augmentation- based methods such as Mutant 117. CSS 10 and CSS+CL (27] generate counterfactual training samples by masking transforming critical words and objects from the samples in the dataset, which introduces additional annotations. In our method, without using additional annotations, we construct the negative samples based on the available samples, which fully makes use of the samples in the dataset. Nevertheless, our D-VQA still performs better than Mutant, which greatly demonstrates the effectiveness of our D-VQA. (3) For the question type of "Num". our D-VQA outperforms the compared methods by large margin, and achieves comparable performance on the remaining question types. These results further verify the effectiveness of our D-VQA.
On the validation set of the VQA v2 dataset, most methods perform worse than UpDn 4 while our D-VQA surpasses all compared methods. These results show that our D-VQA performs better in both IID (VQA v2) and OOD (VQA-CP v2) datasets, indicating the superiority of our D-VQA
Qualitative results. To further demonstrate the effectiveness of our method on the OOD dataset, we provide qualitative results on the test set of the VQA-CP v2 dataset Figure From these results, UpDn and SSL-VQA cannot find the target objects accurately, and thus output the wrong answers or have low scores in the ground- truth answer. Our D-VQA method locates the target objects with high attention weight, and then makes an accurate prediction.
7

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Table 1: Comparison with the taae--ffthe-art methods on the VQA-CP v2 test set and VQA v2 validation in terms Accuracy (%). The backhone of all models UpDn 4 Overall best scores are bold, and the second best of overall scores of ours are underlined. I- IV denote -on-augmentation based methods using human annotations, non-augmentation- based methods without using human annotations, augmentation-baas bbaed methods adopting additional annotations, and sugmentation-base methods without introducing additional annotations, respectively.
VQA-CP v2 test
VQA val (%)
Case
Model
All
Yes/No
Num
Other
All
Yes/No
Num
Other
SAN 46
24.96
38.35
11.14
21.74
52.41
70.06
39.28
47.84
GVQA 3
31.30
57.99
13.68
22.14
48.24
72.03
31.17
34.65
UpDn
39.74
42.27
11.93
46.05
63.48
81.18
42.14
55.66
AtAllgg 36
39.37
43.02
11.89
45,00
63.24
80,99
42.55
55,22
HINT 36
46.73
67.27
10.61
45.88
63.38
81.18
42.99
55.56
SCR
48.47
70.41
10.42
47.29
62.30
77.40
40.90
56.50
AdvReg
41.17
65.49
15.48
35.48
62.75
79.84
42.35
55.16
RUBi
44.23
67.05
17.48
39.61
DLR 22
48.87
70.99
18.72
45.57
57.96
76.82
39.33
48.54
II
VGQE 26
48.75
64.04
LMH 14
52.01
72.58
31.12
46.97
56.35
65.06
37.63
54.69
RMFE [6
54.55
74.03
49.16
45.82
CF-VQA 22
53.55
91.15
13.03
44.97
63.54
82.51
43.96
54.30
CSS
58.95
84.37
49.42
48.21
59.91
73.25
39.77
55.11
CSS+CL 27
59.18
86,99
49.89
47.16
57.29
67.27
38.40
54.71
Mutant 17
61.72
88.90
49.68
50.78
62.56
82.07
42.52
53.28
CVL 1
42.12
45.72
12.45
48.34
Unshuffling 40
42.39
47.72
14.43
47.24
61.08
78.32
42.16
52.71
RandIng 38
55.37
83.89
41.60
44.20
57.24
76.53
33.87
48.57
IV
VSL-VQA
49
57.59
86.53
29.87
50.03
63.73
D-VQA
61.91
88.93
52.32
50.39
64.96
82.18
44.05
57.54
UpDn
SSL-VQA
Ours
Q:
person elephant wesuring?
0/47
11:22
red
0.22
white
0.29
blue 0:13
pink
0.18
0.00
black 0U11
purple
0X00
nel
0.2
white 0.1
0.03
Q: Which foot behind the other?
left
left
0.005
11:55
0.30
0.1
both
neither; 0.01
both 0.1
a.0
hand
right
0.001
neither' 0.01
hand! 0.01
right
Figure 3: Qualitative comparison among UpDn 4. SSL-VQA 49 and our D-VQA on the VQA-CP v2 test set. For each example, put the bounding box with the top-2 attention weights in the image and show the answers with the top-5 predictions. The bold, red answer is the ground-truth answer.
4.4 Ablation studies of D-VQA
Effeet of each component on the model performance. demonstrate the effectiveness of each component in our D-VQA, we conduct ablation studies on VQA-CP v2 regarding the components of D- D-VQA, and show the experimental results Table From these results, we have the following to-seee and vision-to-answer branches promote the model per- formance (refer to Lines of the Table 2 which demonstrates that these two branches are able to capture the corresponding negative biases to be removed. (2) Both types of negative samples
observations: (1) The

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Table 2: Effect of each component of our D-VQA on the model performance. We show the results on the VQA-CP dataset terms of Accuracy (%). We use UpDn' as the backbone model, where i denotes our re-implementation of the baseline.
(üs, q.) (viy @:) question-to-answer vision-to-answer VQA-CP v2 test (%)
41.53
2
V
57.59
3
41.60
4
VV
60.24
5
61.18
6
V
61.91
Table 3: Effect of the bias detection mod- ules on the VQA-CP v2 dataset. "base" de- notes D-VQA without the bias detection modules, while "det( denotes the vi- sion/language bias detection module.
Table 4: Effect of different backbones on the model performance. We report the experimental results on the VQA-CP v2 dataset in terms of Accuracy (%). denotes the re-implementation of the baseline.
Model
Yes/No Number Other Overall GapA
Model
VQA-CP v2 test (%)
SANT
38.44
12.91
46.65
39.11
+15.28
SAN + D-VQA
84.12
26.12
46.56
54.39
base
59.93
UpDn' 4
43.45
13.64
48.18
41.53
+20.38
2
base dete
59.98
UpDn D-VQA
88.93
52.32
50.39
61.91
base + det,
60.28
LXMERT 37
42.84
18.91
55.51
46.23
+23.52
4
D-VQA
61.91
LXMERT + D-VQA
80.43
58.57
67.23
69.75
are able to improve the model performance, and only (0,, 1s a,) improves substantially, while only (v,, qo a,) is marginally improved. Additionally. combining these two types of negative samples further promotes the model performance (refer to Lines 1-4 of the Table 2). From these results, we surmise that language bias is dominant compared with vision bias, while vision bias cannot be ignored. In total, these results demonstrate the effectiveness of each component in our D-VQA method on the model performance.
Evaluation of the bias detection modules. The bias detection modules are devised to determine how many negative biases should be removed. To evaluate the effect of the bias detection mod- ules (language and vision bias detection modules), we conduct experiments on the VQA-CP v2 dataset about removing the bias detection modules, and the experimental results are reported in Table 3 We have the following observations from these results: (1) both of the language and vision bias detection modules promote the model performance, while the language bias detection module has more positive effect than the vision bias detection module (refer to Lines 1-3 of Table 3 These results demonstrate the necessity to recognise the negative bias, and then remove it. (2) Combining language and vision bias detection modules is able further promote the model performance by large margin (refer to Lines 2-4 of Table 3). which demonstrates the necessity and effectiveness of recognising and removing the negative biases in both language and vision modalities.
Effeet of different backbones. To demonstrate the effectiveness of our D- D-A on different back- bones, we conduct experiments the VQA-CP v2 dataset using different backbones (iee, SAN [46], UpDn 4. and LXMERT (37]) and report the experimental results in Table 4 From these results, we find that D-VQA is able to improve the model performance substantially regardless of the backbone, which demonstrates that our method is model-agnostic, embodying the superiority of our D-VQA.
Evaluation of different combinations between X and >g. From the experimental results in Lines 2-3 of Table 2. (0g. 9:) has more positive effect than (b), In this sense, Ag should have a higher weight than A,. To further make trade-off between L and L we conduct experiments on VQA-CP v2 regarding different combinations X, and X,,. and the experimental results are reported in 5 From these results, the highest performance is obtained in our D-VQA when A, vs. X, is 1.0 0.7, and the performance declines for any X, higher or lower than 0.7, which demonstrates a suitable ratio between 7 and X will obtain better performance in method.

---

Table 5: Effect of different combinations between by and Ay- We report the experi- mental results in terms of Accuracy (%)
Table 6: Effect of different scales of the training data of VQA-CP on the model performance. We report the results in terms of Accuracy (%)
Model 2g rs. Op VQA-CP v2 test (%))
Proportion Training Set
Model
1:0.1
60.74
20%
40%
60%
80%
100%
1.3.
61.74
UpDn 4
36.22
38.90
39.40
40.61
41.53
D-VQA
1:
61.40
SSL-VQA
49
52.71
54.42
56.83
57.31
57.59
1.7.7
61.91
1: 1.0
61.63
D-VQA
52.94
56.74
58.31
59.05
61.91
Performance on different scales of the dataset. To further demonstrate the superiority of our method, we conduct experiments regarding different scales of the training data on VQA-CP v2. Specifically, the model trained based on the randomly sampled data (i.e., from 20% to 80% of the original training data) coming from the original training data, and then is evaluated on the standard test set. The experimental results are reported in Table From these results, we find that (1) our D-VQA achieves the best results compared with the state-of-the-a art method SSL-VQA [9] and the baseline method UpDn [4] regardless of the scales of the training data. (2) The performance gap between our D-VQA and compared methods gradually increases with the number of training samples. These results demonstrate the effectiveness of our method on different scales the training data.
5 Conclusion
In this paper, we have proposed a novel method named D- D-VQA to overcome the negative biases in both language and vision modalities. In our D-VQA, we alleviate the negative effect of the biases from the feature and sample perspectives. Specifically, from the feature perspective, we introduce a question-to--nt ceestionto--answer and a vision-to-answer branch to capture the corresponding unimodal biases. Moreover, not all biases are harmful to the VQA models. Some "biases" in the dataset indicate the natural rule of the world, egg., commonsense knowledge. Thus, we explicitly devise the bias detection modules to detect and remove the negative biases. From the sample perspective, we construct two types of negative samples to assist the training and improve the sensitivity of the model on both language and vision modalities. Extensive experiments D- D-VQA achieves new state-of-the-art result, which demonstrates its effectiveness. Note that our method is model-agnostic (refer to Table 4) and can be embedded into other backbones to overcome the negative biases. The current work focuses on the VQA task only, we will extend our approach to other multimodal tasks in future works, e.g., referring expression comprehension. Moreover, although our D-VQA achieves state-of-the-ar performance on most types of questions (see Table Oo this paper), there is much room for improvement questions such "Num" and "Other".
the VQA-CP v2 dataset show that our
Acknowledgement
would like to thank all the anonymous reviewers for their constructive comments and sug- gestions. This work was partially supported by Ministry of Science and Technology Foundation Project 2020AAA0106900. National Natural Science Foundation China (NSFC) 61876208, Key- Area Research and Development Program of Guangdong Province 201 20110000000 Key Realm R&D Program of Guangzhou 202007030007,, Program for Guangdong Introducing Innovative and Enterpreneurial Teams 20177T07X183.
Broader Impact
This paper introduces novel D-VQA method to alleviate the biases in Visual Question Answer- ing (VQA). Overcoming the bias can promote the VQA model to use real reasoning ability to address sophisticated questions. Therefore, this research can promote the development of the robot e,g., dialogue robot, and facilitate people's daily lives. The failure of the debiased technique may result in the collapse of the VQA system in environments that have never been seen. Moreover, we evaluate method benchmark distribbbtiiino dataset (OOD) and demonstrate better debiased ability.
10

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References
[1] E. Abbasnejad, D. Teney, A. Parvanch, J. Shi, and A. van den Hengel. Counterfactual vision and language lcarning In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10041-10051, 2020.
[2] A. Agrawal, D. Batra, and D. Parikh. Analyzing the behavior of visual question answering models. In Conference on Empirical Methods Natural Language Processing (EMNLP), pages 1955-1960, 2016.
[3] A. Agrawal, D. Batra, D. Parikh, and A. Kembhavi. Don't tt just assume; look and answer: Overcoming priors for visual question answering. In JEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 497 1971-,980, 2018.
[4] P. Anderson, X. He, C. Buchler, D. Teney, M. Johnson, S. Gould, and L. Zhang, Bottom-up and top-down attention for image captioning and visual question answering. In IEEE Conference Computer Vision and Pattern Recognition (CVPR), pages 6077 6086, 2018.
[5] P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. Sünderhauf, I. D. Reid, S. Gould, and A. van den Hengel. Vision-and -ad---annd---adeeeee navigation: Interpreting grounddd navigation instructions in real environments. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3674-3683, 2018
[6] S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. L. Zitnick, and D. Parikh. VQA: visual question answering. In IEEE International Conference on Computer Vision (ICCV), pages 2425-2433, 2015.
[7] H. Ben-younes, R. Cadène, N. Thome, and M. Cord. BLOCK: bilinear superdiagonal fusion for visual question answering and visual relationship detection. In AAAI Conference on Artificial Intelligence (AAAI). pages 8102-8109, 2019.
[8] R. Cadène, H. Ben-younes. M. Cord, and N. Thome. MUREL: multimodal relational reasoning for visual question answering. In JEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1999-1998, 2019.
[9] R. Cadène, C. Dancette, H. Ben-younes, M. Cord, and D. Parikh. Rubi: Reducing unimodal biases for visual question answering. In Conference on Neural Information Processing Systems (NeurIPS), pages 839-850, 2019.
[10] L. Chen, X. Yan, J. Xiao, H. Zhang, S. Pu, and Y. Zhuang. Counterfactual samples synthesizing for robust visual question answering. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10797-10806 2020.
[11] T. Chen, S. Kornblith, M. Norouzi, and G. E. Hinton. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning (ICML), pages 1597-1607, 2020.
[12] K. Cho, B. van Merrienboer, Ç. Gülçchree, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. learning phrase representations using RNN encoder-decnder for statistical machine translation. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724-1734. 2014.
[13] V. Cirik, L. Morency, and T. Berg- Kirkpaarra..... Visual referring expression recognition: What do systems actually leam? In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACI-HI.T), pages 781-787, 2018
[14] C. Clark, M. Yatskar, and L. Zettlemoyer. Don't 't take the easy way out: Ensemble based methods for avoiding known dataset biases. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4067-4080, 2019.
[15] C. Deng, Q. Wu, Q. Wa, F. Hu, F. Lyu, and M. Tan. Visual grounding via accumulated attention. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 7746-7755, 2018
[16] I. Gat, Schwartz, A. G. Schwing, and T. Hazan. Removing bias in multi-modal classifiers: Regularization by maximizing functional entropics. In Conference on Neural Information Processing Systems (NeurIPS), 2020.
[17] T. Gokhale, P. Banerjee, C. Baral, and Y. Yang. MUTANT: A training paradigm for out-of-distribution generalization in visual question answering. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 878-892, 2020.
[18] Y. Goyal, T. Khot, D. Summers- Sanmerr--Stay, D. Batra, and D. Parikh. Making the V in VQA matter: Elevating the role of image understanding in visual question answering. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6325-6334 2017.

---

[19] K. He. H. Fan, Y. Wu. Xie, and R. B. Girshick. Momentum contrast for unsupervised visual representation learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 9726-9735. 2020.
[20] S. loffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference Machine Learning (ICMI.), pages 448-456, 2015.
[21] A. Jabri, A. Joulin, and L. van der Maaten. Revisiting visual question answering basclines. In European Conference on Computer Vision (ECCV). pages 727-739, 2016.
[22] C. Jing, Y. Wu, X. Zhang, Y. Jia, and Q. Wu. Overcoming language priors in VQA via decomposed linguistic representations. In AAAI Conference Artificial Intelligence (AAAI), pages 11181-11188. 2020.
[23] K. Kafle and C. Kanan. An analysis of visual question answering algorithms. In IEEE International Conference on Computer Vision (ICCV), pages 1983-1991, 2017.
[24] S. Kazemzadeb, V. Ordonez, M. Matten, and T. L. Berg. Referitgame: Referring to objects in photographs of natural scenes. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 787-798, 2014.
[25] J. Kim, Jun, and Zhang. Bilincar attention networks. In Conference on Neural Information Processing Systems (NeurIPS), pages 1571-1581, 2018.
[26] G. KV and A. Mittal. Reducing language biases in visual question answering with visually-grounded question encoder. European Conference on Computer Vision (ECCV), pages 18-34, 2020.
[27] Z. Liang, W. Jiang, H. Hu, and Zhu. Learning contrast the counterfactual samples for robust visual question answering. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages -2952 2020.
[28] J. Mao, J. Huang, Toshev, O. Camburu, L. Yuille, and K. Murphy, Generation and comprehension of unambiguous object descriptions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11 11-20, 2016.
[29] Y. Niu, K. Tang, H. Zhang, Z. Lu, X. Hua, and J. Wen. Counterfactual VQA: A cause-effect look at language bias. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 12700-127 10, June 2021.
[30] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala. Pytorch: An imperative style, high performance deep learning library. In Conference on Neural Information Processing Systems (NeurIPS), pages 8024 8035, 2019.
[31] J. Pennington, R. Socher, and C. Manning. Glove: Global vectors for word representation. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532- 1543, 2014.
[32] Z. Qiu, Y. Zhang, H. Lin, S. Niu, Y. Liu, Q Du, and M. Tan. Source-free domain adaptation via avatar prototype generation and adaptation. In International Joint Conferences on Artificial Intelligence (IJCAI). pages 2921-2927, 2021.
[33] S. Ramakrishnan, A. Agrawal, and S. Lee. Overcoming language priors in visual question answering with adversarial regularization. In Conference on Neural Information Processing Systems (NeurIPS), pages 1548-1558, 2018.
[34] S. Ren, He, R. B. Girshick, and Sun. Faster R-CNN: towards real-time object detection with region proposal networks. In Conference on Neural Information Processing Systems (NeurIPS), pages 11,99, 2015.
[35] R. Saqur and K. Narasimhan. Multimodal graph networks for compositional generalization in visual question answering. In Conference on Neural Information Processing Systems (NeurIPS), 2020.
[36] R. R. Selvaraju, S. Lee, Shen, H. Jin, S. Ghosh, I.. P. Heek, D. Batra, and D. Parikh. Taking a HINT: leveraging explanations to make vision and language models more grounded. In JEEE International Conference on Computer Vision (ICCV), pages 2591-2600, 2019.
[37] H. Tan and M. Bansal. LXMERT: learning cross- moddatty encoder representations from transformers. In K. Inui, J. Jiang. V. Ng. and X. Wan. editors, Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5099-5110, 2019.
12

---

[38] D. Teney, E. Abbasnejad. K. Kafle, R. Shrestha, C. Kanan, and A. van den Hengel. On the value of out-of-distribution testing: An example of goodhart's law. In Conference on Neural Information Processing Systems (NeurIPS), 2020.
[39] D. Teney, E. Abbasnejad, and A. den Hengel. Learning what makes difference from counterfactual examples and gradient supervision. European Conference on Computer Vision (ECCV), pages 580-599, 2020.
[40] D. Teney, E. Abbasnejad, and van den Hengel. Unshuffling data for improved generalization visual question answering. In IEEE International Conference on Computer Vision (ICCV), pages 1417-1427, 2021.
[41] A. van den Oord, Y. Li, and O. Vinyals. Representation learning with contrastive predictive coding, arXiv preprint Xrviv
/ /807,037487 2018.
[42] P. Wang. Q. Wu, C. Shen, A. R. Dick, and A. van den Hengel. FVQA: fact-based visual question answering.
IEEE Transactions on Puttern Analysis and Machine Intelligence (TPAMI), 40(10))2413 2427, 2018.
[43] X. Wang, Q. Huang, A. Celikyilmaz, J. Gao, D. Shen, Y.-E Wang. W. Y. Wang, and L. Zhang. Reinforced cross modal matching and self- superviddd imitation learning for vision- language navigation. In IEEE Conference Computer Vision and Pattern Recognition (CVPR), pages 6629 6638,, 2019.
[44] J. Wu and R. J. Mooney. Self-critical reasoning for robust visual question answering. In Conference on Neural Information Processing Systems (NeurlPS), pages 8601-861 2019.
[45] S. Yang, G. Li, and Y. Yu. Graph-structured referring expression reasoning in the wild. In JEEE Conference Computer Vision and Pattern Recognition (CVPR), pages 9949 9949-9958, 2020.
[46] Z. Yang. X. He, J. Gao, L. Deng, and A. J. Smola. Stacked attention networks for image question answering. In IEEE Conference Computer Vision and Pattern Recognition (CVPR), pages 21-29, 2016.
[47] Y. Zhang, B. Hooi, D. Hu, J. Liang, and J. Feng. Unleashing the power of contrastive self supervisd visual models via contrast-regularing fine-tuning. In Conference on Neural Information Processing Systems (NeurIPS), 2021.
[48] Y. Zheng. Z. Wen, M. Tan, R. Zeng, Q Chen, Y. Wang, and Q Wu. Modular graph attention network for complex visual relational reasoning. Asian Conference on Computer Vision (ACCV), volume 12627, pages 137-153, 2020.
[49] X. Zhu, Mao, C. Liu, P. Zhang, Wang, and Zhang, Overvoming language priors with self-supervised Icarning for visual question answering. In International Joint Conferences on Artificial Intelligence (ICCAI), pages 1089-1089, 2020.
13


Reviews:
[{"review_id": "KgmpSbfzAQ", "reviewer": "Reviewer_pgR5", "summary": "The paper targets the bias problem in visual question answering task where the models may only capture the biases instead of showing real reasoning abilities. The paper introduces a method called D-VQA which tries to alleviate this problem from feature and sample perspective. With two unimodal bias detection modules, D-VQA recognizes and removes the negative biases while authors also construct two types of negative samples to increase sensitiveness of VQA models to both modalities by minimizing the possibility of predicting correct ground-truth answer of the positive sample. The results on VQA-CP and VQA v2 dataset show the effectiveness of the approach.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The paper introduces an approach called D-VQA which targets the bias problem in VQA. The paper is well written and is easy to follow and targets an important task which can help alleviate problems in the data collection methods such as bias that can occur naturally. My comments follow below:\n\n1) For feature based debiasing, the model has two unimodal bias detection modules which pass image alone and text alone through the model to obtain the bias feature. This bias feature is removed from the multimodal feature and optimized for binary cross entropy loss for answers. The original multimodal feature is brought close to bias free multimodal feature through contrastive loss making sure that no extra parameters are used during inference.\n2) For sample based debiasing, the authors sample negative sample for positive pairs by replacing either the text or the image. The binary cross entropy loss for the negative pairs is maximized so that the model is sensitive to the multimodal input and not focus on bias in the unimodal input.\n3) For the ablations, they should have also been conducted on VQA v2 so that reader can understand the impact and differences on the well-calibrated benchmark. At the moment, it is hard to say how exactly the ablations work in a setting with actual bias.\n4) For the LXMERT+D-VQA option, it would have been useful to have numbers on VQA v2 to understand if D-VQA introduces a drop or gain in performance for the models which are already performing very high on VQA v2 task.\n5) The crucial details for the training have been added into the supplementary but instead should be including in the main paper as the paper feels incomplete without them. Also, is the LXMERT backbone pretrained or not? If yes, how does D-VQA affect pretraining?\n6) From the ablations, it looks like that the sample based debiasing on vision side results in the most gain on the final results. There is no independent evaluation of just using feature based \"question-to-answer\" and \"vision-to-answer\" debiasing and understand how impactful those are. This is in contrast with how paper is structured mostly around feature-based debiasing even though sample-based debiasing which provides the most gain is only discussed for one paragraph. This questions the novelty of the paper as the sample based debiasing on vision side is obvious given numerous past study on the question side bias and is simple to add.\n7) Table 2, Row 1 is technically simple UpDn. Why is that number different compared to Table 1, Row 3?\n8) Authors stress on the point that they don't use any extra data for their augmentations. But contrary, why not improve results possibly by using techniques from MUTANT etc. It would have been good to see if extra data augmentations techniques can work well with DVQA.\n9) Figure 1 necessarily doesn't show the problem of bias in VQA clearly and probably should something more concrete along with questions.\n10) The name D-VQA is too general and I would suggest authors to rename title of their paper to be something more specific as it doesn't completely solved the problem of bias in VQA as it currently seems like reading the paper title.\n11) The paper only tests the debiasing on images from COCO domain specifically. It would be good to see generalization to other domains such as TextVQA as well.\n\n\nPost Rebuttal:\nPlease check my response to the authors.\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The broader impact statement has been included and is reasonable.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "6"}, "extra_text": {}}, {"review_id": "H_aARHT_J_", "reviewer": "Reviewer_jcD5", "summary": "The paper introduces “D-VQA”, a training approach dedicated to remove harmful biases (both language and vision ones) in VQA models. D-VQA performs in two ways, namely: feature-wise and sampling-wise.\n\nOn the features side, during its training, D-VQA attempts to de-bias the features learned by a VQA model by subtracting the (deliberately) biased features learned by unimodal language-only and vision-only models. Moreover, in order to get rid of the unimodal branches during the inference, D-VQA training includes an additional contrastive loss which aims to bring the original VQA features closer to the debiased ones (i.e. the ones obtained after feature subtraction with the unimodal branches).\n\nOn the sampling side, D-VQA employs an existing approach proposed in [42] (which is properly mentioned and recognized by the authors) introducing negative sampling triplets for each positive triplet (by randomly changing either an image or a question in a positive triplet).\n\nCombining the 2 aforementioned techniques allows D-VQA to obtain the state-of-the-art results on both VQA-CP and VQAv2 comparing to the existing bias-reduction methods (while using the UpDn model as a backbone). In addition to that, the authors demonstrate that their approach is model-agnostic (for example, it is compatible with a more recent and powerful LXMERT backbone).", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "*Originality*\n\nThe paper rather exploits and successfully combines already-existing ideas for bias-reduction in VQA (by clearly citing the original works) than proposes radically new ones.\n\nThus, the sampling of negative triplets was proposed in [42]. In this work, the authors straightforwardly extend this idea by randomly switching both images and questions.\n\nSimilarly, de-biasing the VQA features via learning deliberately biased unimodal ones was also proposed in many recent papers (for example, in [9]).\n\nIn my opinion, the main originality of the present work is the idea of the contrastive learning (the loss L_c) which makes the VQA model independent of its unimodal branches at the inference stage (and, therefore, accelerates and simplifies the final model).\n\n*Quality*\n\nTo the best of my knowledge, the significant related work has been properly cited and compared with the proposed approach. The claimed contributions are supported with conclusive experimental results and ablation studies on the VQA-CP and VQAv2 datasets.\n\n*Clarity*\n\nThe paper is clearly written and easy to follow. Some important implementation details can be found in Supplementary Materials.\n\n*Significance*\n\nAll in all, D-VQA can be considered as a significant contribution as it demonstrates the complementarity of two (almost orthogonal) branches of the research on bias reduction in VQA, namely: feature de-biasing and training balancing.\n\nThe results could have been even more convincing had the authors tested their approach on other unbiased datasets than VQA-CP (for example, GQA-OOD: https://github.com/gqa-ood/GQA-OOD - the dataset has been published after the NeurIPS deadline so the lack of evaluation on it cannot be considered as a problem).\n\n*Minor remarks / questions*\n\n1.\tIn the ablation study reported in Table 3, you demonstrate that amplifying the unimodal biased feature vectors f_q and f_v with the estimated real-values b_q and b_v (obtained via “bias detection modules”) improves the performances. Would not it work even better if b_q and b_v were vectors (as in [9], for example) rather than simple real values (in this case, f^{bias}_q could be a pointwise product between f_q and b_q)?\n\n2.\tI am not sure to understand the Equation 5. Does “a” denote a vector or a single real value? Indeed, on the one hand, you state that “m_a” outputs a single real value. But on the other hand, “a” is an output of the softmax operation and is also transposed in Eq. 5 indicating that it is rather a vector.\n\n*Typos*\n\n1.\tLine 103 (and also in other parts of the article): you employ the word “auxiliary” as a verb. I am not a native speaker, but I do not understand what that means, and I am not sure that it is grammatically correct.\n\n2.\tLine 195: “contains” -> “containing”.\n\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The \"broader impact\" statement has been included in the article and seems reasonable.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "7"}, "extra_text": {}}, {"review_id": "HDhfHFti2YH", "reviewer": "Reviewer_e8gV", "summary": "This work addresses a known bias in visual question answering tasks, in which networks learn to pick an answer based on a single modality without perceiving the entire input.  The work novelty comes from proposing a set of regularization strategies s.t: (1) reducing \"bad\" bias that is not a useful commonsense knowledge;  (2) reducing computational inferences through contrastive loss; (3) considering an image-only bias; (4) employ negative sampling to increase attention sensitiveness on both modalities. In both VQA and VQA-CP, the approach shows state-of-the-art performance.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Originality: A known bias with VQA models is that they do not perceive all inputs and take shortcuts. There have been various attempts to fix this issue through training an additional unimodal classifier and removing those biases (e.g., RUBi, LMH). However, this work offers some novel ideas, like negative sampling and contrastive loss. Further, the proposed approach allows the choice of unimodal features that are not contributing to the joint task, while previous works argue that unimodal features should be completely removed. \n\nQuality: Most of the techniques used are conventional (e.g., negative sampling, contrastive loss). Therefore, I consider the approach to be technically sound.  I recommend providing more qualitative examples, particularly negative ones. Also, in my view, since the bias-detection module is a novelty, every supporting evidence for bias-detection in the supplementary material (e.g., Tab 1, Fig 2) is appropriate for the main paper. \n\nClarity: The submission is clear, and the ideas are intuitive. The appendix provides implementation details but no code. A point I find quite confusing is the bias detection model's ability to distinguish between a 'good' and a 'bad' bias. A 'good' bias, according to the authors, helps the joint task. However, the joint task does pick a 'bad' bias as well. I would appreciate a clarification on this. \n\nSignificance: Visual question answering is an important and popular task. The bias discussed in this paper is a major concern in any VQA model, which is why the popular VQA-CP dataset was developed.  The proposed techniques should aid in advancing the goals of answering questions using reasoning instead of unaware biased correlations.  Importantly,  the experiments show a significant improvement in the performance of the VQA-CP task (61.91 vs. 57.59)\n\nTo summarize, here's what concerns me:\n[W1] Are there any negative effects for the proposed methods?\n[W2] Can the authors comment on the impact of different f_q/f_v? For instance, f_g can be an LSTM encoder for textual input. \n[W3] Why the b_q layer picks only 'bad' bias? \n\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The authors have adequately addressed the limitations and potential negative social impact of their work", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "6"}, "extra_text": {}}, {"review_id": "E4oL_scp9s", "reviewer": "Reviewer_hQzS", "summary": "This paper tackle explores how to partially annealed dataset biases when training VQA models.\n To do so, they applied to debiasing strategy:\n  - they generate free negative samples to tentatively debased the answer distribution\n - they use a neural pipeline to tentatively debiased the multimodal features\nInterestingly, both approaches are model-agnostic (up to $f = F(h,g)$), making the approach generic.\n\nThe paper has many merits and solid results. Multiple complementary experiments are insightful.\nYet, there are a few remaining ambiguities that prevent me from fully advocating for acceptance.\n I would be more than happy to increase my score if they are correctly addressed:", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "\n\nKey remarks:\n\nA) The authors emphasize the debiasing aspect of the neural pipeline. It includes equations 3-7. My main concern is about equation 6 and the loss $L_d$.\nFirst, can you elaborate on the $L_d$? What are the target labels of $L_d$ (or is it equivalent of $L_{vqa}$)? More generally, why this specific loss would only keep the “good” biases? \nSecond, as $f$ and $f_{bias}$ are trained independently (and thus, they do not live in the same space), why computing the difference would debias $f$?\n\nThird, some design choices are not well-justified, e.g. why b_q.f_q instead of directly using $f_q$? Why using attention to combine $fˆ^ q_{bias}$ and $f^v_{bias}$? Even if I understand the intuition of those patterns, they remain arbitrary design choices that increase the complexity of the model. I would thus recommend the authors ablate them to justify them.\n \n B) In light of Table 2, the sampling procedure looks crucial. However, no ablation only contains feature-debiasing to determine its impact correctly. Therefore, it is hard to estimate whether feature debiasing has a marginal effect or not. Second, the negative samples are not correctly put in the perspective of the literature. It is mentioned that the process is inspired from 32 and 42 without more details. This is unfortunate as the current paper does show that sampling jointly questions and images is beneficial (which was not observed in 32 and 42). It took me quite some time to be sure that 3.3 was an actual (simple but valid) contribution to the paper.\n\n Potential improvements:\n\nC) I understand the need to have $L_{sam}$ to remove the feature debasing pipelines. However, this aspect can be further study: 1) What happens if the loss is removed and the pipeline is removed at test time? Is there an implicit alignment of features or the debiasing signal is just lost? 2) What happens if the full pipeline is preserved at test time (more parameters), is the final higher as there is no proxy? \n\nD) Positive biases are quite a controversial\ttopic; I would recommend detailing a bit more this section with the relevant literature. On the other side, there is no need to have such an extended related work section on self-supervised learning… as there is no self-supervised learning pipeline in the paper. Self-supervised learning is not CPC! Contrastive losses existed far before SimCLR!\n\nOther remarks:\n - Overall, the paper is clearly written. I may recommend making figure 2, a bit bigger while showing the equation. Another idea would be to summarise all the equation 3-7 into a single one to help the global understanding of the model (in Appendix?).\nRelated work is correct. As mentioned, there is no need for detailed self-supervised learning literature. \nL123: please add citations after “most previous methods.”\nL218: are \\hat{q} and \\hat{v} from the same question-image pair?\nTable1 is complete. Well appreciated\nAs mentioned in B, please complete the ablation in Table 2.\nTables 4 and 6 are persuasive.\nDo you. Have some piece of intuition why D-VQA is so robust with few labels? Is it because of negative sampling (potential additional experiment)\n\n\nConclusion:\n This paper is consistent and has a pure-ML approach that I really appreciate. It also has solid results and convincing complementary experiments.\nHowever, there are still some obscure points that I encourage the authors to clarify before I can recommend acceptance. \nBesides, if some of the experiments I recommended were performed (e.g, Complete Tab2, ablate $L_sim$, ablate neural choice), I am eager to increase my score even more.\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "LGTM", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "4h"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposed a new method for debiasing VQA models. A consensus for acceptance emerged very quickly in the discussion phase, helped by a swift rebuttal by the authors and additional experiments on new datasets.

The AC concurs.
Decision: Accept (Poster)