prior knowledge for constraining answer search space in visual question answering
Abstract.
Diﬀerent approaches have been proposed to Visual Question Answering (VQA).
However, few works are aware of the behaviors of varying joint modality methods over question type prior knowledge extracted from data in constraining answer search space, of which information gives a reliable cue to reason about answers for questions asked in input images.
In this paper, we propose a novel VQA model that utilizes the question-type prior information to improve VQA by leveraging the multiple interactions between diﬀerent joint modality methods based on their behaviors in answering questions from diﬀerent types.
The solid experiments on two benchmark datasets, i.e., VQA 2.0 and TDIUC, indicate that the proposed method yields the best performance with the most competitive approaches.
Introduction
The task of Visual Question Answering (VQA) is to provide a correct answer to a given question such that the answer is consistent with the visual content of a given image.
The VQA research raises a rich set of challenges because it is an intersection of diﬀerent research ﬁelds including computer vision, natural language processing, and reasoning.
Thanks to its wide applications, the VQA has attracted great attention in recent years [4, 42, 9, 43, 2, 16, 35, 38, 21, 23, 39, 40, 44, 45, 29, 26, 34, 36, 41, 46, 3].
This also leads to the presence of large scale datasets [4, 31, 11, 18, 15] and evaluation protocols [4, 15].
There are works that consider types of question as the side information which gives a strong cue to reason about the answer [1, 35, 14].
However, the relation between question types and answers from training data have not been investigated yet.
Fig.
1 shows the correlation between question types and some answers in the VQA 2.0 dataset [11].
It suggests that a question regarding the quantity should be answered by a number, not a color.
The observation indicated that the prior information got from the correlations between question types and answers open an answer search space constrain for the VQA model.
The search space constrain is useful for VQA model to give out ﬁnal prediction and thus, improve the overall performance.
The Fig. 1 is consistent with our observation, e.g., it clearly suggests that a question regarding the quantity should be answered by a number, not a color.
[Figure]
In current state-of-the-art VQA systems, the joint modality component plays an important role since it would learn meaningful joint representations between linguistic and visual inputs [42, 43, 2, 16, 27, 37].
Although diﬀerent joint modality methods or attention mechanisms have been proposed, we hypothesize that each method may capture diﬀerent aspects of the input.
That means diﬀerent attentions may provide diﬀerent answers for questions belonged to diﬀerent question types.
Fig.
2 shows examples in which the attention models (BAN [16] and SAN [43]) attend on diﬀerent regions of input images when dealing with questions from diﬀerent types.
Unfortunately, most of recent VQA systems are based on single attention models [42, 43, 2, 16, 35, 9].
From the above observation, it is necessary to develop a VQA system which leverages the power of diﬀerent attention models to deal with questions from diﬀerent question types.
In this paper, we propose a novel multiple interaction learning with questiontype prior knowledge (MILQT) which extracts the question-type prior knowledge from questions in data to constrain the answer search space and then leverage diﬀerent behaviors of multiple attention mechanisms in dealing with questions from diﬀerent types.
Our contributions are summarized as follows.
(i)
We propose a novel VQA model that leverages the question-type information to augment the VQA loss. This encourages the model to pay more focus on answers that are correlated with the question type of the input question, i.e. answer search space constrain.
(ii) We have identiﬁed that diﬀerent attention mechanisms shows diﬀerent performance in dealing with questions from diﬀerent types.
Thus, our model is designed to learn the multiple interactions between diﬀerent attention mechanisms based on their behaviors in answering questions from diﬀerent types.
This allows the system to automatically ﬁnd an optimal combination between these attention mechanisms to gain better results.
Question: Is the woman in front sitting on a chair?
Ground-truth:
Yes
Answer:
No
Answer:
Yes
Question: Who is holding the umbrella?
Ground-truth: Woman
Answer: Woman
Answer: Man
Question: What sport is being played?
Ground-truth: Baseball
Answer: Baseball Answer: Baseball
SAN
SAN
SAN BAN
BAN
BAN
(iii)
The extensive experiments show that the proposed model yields the best performance with the most competitive approaches in the widely used VQA 2.0 [11] and TDIUC [15] datasets
Related Work
Visual Question Answering .
In recent years, VQA has attracted a large attention from both computer vision and natural language processing communities.
The existing approaches to VQA are mainly based on three main components:
(1) visual features of input image are extracted by
CNNs ] and linguistic features of input question are extracted by RNNs , 7]; (2) an attention model which is usually a deep neural network is employed to learn the joint semantic space from the image and language features [10, 25, 22, 27, 43, 2, 16, 9, 6]; (3) the joint semantic features are then subjected to a classiﬁcation module in which the VQA prediction is posed as a classiﬁcation task over a set of predeﬁned possible answers [31].
The recent VQA researches mainly focus on the development of diﬀerent attention models.
In [9], the authors proposed the Multimodal Compact Bilinear (MCB) pooling by projecting the visual and linguistic features to a higher dimensional space and then convolving both vectors eﬃciently by using element-wise product in Fast Fourier Transform space.
In [43], the authors proposed Stacked Attention Networks (SAN) which locate, via multi-step reasoning, image regions that are relevant to the question for answer prediction.
In [2, 38], the authors employed the top-down attention that learns an attention weight for each image region by applying non-linear transformations on the combination of image features and linguistic features.
In [27], the authors proposed a dense, symmetric attention model that allows each question word attends on image regions and each image region attends on question words.
In [16] the authors proposed Bilinear Attention Networks (BAN) that ﬁnd bilinear attention distributions to utilize given visual-linguistics information seamlessly.
Recently, in [37] the authors introduced Cross Modality Encoder Representations (LXMERT) to learn the alignment and relationships between two modalities, i.e., visual concepts and language semantics.
Regarding the question type, previous works have considered question-type information to improve VQA results.
Agrawal et al.
[1] trained a separated question-type classiﬁer to classify input questions into two categories, i.e., YesNo and non Yes-No.
Each category will be subsequently processed in diﬀerent ways.
In the other words, the question type information is only used for selecting suitable sub-sequence processing.
Shi et al.
[35] also trained a question-type classiﬁer to predict the question type.
The predicted one-hot question type is only used to weight the importance of diﬀerent visual features.
Kaﬂe et al.
[14] also used question type to improve the performance of VQA prediction.
Similar to [1], the authors separately trained a classiﬁer to predict the type of the input question.
The predicted question type is then used to improve VQA prediction through a Bayesian inference model.
In our work, diﬀerent from [1], [35] and [14], question types work as the prior knowledge, which constrain answer search space through loss function.
Additionally, we can further identify the performance of diﬀerent joint modality methods over questions from diﬀerent types.
Besides, through the multiple interaction learning, the behaviors of the joint modality methods are utilized on giving out the ﬁnal answer which further improve VQA performance.
VQA Datasets.
Several large-scale datasets have been proposed by [4, 15, 31] since the ﬁrst VQA dataset (DAQUAR) was released by [24].
The VQA 1.
ﬁrst introduced by [4] is strongly biased.
Speciﬁcally, some questions have same answers regardless the corresponding images.
The upgraded version of VQA 1. is introduced as VQA 2.0 [11] to solve the bias problem.
For every question in VQA 2.0 [11], there are two nearly similar images with diﬀerent answers.
The VQA 2.0 dataset is the most popular and is widely used in VQA problem.
In VQA 2.0 dataset, questions are divided into three groups: “Yes-No”, “Number” and “Other”.
In order to reduce the bias and increase the diversity of questions in VQA 1.0, Kushal [15] proposed a new dataset TDIUC in which questions belong to 12 diﬀerent categories.
They also proposed two evaluation metrics that compensate for forms of dataset bias.
Another widely-used dataset is Visual
Genome[18], which provides questions related to images that are supposed to have a high diversity.
Methodology
The proposed multiple interaction learning with question-type prior knowledge (MILQT) is illustrated in Fig.
Similar to the most of the VQA systems [16, 43, 2], multiple interaction learning with question-type prior knowledge (MILQT) consists of the joint learning solution for input questions and images, followed by a multi-class classiﬁcation over a set of predeﬁned candidate answers.
However,
MILQT allows to leverage multiple joint modality methods under the guiding of question-types to output better answers.
As illustrated in Fig.
3, MILQT consists of two modules: Question-type awareness A , and Multi-hypothesis interaction learning M .
The ﬁrst module aims to learn the question-type representation, which is further used to enhance the joint visual-question embedding features and to constrain answer search
[Figure]
−
[Figure]
space through prior knowledge extracted from data.
Based on the question-type information, the second module aims to identify the behaviors of multiple joint learning methods and then justify adjust contributions to giving out ﬁnal answer predictions.
Inthefollowing, we describe the representation of input questions and images in Section 3.1.
Section 3.2 presents the Question-type awareness module
A .
Section 3.3 presents the Multi-hypothesis interaction learning module M .
Section 3.4 presents the multi-task loss for entire model training.
3. Input Representation
Question representation.
Given an input question, follow the recent state-ofthe-art [2, 16], we trim the question to a maximum of 12 words.
The questions that are shorter than 12 words are zero-padded.
Each word is then represented by a 600-D vector that is a concatenation of the 300-D GloVe word embedding [30] and the augmenting embedding from training data as [16].
This step results in a sequence of word embeddings with size of 12 × 600 and is denoted as f w in
Fig 3.
In order to obtain the intent of question, the f w is passed through a Gated
Recurrent Unit (GRU) [7] which results in a 1024-D vector representation f q for the input question.
Image representation.
There are several object detectors have been proposed in the literature, of which outputs vary in size and location.
Inspired by recent advances of VQA [2, 35, 38], we use bottom-up attention, i.e. an object detection which takes as FasterRCNN [32] backbone, to extract image representation.
At ﬁrst, the input image is passed through bottom-up networks to get
K × 2048 bounding box representation which is denotes as f v in Fig.
Question-type classiﬁcation.
This component in module A aims to learn the question-type representation.
Speciﬁcally, aforementioned component takes the question embedding f q as input, which is then passed through several fullyconnected (FC) layers and is ended by a softmax layer which produces a probability distribution h over P question types, where P depends on the dataset, i.e., P equals 3 for VQA 2.0 [11] and equals 12 for TDIUC [15].
The question type embedding f qt extracted from question-type classiﬁcation component will be combined with the attention features to enhance the joint semantic representation between the input image and question, while the predicted question type will be used to augment the VQA loss.
Multi-level multi-modal fusion.
Unlike the previous works that perform only one level of fusion between linguistic and visual features that may limit the capacity of these models to learn a good joint semantic space.
In our work, a multi-level multi-modal fusion that encourages the model to learn a better joint semantic space is introduced which takes the question-type representation got from question-type classiﬁcation component as one of inputs.
First level multi-modal fusion: The ﬁrst level fusion is similar to previous works [2, 16, 43].
Given visual features f v , question features f q , and any joint modality mechanism (e.g., bilinear attention [16], stacked attention [43], bottomup [2] etc.), we combines visual features with question features and learn attention weights to weight for visual and/or linguistic features.
Diﬀerent attention mechanisms have diﬀerent ways for learning the joint semantic space.
The detail of each attention mechanism can be found in the corresponding studies [43, 16, 2].
The output of the ﬁrst level multi-modal fusion is denoted as f att in the
Fig.
Second level multi-modal fusion: In order to enhance the joint semantic space, the output of the ﬁrst level multi-modal fusion f att is combined with the question-type feature f qt , which is the output of the last FC layer of the “Question-type classiﬁcation” component.
We try two simple but eﬀective operators, i.e. element-wise multiplication — EWM or element-wise addition —
EWA , to combine f att and f qt .
The output of the second level multi-modal fusion, which is denoted as f att − qt in Fig.
3, can be seen as an attention representation that is aware of the question-type information.
Given an attention mechanism, the f att − qt will be used as the input for a classiﬁer that predicts an answer for the corresponding question.
This is shown at the “Answer prediction” boxes in the Fig.
Augmented VQA loss. The introduced loss function takes model predicted question types and prior knowledge question types from data to identify the answer search space constraints when the model outputs predicted answers.
Prior computation.
In order to make the VQA classiﬁer pay more attention on the answers corresponding to the question type of the input question, we use the statistical information from training data to identify the relation between the question type and the answer.
The Alg. 1 presents the calculation of the prior information between the question types and the answers.
To calculate the prior, we ﬁrstly make statistics of the frequency of diﬀerent question types in each VQA candidate answer.
This results in a matrix m qt − ans (lines 2 to 4).
We then column-wise normalize the matrix m qt − ans by dividing elements in a column by the sum of the column (lines 5 to 7).
Augmented VQA loss function design l vqa .
Let y i ∈
R
A × 1 , g i ∈
R
A × 1 , h i ∈ R P × 1 be the VQA groundtruth answer, VQA answer prediction, and the question-type prediction of the i th input question-image, respectively.
Given the question, our target is to increase the chances of possible answers corresponding to the question type of the question.
To this end, we ﬁrst deﬁne the weighting (question-type) awareness matrix m awn by combining the predicted questiontype h i and the prior information m qt − ans as follows:
[Equation]
This weighting matrix is used to weight the VQA groundtruth y i and VQA answer prediction g i to as follows:
[Equation]
where (cid:12) is the element-wise product.
As a result, this weighting increases the chances of possible answers corresponding to the question type of the question.
Finally, the VQA loss l vqa is computed as follows:
[Equation]
where Q and A are the number of training questions and candidate answers; σ is the element-wise sigmoid function.
(4) is a soft cross entropy loss and has been shown to be more eﬀective than softmax in VQA problem [38].
It is worth noting that when computing the weighting matrix a awn in (1), instead of using the predicted question type h
i , we can also use the groundtruth question type.
However, we found that there are some inconsistency between the groundtruth question types and the groundtruth answers.
For example, in
VQA 2.0 dataset, most of questions started by “how many” are classiﬁed with the question type “number”, and the answers to these questions are numeric numbers.
However, there are also some exceptions.
For example, the question “How many stripes are there on the zebra?” is annotated with the groundtruth question-type “number” but its annotated groundtruth answer is “many”, which is not a numeric number.
By using groundtruth question type to augment the loss, the answer to that question is likely a numeric number, which is an incorrect answer compared to the groundtruth answer.
In order to make the model robust to these exceptions, we use the predicted question type to augment the
VQA loss.
Using the predicted question type can be seen as a self-adaptation mechanism that allows the system to adapt to exceptions.
In particular, for the above example, the predicted question type may not be necessary “number” and it can be “other”.
3. Multi-hypothesis interaction learning
As presented in Fig.
3, MILQT allows to utilize multiple hypotheses (i.e., joint modality mechanisms).
Speciﬁcally, we propose a multi-hypothesis interaction learning design M that takes answer predictions produced by diﬀerent joint modality mechanisms and interactively learn to combine them.
Let g ∈ R
A × J be the matrix of predicted probability distributions over A answers from the
J joint modality mechanisms.
M outputs the distribution ρ ∈ R A , which is calculated from g through Equation (5).
[Equation]
w mil ∈ R P
× J is the learnable weight which control the contributions of J considered joint modality mechanisms on predicting answer based on the guiding of
P question types; (cid:12) denotes Hardamard product.
3. Multi-task loss
In order to train the proposed MILQT, we deﬁne a multi-task loss to jointly optimize the question-type classiﬁcation, the answer prediction of each individual attention mechanism, and the VQA loss (4).
Formally, our multi-task loss is deﬁned as follows:
[Equation]
Question: How many couches? Ground-truth: Answer: Answer:
Answer: 2 Question type: Number SAN BAN OURS
Question: What kind of bike is this? Ground-truth: Motorcycle Answer: Motorcycle Answer: Motorcycle
Answer: Motorcycle Question type: Other SAN BAN OURS
Question:
Is the woman 's hat striped? Ground-truth: Yes Answer:
Yes
Answer: No Answer: Yes Question type: Yes/No
SAN
BAN
OURS
Question:
[Figure]
Answer:
[Figure]
[Figure]
where α 1 , α 2 , α 3 are parameters controlling the importance of each loss; l qt is the question-type classiﬁcation loss; l H j is the answer prediction loss of j th mechanism over J joint modality methods; l vqa is the introduced VQA loss augmented by the predicted question type and the prior information deﬁned by (4).
Experiments 4. Dataset and implementation detail
Dataset.
We conduct the experiments on two benchmark VQA datasets that are VQA 2.0 [11] and TDIUC [15].
The VQA 2.0 dataset is the most popular and is widely used in VQA problem.
In VQA 2.0 dataset, questions are divided into three question types, i.e., “Yes-No”, “Number” and “Other” while the TDIUC dataset has 12 diﬀerent question types.
As standardly done in the literature, we use the standard VQA accuracy metric [4] when evaluating on VQA 2.0 dataset and Arithmetric MPT as well as Harmonic MPT proposed in [15] when evaluating on TDIUC 3 .
Implementation detail.
Our proposed MILQT is implemented using PyTorch [28].
The experiments are conducted on a single NVIDIA Titan V with 12GB RAM.
In all experiments, the learning rate is set to 10 − 3 (or 7 × 10 − 4 if using Visual
Genome [18] as augmenting data) and batch size is set to 256.
The number of detected bounding boxes is set to 50 when extracting visual features.
The GRU [7] for question embedding has one layer with 1024-D hidden state and processes words in forward order.
During training, except image representations f v ,
[Table]
[Table]
Contribution of hypothesis interaction learning
[Table] [Table]
other components are trained end-to-end with the multi-task loss (6).
AdaMax optimizer [17] is used to train our model.
4. Ablation study
To evaluate the contribution of question-type awareness A module and multihypothesis interaction learning M in our method, we conduct ablation studies when training on the train set and testing on the validation set of VQA 2.0 [11].
Starting with the BAN glimpse 2 with counter sub-module (BAN-2-Counter) [16] as the baseline, we show the eﬀectiveness of proposed modules when they are integrated into the baseline.
The counter sub-module [47] is used in the baseline to prove the extendability of proposed model on supporting “Number” question.
However, any sub-modules can also be applied, e.g., relational reasoning submodule [33] to support for “Yes/No” and “Other” questions.
It is worth noting that in order to make a fair comparison, we use the same visual features and question embedding features for both BAN-2-Counter baseline and our model.
of question -type awareness and prior information proposed in Section 3.2.
The ﬁrst section in Table 1 shows that by having second level multi-modal fusion (Section 3.2) which uses element-wise multiplica-
[Table] [Table]
tion ( +mul ) to combine the question-type feature f qt and the attention feature f att , the overall performance increases from 65 .
25% (baseline) to 65 .
By further using the predicted question type and the prior information ( +prior ) to augment the VQA loss, the performance increases to 66 .
13% which is +0 .
88% improvement over the baseline.
The results in the ﬁrst section in Table 1 conﬁrm that combining question-type features with attention features helps to learn a better joint semantic space, which leads to the performance boost over the baseline.
These results also conﬁrm that using the predicted question type and the prior provides a further boost in the performance.
We also ﬁnd out that using EWM provides better accuracy than EWA at the second level fusion.
The eﬀectiveness of multi-hypothesis interaction learning proposed in Section 3.3.
The second section in Table 1 shows the eﬀectiveness when leveraging diﬀerent joint modality mechanisms by using multi-hypothesis interaction learning.
By using BAN-2-Counter [16] and BAN-2 [16] (BAN-2-Counter + BAN-2), the overall performance is 66 .
15% which is +0 .
9% improvement over the BAN-2-Counter baseline.
Table 3 illustrates the correlation between diﬀerent joint modality mechanisms and question types.
This information is extracted from w mil which identify the contributions of each mechanism in giving ﬁnal VQA results guiding by the question type information.
The results in Table 4 indicate that some joint modality methods achieve better performance in some speciﬁc question types, e.g., joint modality method BAN outperform other methods in Number question type by a large margin.
The correlation in Table 3 and performance in Table 4 also indicates that the MILQT model tends to leverage the contribution of joint methods proportional to their performance in each speciﬁc question type.
Besides, the results in Tableindicate that under the guiding of question type, M module produce better performance when comparing with none-use solution or the weighted sum method [19] in which the predictions of diﬀerent joint modality mechanisms are summed up and the answer with highest score are considered as the ﬁnal answer.
The eﬀectiveness of the entire proposed model.
The third section in Table 1 presents results when all components (except the visual feature extractor) are combined in a uniﬁed model and are trained end-to-end.
To verify the eﬀectiveness of the proposed framework, we conduct two conﬁgurations.
In the ﬁrst conﬁguration, we use two joint modality mechanisms BAN-2-Counter and BAN-2, the EWM in the second level multi-modal fusion, and the predicted question type together with the prior information to augment the loss.
The second conﬁguration is similar to the ﬁrst conﬁguration, except that we use
BAN-2-Counter and SAN in interaction learning.
The third section on Tableshows that both conﬁgurations give the performance boost over the baseline.
The second conﬁguration achieves better performance, i.e., 66 .
48% accuracy, which outperforms over the baseline BAN-2-Counter +1 .
Table 1 also show that using “question-type awareness” gives further boost over using interaction learning only, i.e., the performance of “BAN-2-Counter + SAN + Mul + prior” (66.48) outperforms the performance of “BAN-2-Counter + SAN” (65.64).
Fig.presents some visualization results of our second conﬁguration and other methods on the VQA 2.0 validation set.
Question-type classiﬁcation analysis The proposed MILQT is a model which allows joint training between question-type classiﬁcation and VQA answer classiﬁcation.
The eﬀectiveness of multi-task learning helps to improve performance in both tasks.
To further analyze the eﬀectiveness of MILQT in the question-type classiﬁcation, we provide in this section the question type classiﬁcation on TDIUC dataset.
We follow QTA [35] to calculate the accuracy, i.e., the overall accuracy is the number of correct predictions over the number of testing questions, across all categories.
The results are presented in Table 5.
Our MILQT uses BAN-2 [16], BAN2-Counter [16], and SAN [43] in the interaction learning, element-wise multiplication in the second level of multi-modal fusion, and the predicted question type with prior information to augment the VQA loss.
Compare to the state-ofthe-art QTA [35], our MILQT outperforms QTA for most of question types.
In overall, we achieve state-of-the-art performance on question-type classiﬁcation task on TDIUC dataset with 96 .
45% accuracy.
[Table]
It is worth noting that for the “Utility and Aﬀordances” category, the question type classiﬁcation accuracy is 0% for both QTA and MILQT.
It is because the imbalanced data problem in TDIUC dataset.
The “Utility and Aﬀordances” category has only ≈ 0 .
03% samples in the dataset.
Hence this category is strongly dominated by other categories when learning the question type classiﬁer.
Note that, there are cases in which questions belonging to the “Utility and Aﬀordances” category have similar answers with questions belonging to other categories.
Thus, the data becomes less bias w.r.t.
answers (in comparing to question categories).
This explains why although both MILQT and QTA have 0% accuracy for the “Utility and Aﬀordances” on the question category classiﬁcation, both of them achieve some accuracy on the VQA classiﬁcation (see Table 5)
[Table]
4. Comparison to the state of the art
Experiments on VQA 2.0 test-dev and test-standard.
We evaluate MILQT on the test-dev and test-standard of VQA 2.0 dataset [11].
To train the model, similar to previous works [43, 38, 13, 16], we use both training set and validation set of VQA 2.0.
We also use the Visual Genome [18] as additional training data.
MILQT consists of three joint modality mechanisms, i.e., BAN-2, BAN-2Counter, and SAN accompanied with the EWM for the multi-modal fusion, and the predicted question type together with the prior information to augment the
VQA loss.
Table 4 presents the results of diﬀerent methods on test-dev and teststd of VQA 2.0.
The results show that our MILQT yields the good performance with the most competitive approaches.
Experiments on TDIUC.
In order to prove the stability of MILQT, we evaluate MILQT on TDIUC dataset [15].
The results in Table 6 show that the proposed model establishes the state-of-the-art results on both evaluation met- [Table] rics Arithmetic MPT and Harmonic MPT [15].
Speciﬁcally, our model significantly outperforms the recent QTA [35], i.e., on the overall, we improve over
QTA 6 .
1% and 11 .
1% with Arithemic MPT and Harmonic MPT metrics, respectively.
It is worth noting that the results of QTA [35] in Table 6, which are cited from [35], are achieved when [35] used the one-hot predicted question type of testing question to weight visual features.
When using the groundtruth question type to weight visual features, [35] reported 69 .
11% and 60 .
08% for Arithemic
MPT and Harmonic MPT metrics, respectively.
Our model also outperforms these performances a large margin, i.e., the improvements are 3 .
9% and 6 .
8% for
Arithemic MPT and Harmonic MPT metrics, respectively.
We also note that for the question type “Absurd”, we get lower performance than QTA [35].
For this question type, the question is irrelevant with the image content.
Consequently, this question type does not help to learn a joint meaningful embedding between the input question and image.
This explains for our lower performance on this question type.
Conclusion
We present a novel multiple interaction learning with question-type prior knowledge for constraining answer search space— MILQT that takes into account the question-type information and the prior to improve the VQA performance at diﬀerent stages.
The system also allows to utilize and learn diﬀerent attentions under a uniﬁed model in an interacting manner.
The extensive experimental results show that all proposed components improve the VQA performance.
We yields the best performance with the most competitive approaches on VQA 2. and TDIUC dataset.