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| \begin{document} | |
| \title{Making the V in VQA Matter: \\ | |
| Elevating the Role of Image Understanding in Visual Question Answering} | |
| \author{Yash Goyal\thanks{The first two authors contributed equally.} $\,^1$ | |
| \quad Tejas Khot\footnotemark[1] $\,^1$ \quad Douglas Summers-Stay$^2$ \quad Dhruv Batra$^3$ \quad Devi Parikh$^3$\\ | |
| $^1$Virginia Tech \quad $^2$Army Research Laboratory \quad $^3$Georgia Institute of Technology\\ | |
| {\tt\small $^1$\{ygoyal, tjskhot\}@vt.edu} \quad \tt\small $^2$douglas.a.summers-stay.civ@mail.mil \quad \tt\small $^3$\{dbatra, parikh\}@gatech.edu} | |
| \maketitle | |
| \begin{abstract} | |
| Problems at the intersection of vision and language are of significant | |
| importance both as challenging | |
| research questions and for the rich set of applications they enable. | |
| However, inherent structure in our world and bias in our language | |
| tend to be a simpler signal for learning than visual modalities, | |
| resulting in models that ignore visual information, | |
| leading to an inflated sense of their capability. | |
| We propose to counter these language priors for the task of | |
| Visual Question Answering (VQA) and make vision (the V in VQA) matter! | |
| Specifically, we \emph{balance} the popular VQA dataset~\cite{VQA} by collecting | |
| complementary images such that every question | |
| in our balanced dataset is associated with not just a single image, but | |
| rather a \emph{pair of similar images} | |
| that result in two different answers to the question. | |
| Our dataset is by construction more balanced than the original VQA | |
| dataset and has approximately \emph{twice} | |
| the number of image-question pairs. | |
| Our complete balanced dataset is publicly available at \url{http://visualqa.org/} as part of the 2nd iteration of the Visual Question Answering Dataset and Challenge (VQA v2.0). | |
| We further benchmark a number of state-of-art VQA models on our balanced dataset. | |
| All models perform significantly worse on our balanced dataset, | |
| suggesting that these models have indeed learned to exploit | |
| language priors. This finding provides the first concrete empirical evidence | |
| for what seems to be a qualitative sense among practitioners. | |
| Finally, our data collection protocol for identifying | |
| complementary images enables us to develop a novel interpretable model, | |
| which in addition to providing an answer to the given (image, question) | |
| pair, also provides a counter-example based explanation. | |
| Specifically, it identifies an image that is similar to the original | |
| image, but it believes has a different answer to the same question. | |
| This can help in building trust for machines among their users. | |
| \end{abstract} | |
| \section{Introduction} | |
| \label{sec:intro} | |
| Language and vision problems such as image captioning | |
| \cite{captioning_msr, captioning_xinlei, captioning_berkeley, | |
| captioning_stanford, captioning_google, captioning_toronto, | |
| captioning_baidu_ucla} and visual question answering (VQA) | |
| \cite{VQA, fritz, Malinowski_2015_ICCV, baiduVQA, Ren_2015_NIPS} | |
| have gained popularity in recent years as the computer vision | |
| research community is progressing beyond ``bucketed'' recognition | |
| and towards solving multi-modal problems. | |
| \begin{figure}[t] | |
| \centering | |
| \includegraphics[width=1\linewidth]{teaser} | |
| \caption{Examples from our balanced VQA dataset.} | |
| \label{fig:dataset} | |
| \end{figure} | |
| The complex compositional structure of language makes problems | |
| at the intersection of vision and language challenging. | |
| But recent works \cite{Larry_NN_caption,binaryVQA,ZhouVQABaseline, | |
| FbVQA,Kushal_VQA,Aish_analyze} | |
| have pointed out that language also provides a strong prior | |
| that can result in good superficial performance, | |
| without the underlying models truly understanding the visual content. | |
| This phenomenon has been observed in image captioning \cite{Larry_NN_caption} | |
| as well as visual question answering \cite{binaryVQA,ZhouVQABaseline,FbVQA,Kushal_VQA,Aish_analyze}. | |
| For instance, in the VQA \cite{VQA} dataset, the most common sport answer | |
| ``tennis'' is the correct answer for 41\% of the questions starting with | |
| ``What sport is'', and ``2'' is the correct answer for 39\% of the questions | |
| starting with ``How many''. | |
| Moreover, Zhang~\etal~\cite{binaryVQA} points out a particular `visual priming bias' | |
| in the VQA dataset -- | |
| specifically, subjects saw an image while asking questions about it. | |
| Thus, people only ask the question ``Is there a clock tower in the picture?'' on images | |
| actually containing clock towers. | |
| As one particularly perverse example -- for questions in the VQA dataset | |
| starting with the n-gram ``Do you see a \ldots'', | |
| blindly answering ``yes'' without reading the rest of the question | |
| or looking at the associated image results in a VQA accuracy of $87\%$! | |
| These language priors can give a false impression that machines are making | |
| progress towards the goal of understanding images correctly when they are | |
| only exploiting language priors to achieve high accuracy. | |
| This can hinder progress in pushing state of art in the computer vision | |
| aspects of multi-modal AI \cite{DatasetBias,binaryVQA}. | |
| In this work, we propose to counter these language biases and elevate the | |
| role of image understanding in VQA. In order to accomplish this goal, | |
| we collect a balanced VQA dataset with significantly reduced language biases. | |
| Specifically, we create a balanced VQA dataset in the following way -- given | |
| an (image, question, answer) triplet $(I, Q, A)$ from the VQA dataset, | |
| we ask a human subject to identify an image $I'$ that is similar to $I$ | |
| but results in the answer to the question $Q$ to become $A'$ (which is different from $A$). | |
| Examples from our balanced dataset are shown in \figref{fig:dataset}. | |
| More random examples can be seen in \figref{fig:more_examples} and on the project website\footnote{\label{website}\url{http://visualqa.org/}}. | |
| Our hypothesis is that this balanced dataset will force VQA models to focus | |
| on visual information. | |
| After all, | |
| when a question $Q$ | |
| has two different answers ($A$ and $A'$) for two different images ($I$ and $I'$ respectively), the only way to know the | |
| right answer is by looking at the image. | |
| Language-only models have simply no basis for differentiating between the two cases -- | |
| $(Q, I)$ and $(Q, I')$, and by construction must get one wrong. | |
| We believe that this construction will also prevent language+vision models | |
| from achieving high accuracy by exploiting language priors, | |
| enabling VQA evaluation protocols to more accurately reflect progress in image understanding. | |
| Our balanced VQA dataset is also particularly difficult because | |
| the picked complementary image $I'$ is close to the original image $I$ in the | |
| semantic (fc7) space of VGGNet \cite{Simonyan15} features. Therefore, VQA models | |
| will need to understand the subtle differences between the two images to predict | |
| the answers to both the images correctly. | |
| Note that simply ensuring that the answer distribution $P(A)$ is uniform across the | |
| dataset would not accomplish the goal of alleviating language biases discussed above. | |
| This is because language models exploit the correlation between question n-grams | |
| and the answers, \eg questions starting with ``Is there a clock'' has the | |
| answer ``yes'' 98\% of the time, and questions starting with ``Is the man standing'' | |
| has the answer ``no'' 69\% of the time. | |
| What we need is not just higher entropy in $P(A)$ across the dataset, | |
| but higher entropy in $P(A|Q)$ so that image $I$ must play a role | |
| in determining $A$. This motivates our balancing on a per-question level. | |
| Our complete balanced dataset contains approximately \emph{1.1 Million} | |
| (image, question) pairs -- almost \emph{double} the size of the VQA \cite{VQA} dataset -- | |
| with approximately \emph{13 Million} associated answers on the $\sim$200k images | |
| from COCO~\cite{coco}. | |
| We believe this balanced VQA dataset is a better dataset | |
| to benchmark VQA approaches, and is publicly available for download on the project website. | |
| Finally, our data collection protocol enables us to develop a counter-example based | |
| explanation modality. We propose a novel model that not only answers questions | |
| about images, but also `explains' its answer to an image-question pair by providing | |
| ``hard negatives'' \ie, examples of images that it believes are similar to the image at hand, | |
| but it believes have different answers to the question. Such an explanation modality will | |
| allow users of the VQA model to establish greater trust in the model and | |
| identify its oncoming failures. | |
| Our main contributions are as follows: | |
| (1) We balance the existing VQA dataset \cite{VQA} by collecting complementary images | |
| such that almost every question | |
| in our balanced dataset is associated with not just a single image, but | |
| rather a pair of similar images that result in two different answers to the question. | |
| The result is a more balanced VQA dataset, which is also approximately twice the size | |
| of the original VQA dataset. | |
| (2) We evaluate state-of-art VQA models (with publicly available code) on our | |
| balanced dataset, and show that models trained on the existing `unbalanced' | |
| VQA dataset perform poorly on our new balanced dataset. This finding confirms | |
| our hypothesis that these models have been exploiting language priors in the | |
| existing VQA dataset to achieve higher accuracy. | |
| (3) | |
| Finally, our data collection protocol for identifying | |
| complementary scenes enables us to develop a novel interpretable model, | |
| which in addition to answering questions about images, also provides a | |
| counter-example based explanation -- it retrieves images that it believes are | |
| similar to the original image but have different answers to the question. | |
| Such explanations can help in building trust for machines among their users. | |
| \section{Related Work} | |
| \label{sec:related_work} | |
| \begin{figure*} | |
| \begin{tabular}{cccc} | |
| \includegraphics[width = 0.22\linewidth]{ex_1.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_2.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_33.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_4.jpg}\\ | |
| \includegraphics[width = 0.22\linewidth]{ex_5.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_6.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_7.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_8.jpg}\\ | |
| \includegraphics[width = 0.22\linewidth]{ex_35.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_10.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_36.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_12.jpg}\\ | |
| \includegraphics[width = 0.22\linewidth]{ex_13.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_14.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_15.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_16.jpg}\\ | |
| \includegraphics[width = 0.22\linewidth]{ex_17.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_18.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_19.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_20.jpg}\\ | |
| \includegraphics[width = 0.22\linewidth]{ex_21.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_22.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_23.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_24.jpg}\\ | |
| \includegraphics[width = 0.22\linewidth]{ex_25.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_26.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_27.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_28.jpg}\\ | |
| \includegraphics[width = 0.22\linewidth]{ex_29.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_30.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_31.jpg} & | |
| \includegraphics[width = 0.22\linewidth]{ex_32.jpg}\\ | |
| \end{tabular} | |
| \caption{Random examples from our proposed balanced VQA dataset. Each question has two similar images with different answers to the question.} | |
| \label{fig:more_examples} | |
| \end{figure*} | |
| \textbf{Visual Question Answering.} | |
| A number of recent works have proposed visual question answering datasets | |
| \cite{VQA, VisualGenome, fritz, Ren_2015_NIPS, baiduVQA, Madlibs, MovieQA,fsvqa} and | |
| models \cite{MCB, HieCoAtt, NeuralModuleNetworks, dynamic_memory_net_vision, Lu2015, Malinowski_2015_ICCV, binaryVQA,StackedAttentionNetworks,saenko,wang,shih,kim,han,Ilievski,wu,saito,kanan_kafle}. | |
| Our work builds on top of the VQA dataset from Antol~\etal\cite{VQA}, | |
| which is one of the most widely used VQA datasets. | |
| We reduce the language biases present in this popular dataset, | |
| resulting in a dataset that is more balanced and about twice the size of the VQA dataset. | |
| We benchmark one `baseline' VQA model \cite{Lu2015}, | |
| one attention-based VQA model \cite{HieCoAtt}, | |
| and the winning model from the VQA Real Open Ended Challenge 2016 | |
| \cite{MCB} on our balanced VQA dataset, and compare them to a language-only model. | |
| \textbf{Data Balancing and Augmentation.} | |
| At a high level, our work may be viewed as constructing a more rigorous evaluation | |
| protocol by collecting `hard negatives'. In that spirit, it is similar to the | |
| work of Hodosh~\etal~\cite{julia_cap_bal}, who | |
| created a binary forced-choice image captioning task, where a machine must | |
| choose to caption an image with one of two similar captions. | |
| To compare, Hodosh~\etal~\cite{julia_cap_bal} implemented hand-designed rules | |
| to create two similar captions for images, | |
| while we create a novel annotation interface to collect two similar images for questions | |
| in VQA. | |
| Perhaps the most relevant to our work is that of Zhang~\etal~\cite{binaryVQA}, | |
| who study this goal of balancing VQA in a fairly restricted setting -- | |
| binary (yes/no) questions on abstract scenes made from clipart | |
| (part of the VQA abstract scenes dataset \cite{VQA}). | |
| Using clipart allows Zhang~\etal to ask human annotators to ``change the clipart scene | |
| such that the answer to the question changes''. Unfortunately, such fine-grained editing | |
| of image content is simply not possible in real images. | |
| The novelty of our work over Zhang~\etal is | |
| the proposed complementary image data collection interface, application to real images, | |
| extension to \emph{all} questions (not just binary ones), | |
| benchmarking of state-of-art VQA models on the balanced dataset, | |
| and finally the novel VQA model with counter-example based explanations. | |
| \textbf{Models with explanation.} | |
| A number of recent works have proposed mechanisms for generating `explanations' | |
| \cite{generating_visual_explanations, grad-cam,cam, VQA_interpretability_ICML, sameersingh} | |
| for the predictions made by deep learning models, which are typically `black-box' | |
| and non-interpretable. | |
| \cite{generating_visual_explanations} generates a natural language explanation | |
| (sentence) for image categories. | |
| \cite{grad-cam, cam, VQA_interpretability_ICML, sameersingh} provide `visual | |
| explanations' or spatial maps overlaid on images to highlight the regions that | |
| the model focused on while making its predictions. | |
| In this work, we introduce a third explanation modality: counter-examples, | |
| instances the the model believes are close to but not belonging to the category | |
| predicted by the model. | |
| \section{Dataset} | |
| \label{sec:dataset} | |
| We build on top of the VQA dataset introduced by Antol~\etal~\cite{VQA}. | |
| VQA real images dataset contains just over 204K images from COCO \cite{coco}, | |
| 614K free-form natural language questions (3 questions per image), | |
| and over 6 million free-form (but concise) answers (10 answers per question). | |
| While this dataset has spurred significant progress in VQA domain, | |
| as discussed earlier, it has strong language biases. | |
| \begin{figure}[t] | |
| \centering | |
| \includegraphics[width=1\linewidth]{interface.jpg} | |
| \caption{A snapshot of our Amazon Mechanical Turk (AMT) interface to collect complementary images.} | |
| \label{fig:interface} | |
| \end{figure} | |
| Our key idea to counter this language bias is the following -- | |
| for every (image, question, answer) triplet $(I,Q,A)$ in the VQA dataset, | |
| our goal is to identify an image $I'$ that is similar to $I$, | |
| but results in the answer to the question $Q$ to become $A'$ | |
| (which is different from $A$). | |
| We built an annotation interface (shown in \figref{fig:interface}) | |
| to collect such complementary images on Amazon Mechanical Turk (AMT). | |
| AMT workers are shown 24 nearest-neighbor images of $I$, | |
| the question $Q$, and the answer $A$, and asked to pick an image $I'$ from | |
| the list of 24 images for which $Q$ ``makes sense'' | |
| and the answer to $Q$ is \emph{not} $A$. | |
| To capture ``question makes sense'', | |
| we explained to the workers (and conducted qualification | |
| tests to make sure that they understood) that any premise assumed in the | |
| question must hold true for the image they select. | |
| For instance, the question ``What is the woman doing?'' assumes that a woman is present | |
| and can be seen in the image. It does not make sense to ask this question on | |
| an image without a woman visible in it. | |
| We compute the 24 nearest neighbors by first representing each image with the | |
| activations from the penultimate (`fc7') layer of a deep Convolutional | |
| Neural Network (CNN) -- in particular VGGNet \cite{Simonyan15} -- and then | |
| using $\ell_2$-distances to compute neighbors. | |
| After the complementary images are collected, we conduct a second round | |
| of data annotation to collect answers on these new images. Specifically, | |
| we show the picked image $I'$ with the question $Q$ to 10 new AMT workers, | |
| and collect 10 ground truth answers (similar to \cite{VQA}). | |
| The most common answer among the 10 is the new answer $A'$. | |
| \begin{figure*}[t] | |
| \centering | |
| \bf{Answers from unbalanced dataset}\par\medskip | |
| \includegraphics[width=\linewidth,height=6cm]{only_old.jpg} | |
| \bf{Answers from balanced dataset}\par\medskip | |
| \includegraphics[width=\linewidth,height=6cm]{all_balanced.jpg} | |
| \caption{Distribution of answers per question type for a random sample of 60K questions from the original (unbalanced) VQA dataset \cite{VQA} (top) and from our proposed balanced dataset (bottom).} | |
| \label{fig:bal_answers} | |
| \end{figure*} | |
| This two-stage data collection process finally results in pairs of complementary | |
| images $I$ and $I'$ that are semantically similar, but have different answers | |
| $A$ and $A'$ respectively to the same question $Q$. Since $I$ and $I'$ are | |
| semantically similar, a VQA model will have to understand the subtle differences | |
| between $I$ and $I'$ to provide the right answer to both images. | |
| Example complementary images are shown in \figref{fig:dataset}, \figref{fig:more_examples}, and on the project website. | |
| Note that sometimes it may not be \emph{possible} to pick one of the 24 neighbors as a complementary image. | |
| This is because either (1) the question does not make sense for any of the 24 images | |
| (\eg the question is `what is the woman doing?' and none of the neighboring images | |
| contain a woman), or (2) the question is applicable to some neighboring images, but | |
| the answer to the question is still $A$ (same as the original image $I$). | |
| In such cases, our data collection interface allowed AMT | |
| workers to select ``not possible''. | |
| We analyzed the data annotated with ``not possible'' selection by AMT workers | |
| and found that this typically happens when | |
| (1) the object being talked about in | |
| the question is too small in the original image and thus the nearest neighbor | |
| images, while globally similar, do not necessarily contain the object resulting in the question not making sense, or | |
| (2) when the concept in the question is rare (\eg, when workers are | |
| asked to pick an image such that the answer to the question | |
| ``What color is the banana?'' is NOT ``yellow''). | |
| In total, such ``not possible'' selections make up 22\% of all the questions in the VQA dataset. | |
| We believe that a more sophisticated interface that allowed workers to | |
| scroll through many more than 24 neighboring images could possibly reduce this fraction. | |
| But, | |
| (1) it will likely still not be 0 (there may be no image in COCO where the | |
| answer to ``is the woman flying?'' is NOT ``no''), and | |
| (2) the task would be significantly more cumbersome for workers, | |
| making the data collection significantly more expensive. | |
| We collected complementary images and | |
| the corresponding new answers for all of train, val and test splits | |
| of the VQA dataset. | |
| AMT workers picked ``not possible'' for approximately 135K total | |
| questions. | |
| In total, we collected approximately 195K complementary images for train, 93K complementary images for val, and 191K complementary images for test set. | |
| In addition, we augment the test set with $\sim$18K additional (question, image) pairs | |
| to provide additional means to detect anomalous trends on the test data. | |
| Hence, our complete balanced dataset contains more than 443K train, 214K val and 453K test (question, image) pairs. | |
| Following original VQA dataset \cite{VQA}, we divide our test set into 4 splits: test-dev, test-standard, test-challenge and test-reserve. For more details, please refer to \cite{VQA}. | |
| Our complete balanced dataset is publicly available for download. | |
| We use the publicly released VQA evaluation script in our experiments. | |
| The evaluation metric uses 10 ground-truth answers for each question to | |
| compute VQA accuracies. | |
| As described above, we collected 10 answers for every complementary image | |
| and its corresponding question to be consistent with the VQA dataset \cite{VQA}. | |
| Note that while unlikely, it is possible that the majority vote of the 10 new | |
| answers may not match the intended answer of the person picking the image | |
| either due to inter-human disagreement, or if the worker selecting the complementary | |
| image simply made a mistake. | |
| We find this to be the case -- \ie, $A$ to be the same as $A'$ -- | |
| for about 9\% of our questions. | |
| \figref{fig:bal_answers} compares the distribution of answers per question-type | |
| in our new balanced VQA dataset with the original (unbalanced) VQA dataset \cite{VQA}. We notice several interesting trends. | |
| First, binary questions | |
| (\eg ``is the'', ``is this'', ``is there'', ``are'', ``does'') have a | |
| \emph{significantly} more balanced distribution over ``yes'' and ``no'' answers | |
| in our balanced dataset compared to unbalanced VQA dataset. | |
| ``baseball'' is now slightly more popular than ``tennis'' under ``what sport'', | |
| and more importantly, overall ``baseball'' and ``tennis'' dominate less in the | |
| answer distribution. Several other sports like ``frisbee'', ``skiing'', | |
| ``soccer'', ``skateboarding'', ``snowboard'' and ``surfing'' are more visible | |
| in the answer distribution in the balanced dataset, suggesting that it contains | |
| heavier tails. | |
| Similar trends can be seen across the board with colors, animals, numbers, \etc. | |
| Quantitatively, we find that the entropy of answer distributions | |
| averaged across various question types (weighted by frequency of | |
| question types) increases by 56\% after balancing, confirming the heavier tails | |
| in the answer distribution. | |
| As the statistics show, while our balanced dataset is not perfectly balanced, it is \emph{significantly} more balanced than the original VQA dataset. | |
| The resultant impact of this balancing on performance of | |
| state-of-the-art VQA models is discussed in the next section. | |
| \section{Benchmarking Existing VQA Models} | |
| \label{sec:benchmark} | |
| Our first approach to training a VQA model that emphasizes the | |
| visual information over language-priors-alone is to re-train | |
| the existing state-of-art VQA models (with code publicly available | |
| \cite{Lu2015,HieCoAtt,MCB}) | |
| on our new balanced VQA dataset. | |
| Our hypothesis is that simply training a model to | |
| answer questions correctly on our balanced | |
| dataset will already encourage the model to focus more on the visual signal, | |
| since the language signal alone has been impoverished. | |
| We experiment with the following models: | |
| \textbf{Deeper LSTM Question + norm Image (d-LSTM+n-I)} \cite{Lu2015}: | |
| This was the VQA model introduced in \cite{VQA} together | |
| with the dataset. It uses a CNN embedding of the image, | |
| a Long-Short Term Memory (LSTM) embedding of the question, | |
| combines these two embeddings via a point-wise multiplication, | |
| followed by a multi-layer perceptron classifier to predict a probability | |
| distribution over $1000$ most frequent answers in the training dataset. | |
| \textbf{Hierarchical Co-attention (HieCoAtt)} \cite{HieCoAtt}: | |
| This is a recent attention-based VQA model that `co-attends' to both the image | |
| and the question | |
| to predict an answer. Specifically, it models | |
| the question (and consequently the image via the co-attention mechanism) | |
| in a hierarchical fashion: at the word-level, phrase-level and entire question-level. | |
| These levels are combined recursively to produce a distribution over the $1000$ most frequent answers. | |
| \textbf{Multimodal Compact Bilinear Pooling (MCB)} \cite{MCB}: This is | |
| the winning entry on the real images track of the VQA Challenge 2016. This model | |
| uses a multimodal compact bilinear pooling mechanism to attend over image features | |
| and combine the attended image features with language features. These combined | |
| features are then passed through a fully-connected layer to predict a | |
| probability distribution over the $3000$ most frequent answers. | |
| It should be noted that MCB uses image features from a more powerful CNN architecture ResNet \cite{ResNet} while the previous two models use image features from VGGNet \cite{Simonyan15}. | |
| \textbf{Baselines:} To put the accuracies of these models in perspective, | |
| we compare to the following baselines: | |
| \textbf{Prior:} Predicting the most common answer in the training set, | |
| for all test questions. | |
| The most common answer is ``yes'' in both the unbalanced and balanced sets. | |
| \textbf{Language-only:} | |
| This language-only baseline has a similar architecture as Deeper LSTM Question + norm Image \cite{Lu2015} except that it only accepts the question as input and | |
| does not utilize any visual information. | |
| Comparing VQA models to language-only ablations quantifies to what | |
| extent VQA models have succeeded in leveraging the image to answer the | |
| questions. | |
| The results are shown in Table~\ref{tab:imbalanced}. | |
| For fair comparison of accuracies with original (unbalanced) dataset, we create a balanced train set which is of similar size as original dataset (referred to as B$_\text{half}$ in table). | |
| For benchmarking, we also report results using the full balanced train set. | |
| \begin{table}[h] | |
| \setlength{\tabcolsep}{10pt} | |
| {\small | |
| \begin{center} | |
| \begin{tabular}{@{}lcccc@{}} | |
| \toprule | |
| Approach & UU & UB & B$_\text{half}$B & BB\\ | |
| \midrule | |
| Prior & 27.38 & 24.04 & 24.04 & 24.04\\ | |
| Language-only & 48.21 & 41.40 & 41.47 & 43.01\\ | |
| d-LSTM+n-I \cite{Lu2015}& 54.40 & 47.56 & 49.23 & 51.62\\ | |
| HieCoAtt \cite{HieCoAtt} & 57.09 & 50.31 & 51.88 & 54.57\\ | |
| MCB \cite{MCB} & 60.36 & 54.22 & 56.08 & 59.14\\ | |
| \bottomrule | |
| \end{tabular} | |
| \end{center} | |
| } | |
| \caption {Performance of VQA models when trained/tested on unbalanced/balanced VQA datasets. UB stands for training on \textbf{U}nbalanced train and testing on \textbf{B}alanced val datasets. UU, B$_\text{half}$B and BB are defined analogously. | |
| } | |
| \label{tab:imbalanced} | |
| \end{table} | |
| We see that the current state-of-art VQA models trained on the original | |
| (unbalanced) VQA dataset perform significantly worse when evaluated on our | |
| balanced dataset, compared to evaluating on the original unbalanced VQA dataset | |
| (\ie, comparing UU to UB in the table). This finding confirms our hypothesis | |
| that existing models have learned severe language biases present in the dataset, resulting | |
| in a reduced ability to answer questions correctly when the same question has | |
| different answers on different images. When these models are trained on our | |
| balanced dataset, their performance improves (compare UB to B$_\text{half}$B in the table). | |
| Further, when models are trained on complete balanced dataset ($\sim$twice the size of original dataset), the accuracy improves by 2-3\% (compare B$_\text{half}$B to BB). | |
| This increase in accuracy suggests that current VQA models are data starved, and would benefit from even larger VQA datasets. | |
| As the absolute numbers in the table suggest, there is significant room | |
| for improvement in building visual understanding models that can extract | |
| detailed information from images and leverage this information to answer | |
| free-form natural language questions about images accurately. As expected from the construction of this balanced dataset, | |
| the question-only approach performs \emph{significantly} worse on the | |
| balanced dataset compared to the unbalanced dataset, again confirming | |
| the language-bias in the original VQA dataset, | |
| and its successful alleviation (though not elimination) in our proposed | |
| balanced dataset. | |
| Note that in addition to the lack of language bias, visual reasoning is | |
| also challenging on the balanced dataset since there are pairs of images | |
| very similar to each other in image representations learned by CNNs, but | |
| with different answers to the same question. To be successful, VQA models | |
| need to understand the subtle differences in these images. | |
| The paired construction of our dataset allows us to analyze the performance of | |
| VQA models in unique ways. Given the prediction of a VQA model, | |
| we can count the number of questions where \emph{both} | |
| complementary images ($I$,$I'$) received correct answer predictions for the | |
| corresponding question $Q$, | |
| or both received identical (correct or incorrect) answer predictions, | |
| or both received different answer predictions. | |
| For the HieCoAtt \cite{HieCoAtt} model, when trained on the unbalanced dataset, | |
| 13.5\% of the pairs were answered correctly, | |
| 59.9\% of the pairs had identical predictions, | |
| and 40.1\% of the pairs had different predictions. | |
| In comparison, when trained on balanced dataset, the same model | |
| answered 17.7\% of the pairs correctly, a 4.2\% increase in performance! | |
| Moreover, it predicts identical answers for 10.5\% fewer pairs (49.4\%). | |
| This shows that by training on balanced dataset, this VQA model has | |
| learned to tell the difference between two otherwise similar images. However, significant room for improvement remains. The VQA model still can not tell the difference between two images that have a noticeable difference -- a difference enough to result in the two images having different ground truth answers for the same question asked by humans. | |
| To benchmark models on VQA v2.0 dataset, we also train these models on VQA v2.0 train+val and report results on VQA v2.0 test-standard in Table~\ref{tab:test_results}. | |
| Papers reporting results on VQA v2.0 dataset are suggested to report test-standard accuracies and compare their methods' accuracies with accuracies reported in Table~\ref{tab:test_results}. | |
| \begin{table}[h] | |
| \setlength{\tabcolsep}{10pt} | |
| {\small | |
| \begin{center} | |
| \begin{tabular}{@{}lcccc@{}} | |
| \toprule | |
| Approach & All & Yes/No & Number & Other\\ | |
| \midrule | |
| Prior & 25.98 & 61.20 & 00.36 & 01.17\\ | |
| Language-only & 44.26 & 67.01 & 31.55 & 27.37\\ | |
| d-LSTM+n-I \cite{Lu2015}& 54.22 & 73.46 & 35.18 & 41.83\\ | |
| MCB \cite{MCB} & 62.27 & 78.82 & 38.28 & 53.36\\ | |
| \bottomrule | |
| \end{tabular} | |
| \end{center} | |
| } | |
| \caption {Performance of VQA models when trained on VQA v2.0 train+val and tested on VQA v2.0 test-standard dataset. | |
| } | |
| \label{tab:test_results} | |
| \end{table} | |
| \textbf{Analysis of Accuracies for Different Answer Types:}\\ | |
| We further analyze the accuracy breakdown over answer types | |
| for Multimodal Compact Bilinear Pooling (MCB) \cite{MCB} | |
| and Hierarchical Co-attention (HieCoAtt) \cite{HieCoAtt} models. | |
| \begin{table}[h] | |
| \setlength{\tabcolsep}{6.8pt} | |
| {\small | |
| \begin{center} | |
| \begin{tabular}{@{}llcccc@{}} | |
| \toprule | |
| Approach & Ans Type & UU & UB & B$_\text{half}$B & BB \\ | |
| \midrule | |
| \multirow{4}{*}{MCB \cite{MCB}} & Yes/No & 81.20 & 70.40 & 74.89 & 77.37\\ | |
| &Number & 34.80 & 31.61 & 34.69 & 36.66 \\ | |
| &Other& 51.19 & 47.90 & 47.43 & 51.23 \\ | |
| &All & 60.36 & 54.22 & 56.08 & 59.14\\ | |
| \midrule | |
| \multirow{4}{*}{HieCoAtt \cite{HieCoAtt}} & Yes/No & 79.99 & 67.62 & 70.93 & 71.80\\ | |
| &Number & 34.83 & 32.12 & 34.07 & 36.53 \\ | |
| &Other& 45.55 & 41.96 & 42.11 & 46.25 \\ | |
| &All & 57.09 & 50.31 & 51.88 & 54.57 \\ | |
| \bottomrule | |
| \end{tabular} | |
| \end{center} | |
| } | |
| \caption { | |
| Accuracy breakdown over answer types achieved by MCB \cite{MCB} and HieCoAtt \cite{HieCoAtt} models | |
| when trained/tested on unbalanced/balanced VQA datasets. | |
| UB stands for training on \textbf{U}nbalanced train and testing on \textbf{B}alanced val datasets. UU, B$_\text{half}$B and BB are defined analogously. | |
| } | |
| \label{tab:MCB_anstype} | |
| \end{table} | |
| The results are shown in \tableref{tab:MCB_anstype}. First, we immediately notice that the accuracy for the answer-type ``yes/no'' drops significantly from UU to UB | |
| ($\sim$$10.8\%$ for MCB and $\sim$$12.4\%$ for HieCoAtt). This suggests that these VQA | |
| models are really exploiting language biases for ``yes/no'' type questions, which leads to high | |
| accuracy on unbalanced val set because the unbalanced val set also contains these biases. | |
| But performance drops significantly when tested on the balanced val set which has significantly reduced biases. | |
| Second, we note that for both the state-of-art VQA models, the largest source of improvement | |
| from UB | |
| to B$_\text{half}$B | |
| is the ``yes/no'' answer-type ($\sim$$4.5\%$ for MCB and $\sim$$3\%$ for HieCoAtt) and | |
| the ``number'' answer-type ($\sim$$3\%$ for MCB and $\sim$$2\%$ for HieCoAtt). | |
| This trend is particularly interesting since the ``yes/no'' and ``number'' answer-types are the ones | |
| where existing approaches have shown minimal improvements. For instance, in the results announced | |
| at the VQA Real Open Ended Challenge 2016\footnote{\url{http://visualqa.org/challenge.html}}, | |
| the accuracy gap between the top-4 approaches is a mere $0.15\%$ in ``yes/no'' answer-type category | |
| (and a gap of $3.48\%$ among the top-10 approaches). | |
| Similarly, ``number'' answer-type accuracies only vary by $1.51\%$ and $2.64\%$ respectively. | |
| The primary differences between current generation of state-of-art approaches seem to come from the | |
| ``other'' answer-type where accuracies vary by $7.03\%$ and $10.58\%$ among the top-4 and top-10 entries. | |
| This finding suggests that language priors present in the unbalanced VQA dataset | |
| (particularly in the ``yes/no'' and ``number'' answer-type questions) lead to similar accuracies for all state-of-art VQA models, | |
| rendering vastly different models virtually indistinguishable from each other (in terms of their accuracies for these answer-types). | |
| Benchmarking these different VQA models on our balanced dataset (with reduced language priors) | |
| may finally allow us to distinguish between `good' models (ones that encode the `right' inductive biases for this task, | |
| such as attention-based or compositional models) from others that are simply high-capacity models tuning themselves to the biases in the dataset. | |
| \section{Counter-example Explanations} | |
| \label{sec:explanation} | |
| We propose a new explanation modality: counter-examples. We propose a model | |
| that when asked a question about an image, | |
| not only provides an answer, | |
| but also provides example images that are similar to the input image but the model | |
| believes have different answers to the input question. | |
| This would instill trust in the user that the model does in fact | |
| `understand' the concept being asked about. | |
| For instance, for a question ``What color is the fire-hydrant?'' | |
| a VQA model may be perceived as more trustworthy | |
| if in addition to saying ``red'', it also adds ``unlike this'' | |
| and shows an example image containing a fire-hydrant | |
| that is not red.\footnote{It could easily also convey what color it thinks | |
| the fire-hydrant is in the counter-example. | |
| We will explore this in future work.} | |
| \subsection{Model} | |
| Concretely, at test time, our ``negative explanation'' | |
| or ``counter-example explanation'' model functions in two steps. | |
| In the first step, similar to a conventional VQA model, | |
| it takes in an (image, question) pair $(Q, I)$ as input and predicts | |
| an answer $A_{pred}$. | |
| In the second step, it uses this predicted answer $A_{pred}$ along with | |
| the question $Q$ to retrieve an image that is similar to $I$ but has a | |
| different answer than $A_{pred}$ to the question $Q$. | |
| To ensure similarity, the model picks one of $K$ nearest neighbor images of $I$, | |
| $I_{NN}=$ \{$I_1$, $I_2$, ..., $I_{K}$\} as the counter-example. | |
| How may we find these ``negative explanations''? | |
| One way of picking the counter-example from $I_{NN}$ is to follow the | |
| classical ``hard negative mining'' strategy popular in computer vision. | |
| Specifically, simply pick the | |
| image that has the lowest $P(A_{pred}|Q,I_i)$ where $i \in {1, 2, ..., K}$. | |
| We compare to this strong baseline. | |
| While this ensures that $P(A_{pred}|Q,I_i)$ is low for $I_i$, | |
| it does not ensure that the $Q$ ``makes sense'' for $I_i$. | |
| Thus, when trying to find a negative explanation for | |
| ``Q: What is the woman doing? A: Playing tennis'', this ``hard negative mining'' | |
| strategy might pick an image without a woman in it, which would make | |
| for a confusing and non-meaningful explanation to show to a user, if the goal | |
| is to convince them that the model has understood the question. | |
| One could add a component of | |
| question relevance \cite{ray2016question} to identify better counter-examples. | |
| Instead, we take advantage of our balanced data collection mechanism | |
| to directly train for identifying a good counter-example. Note that the | |
| $I'$ picked by humans is a good counter-example, by definition. | |
| $Q$ is relevant to $I'$ (since workers were asked to ensure it was), | |
| $I'$ has a different answer $A'$ than $A$ (the original answer), | |
| and $I'$ is similar to $I$. Thus, we have supervised training data where | |
| $I'$ is a counter-example from $I_{NN}$ ($K$ = 24) for question $Q$ | |
| and answer $A$. | |
| We train a model that learns to provide negative or counter-example | |
| explanations from this supervised data. | |
| To summarize, during test time, our model does two things: first it answers | |
| the question (similar to a conventional VQA model), and second, it explains | |
| its answer via a counter-example. For the first step, it is given as input | |
| an image $I$ and a question $Q$, and it outputs a predicted answer $A_{pred}$. | |
| For the second (explaining) step, it is given as input the question $Q$, | |
| an answer to be explained $A$\footnote{In practice, this answer to be explained | |
| would be the answer predicted by the first step $A_{pred}$. However, we only have access to | |
| negative explanation annotations from humans for the ground-truth answer $A$ to the question. | |
| Providing $A$ to the explanation module also helps in evaluating the two steps of | |
| answering and explaining separately.}, | |
| \emph{and} a set $I_{NN}$ from which the model has to identify the counter-example. | |
| At training time, the model is given image $I$, | |
| the question $Q$, and the corresponding ground-truth answer $A$ | |
| to learn to answer questions. | |
| It is also given $Q$, $A$, $I'$ (human-picked), $I_{NN}$ ($I' \in I_{NN}$) | |
| to learn to explain. | |
| Our model architecture contains two heads on top of a shared base `trunk' | |
| -- one head for answering the question and the other head for providing | |
| an explanation. | |
| Specifically, our model consists of three major components: | |
| \vspace{5pt} | |
| \noindent \textbf{1. Shared base}: | |
| The first component of our model is learning representations of images and questions. It is a 2-channel | |
| network that takes in an image CNN embedding as input in one branch, | |
| question LSTM embedding as input in another branch, | |
| and combines the two embeddings by a point-wise multiplication. | |
| This gives us a joint $QI$ embedding, similar to the model in \cite{Lu2015}. | |
| The second and third components -- the answering model and the explaining model | |
| -- take in this joint $QI$ embedding as input, | |
| and therefore can be considered as two heads over this first shared component. | |
| A total of 25 images -- the original image $I$ and 24 candidate images | |
| \{$I_1$, $I_2$, ..., $I_{24}$\} are passed through this shared | |
| component of the network. | |
| \vspace{5pt} | |
| \noindent \textbf{2. Answering head:} The second component is learning to | |
| answer questions. Similar to \cite{Lu2015}, it consists of a fully-connected | |
| layer fed into a softmax that predicts the probability distribution | |
| over answers given the $QI$ embedding. | |
| Only the $QI$ embedding corresponding to the original image $I$ | |
| is passed through this component and result in a cross-entropy loss. | |
| \vspace{5pt} | |
| \noindent \textbf{3. Explaining head:} The third component is learning to | |
| explain an answer $A$ | |
| via a counter-example image. It is a 2-channel network which | |
| linearly transforms the joint $QI$ embedding (output from the first component) | |
| and the answer to be explained $A$ (provided as input)\footnote{Note that in theory, one \emph{could} provide $A_{pred}$ as input | |
| during training instead of $A$. After all, this matches the expected use | |
| case scenario at test time. | |
| However, this alternate setup (where $A_{pred}$ is provided as input instead of $A$) | |
| leads to a peculiar and unnatural explanation training goal -- specifically, | |
| the explanation head will \emph{still be} learning to explain $A$ | |
| since that is the answer for which we collected negative explanation human annotations. | |
| It is simply unnatural to build that model that answers a question with | |
| $A_{pred}$ but learn to explain a different answer $A$! | |
| Note that this is an interesting scenario where the current push towards | |
| ``end-to-end'' training for everything breaks down. | |
| } | |
| into a common | |
| embedding space. It computes an inner product of these 2 embeddings | |
| resulting in a scalar number for each image in $I_{NN}$ | |
| (also provided as input, from which a counter-example is to be picked). | |
| These $K$ inner-product values for $K$ candidate images are then passed | |
| through a fully connected layer to generate $K$ scores $S(I_i)$, where | |
| $i \in \{1, 2, ..., K\}$. The $K$ candidate images \{$I_1$, $I_2$, ..., $I_{K}$\} | |
| are then sorted according to these scores $S(I_i)$ as being most to least | |
| likely of being good counter-examples or negative explanations. | |
| This component is trained with pairwise hinge ranking losses that | |
| encourage $S(I') - S(I_i) > M - \eps, \quad I_i \in \{I_1, I_2, ..., I_{K}\} \setminus \{I'\}$, | |
| \ie the score of the human picked image $I'$ is encouraged to be higher | |
| than all other candidate images by a desired margin of $M$ (a hyperparameter) | |
| and a slack of $\eps$. This is of course the classical `constraint form' | |
| of the pairwise hinge ranking loss, and we minimize the standard expression | |
| $\max\Big(0, M - \big(S(I') - S(I_i)\big)\Big)$. | |
| The combined loss function for the shared component is | |
| \begin{multline} | |
| \mathcal{L} = -\log P(A|I,Q) \\+ \lambda \sum_{i} | |
| \max\Big(0, M - \big(S(I') - S(I_i)\big)\Big) | |
| \end{multline} | |
| where, | |
| the first term is the cross-entropy loss (for training the answering module) | |
| on $(I,Q)$, | |
| the second term is the sum of pairwise hinge losses that encourage the | |
| explaining model to give high score to image $I'$ (picked by humans) | |
| than other $I_i$s in $I_{NN}$, and | |
| $\lambda$ is the trade-off weight parameter between the two losses. | |
| \subsection{Results} | |
| \begin{figure}[t] | |
| \centering | |
| \includegraphics[width=1\linewidth]{negative_explanation.jpg} | |
| \caption{Three counter-example or negative explanations (right three columns) generated by our model, along with the input image (left), the input question $Q$ and the predicted answer $A$. | |
| } | |
| \label{fig:example_negative_explanations} | |
| \end{figure} | |
| \figref{fig:example_negative_explanations} shows qualitative | |
| examples of negative explanations produced by our model. We see the original | |
| image $I$, the question asked $Q$, the answer $A_{pred}$ predicted by | |
| the VQA head in our model, and top three negative explanations produced | |
| by the explanation head. We see that most of these explanations are sensible | |
| and reasonable -- the images are similar to $I$ but with answers that | |
| are different from those predicted for $I$. | |
| For quantitative evaluation, we compare our model with a number of baselines: | |
| \textbf{Random:} Sorting the candidate images in $I_{NN}$ randomly. | |
| That is, a random image from $I_{NN}$ is picked as the most likely | |
| counter-example. | |
| \textbf{Distance:} Sorting the candidate images in increasing order | |
| of their distance from the original image $I$. That is, the image | |
| from $I_{NN}$ most similar to $I$ is picked as the most likely counter-example. | |
| \textbf{VQA Model:} Using a VQA model's probability for the predicted | |
| answer to sort the candidate images in \emph{ascending} order of $P(A|Q,I_i)$. | |
| That is, the image from $I_{NN}$ \emph{least likely} to have $A$ as the | |
| answer to $Q$ is picked as the \emph{most likely} counter-example. | |
| Note that while $I'$ -- the image picked by humans -- is a good counter-example, | |
| it is not necessarily the unique (or even the ``best'') counter-example. | |
| Humans were simply asked to pick any image where $Q$ makes sense and the | |
| answer is not $A$. There was no natural criteria to convey to humans to | |
| pick the ``best'' one -- it is not clear what ``best'' would mean in the | |
| first place. To provide robustness to this potential ambiguity in the | |
| counter-example chosen by humans, in a manner similar to the | |
| ImageNet \cite{ImageNet} top-5 evaluation metric, we evaluate our approach using the | |
| Recall@5 metric. It measures how often the human picked $I'$ is among | |
| the top-5 in the sorted list of $I_i$s in $I_{NN}$ our model produces. | |
| \begin{table}[h] | |
| \setlength{\tabcolsep}{10pt} | |
| {\small | |
| \begin{center} | |
| \begin{tabular}{@{}lcccc@{}} | |
| \toprule | |
| & Random & Distance & VQA \cite{VQA} & Ours \\ | |
| \midrule | |
| Recall@5 & 20.79 & 42.84 & 21.65 & 43.39 \\ | |
| \bottomrule | |
| \end{tabular} | |
| \end{center} | |
| } | |
| \caption {Negative or counter-example explanation performance of our model compared to strong baselines.} | |
| \label{tab:explanation_results} | |
| \end{table} | |
| In \tableref{tab:explanation_results}, | |
| we can see that our explanation model significantly outperforms | |
| the random baseline, as well as the VQA \cite{VQA} model. Interestingly, | |
| the strongest baseline is Distance. While our approach outperforms it, | |
| it is clear that identifying an image that is a counter-example to | |
| $I$ from among $I$'s nearest neighbors is a challenging task. | |
| Again, this suggests that visual understanding models that can extract | |
| meaningful details from images still remain elusive. | |
| \section{Conclusion} | |
| \label{sec:conclusion} | |
| To summarize, in this paper we address the strong language priors | |
| for the task of Visual Question Answering and elevate the role of image understanding | |
| required to be successful on this task. | |
| We develop a novel data-collection interface to | |
| `balance' the popular VQA dataset~\cite{VQA} by collecting `complementary' images. For every question in the dataset, we have two complementary images that look similar, but have different answers to the question. | |
| This effort results in a dataset that is not only more balanced than the original VQA dataset | |
| by construction, but also is about twice the size. | |
| We find both qualitatively and quantitatively | |
| that the `tails' of the answer distribution are heavier in this balanced dataset, | |
| which reduces the strong language priors that may be exploited by models. | |
| Our complete balanced dataset is publicly available at \url{http://visualqa.org/} as part of the 2nd iteration of the Visual Question Answering Dataset and Challenge (VQA v2.0). | |
| We benchmark a number of (near) state-of-art VQA models | |
| on our balanced dataset and find that | |
| testing them on this balanced dataset results in a significant drop in performance, | |
| confirming our hypothesis that these models | |
| had indeed exploited language biases. | |
| Finally, our framework around complementary images | |
| enables us to develop a novel explainable model -- when asked a question about an image, our | |
| model not only returns an answer, but also produces a list of similar images that it considers | |
| `counter-examples', \ie where the answer is not the same as the predicted response. | |
| Producing such explanations may enable a user to build a better mental model of | |
| what the system considers a response to mean, and ultimately build trust. | |
| { | |
| \small | |
| \textbf{Acknowledgements.} | |
| We thank Anitha Kannan and Aishwarya Agrawal for helpful discussions. | |
| This work was funded in part by NSF CAREER awards to DP | |
| and DB, an ONR YIP award to DP, ONR Grant N00014-14-1-0679 to DB, a Sloan Fellowship to DP, ARO YIP awards | |
| to DB and DP, an Allen Distinguished Investigator award | |
| to DP from the Paul G. Allen Family Foundation, ICTAS | |
| Junior Faculty awards to DB and DP, Google Faculty Research | |
| Awards to DP and DB, Amazon Academic Research | |
| Awards to DP and DB, AWS in Education Research grant | |
| to DB, and NVIDIA GPU donations to DB. The views and | |
| conclusions contained herein are those of the authors and | |
| should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, | |
| of the U.S. Government, or any sponsor. | |
| } | |
| {\small | |
| \bibliographystyle{ieee} | |
| \bibliography{references} | |
| } | |
| \end{document} | |