MUTANT : A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering (2020)

Abstract: While progress has been made on the visual question answering leaderboards, models often utilize spurious correlations and priors in datasets under the i.i.d. setting. As such, evaluation on out-of-distribution (OOD) test samples has emerged as a proxy for generalization. In this paper, we present MUTANT , a training paradigm that exposes the model to perceptually similar, yet semantically distinct mutations of the input, to improve OOD generalization, such as the VQA-CP challenge. Under this paradigm, models utilize a consistency-constrained training objective to understand the effect of semantic changes in input (question-image pair) on the output (answer). Unlike existing methods on VQA-CP, MUTANT does not rely on the knowledge about the nature of train and test answer distributions. MUTANT establishes a new state-of-the-art accuracy on VQA-CP with a 10.57% improvement. Our work opens up avenues for the use of semantic input mutations for OOD generalization in question answering.

## introduction availability of large-scale datasets has enabled the use of statistical machine learning in vision and language understanding, and has lead to significant advances. however, the commonly used evaluation criterion is the performance of models on test-samples drawn from the same distribution as the training dataset, which cannot be a measure of generalization. training under this "independent and identically distributed" (i.i.d.) setting can drive decision making to be highly influenced by dataset biases and spurious correlations as shown in both natural language inference @xcite @xcite and visual question answering @xcite ., * equal contribution what is the color of the frisbee? what is the child doing? @xmath0; @xcite . as such, evaluation on out-of-distribution (ood) samples has emerged as a metric for generalization. visual question answering (vqa) @xcite is a task at the crucial intersection of vision and language. the aim of vqa models is to provide an answer, given an input image and a question about it. large datasets @xcite have been extensively used for developing vqa models. however over-reliance on datasets can cause models to learn spurious correlations such as linguistic priors @xcite that are specific to certain datasets and do not generalize to "out-of-distribution" (ood) samples, as shown in figure 1 . while learning patterns in the data is important, learning dataset-specific spurious correlations is not a feature of robust vqa models. developing robust models has thus become a key pursuit for recent work in visual question answer-ing through data augmentation @xcite , reorganization @xcite . every dataset contains biases; indeed inductive bias is necessary for machine learning algorithms to work. @xcite states that an unbiased learner's ability to classify is no better than a lookup from memory. however this bias has a component which is useful for generalization (positive bias), and a component due to spurious correlations (negative bias). we use the term "positive bias" to denote the correlations that are necessary to perform a task -for instance, the answer to a "what sport is . . . " question is correlated to a name of a sport. the term "negative bias" is used for spurious correlations tat may be learned from the data -for instance, always predicting "tennis" as the answer to "what sport. . . " questions. the goal of ood generalization is to mitigate negative bias while learning to perform the task. however existing methods such as lmh @xcite try to remove all biases between question-answer pairs, by penalizing examples that can be answered without looking at the image; we believe this to be counterproductive. the analogy of antibiotics which are designed to remove pathogen bacteria, but also end up removing useful gut microbiome @xcite is useful to understand this phenomenon. we present a method that focuses on increasing positive bias and mitigating negative bias, to address the problem of ood generalization in visual question answering. our approach is to enable the mutation of inputs (questions and images) in order to expose the vqa model to perceptually similar yet semantically dissimilar samples. the intuition is to implicitly allow the model to understand the critical changes in the input which lead to a change in the answer. this concept of mutations is illustrated in figure 1 . if the color of the frisbee is changed, or the child removed, i.e. when an image-mutation is performed, the answer to the question changes. similarly, if a word is substituted by an adversarial word (@xmath1, an antonym, or negation (@xmath2, i.e. when a question-mutation is performed, the answer also changes. notice that both mutations do not significantly change the input, most of the pixels in the image and words in the question are unchanged, and the type of reasoning required to answer the question is unchanged. however the mutation significantly changes the answer. in this work, we use this concept of mutations to enable models to focus on parts of the input that are critical to the answering process, by training our models to produce answers that are consistent with such mutations. we present a question-type exposure framework which teaches the model that although such linguistic priors may exist in training data (such as the dominant answer "tennis" to "what sport is ..." questions), other sports can also be answers to such questions, thus mitigating negative bias. this is in contrast to @xcite who focus on using data augmentation as a means for mitigating language bias. our method uses a pair-wise training protocol to ensure consistency between answer predictions for the original sample and the mutant sample. our model includes a projection layer, which projects cross-modal features and true answers to a learned manifold and uses noise-contrastive estimation loss @xcite for minimizing the distance between these two vectors. our results establish a new state-of-the-art accuracy of 69.52% on the vqa-cp-v2 benchmark outperforming the current best models by 10.57%. at the same time, our models achieves the best accuracy (70.24%) on vqa-vqa-v2 among models designed for the vqa-cp task. this work takes a step away from explicit debiasing as a method for ood generalization and instead proposes amplification of positive bias and implicit attenuation of spurious correlations as the objective. our contributions are as follow. ## mutant we consider the open-ended vqa problem as a multi-class classification problem. the vqa dataset @xmath3, i i , a i } n @xmath4, and answers a @xmath5. many contemporary vqa models such as up-dn @xcite and lxmert @xcite first extract cross-modal features from the image and question using attention layers, and then use these features as inputs to a neural network answering module which predicts the answer classes. in this section we define our mutant paradigm under this formulation of the vqa task. ## generating input mutations for vqa in order to train vqa models under the mutant paradigm, we need a mechanism to create mutant samples. mutations are transformations that act on semantic entities in either the image or the question, in ways that can reliably lead to a new answer. for the question, semantic entities are words, while for images, semantic entities are objects. it is important to note that our mutation process is automated and does not use the knowledge about the test set distribution in order to create new samples. in this section, we delineate our automated generation process for both image and question-mutation. ## discussion and conclusion in this paper, we present a method that uses input mutations to train vqa models with the goal of out-of-distribution generalization. our novel answer projection module trained for minimizing distance between answer and input projections complements the canonical vqa classification task. our type exposure model allows our network to consider all valid answers per question type as equally probable answer candidates, thus moving away from the negative question-answer linguistic priors. coupled with pairwise consistency, these modules achieve a new state-of-the-art accuracy on the vqa-cp-v2 dataset and reduce the gap between model performance on vqa-v2 data. we differentiate our work from methods using random adversarial perturbations for robust learning @xcite . instead we view input mutations as structured perturbations which lead to a semantic change in the input space and a deterministic change in the output space. we envision that the concept of input mutations can be extended to other vision and language tasks for robustness. concurrent work in the domain of image classification shows that carefully designed perturbations or manipulations of the input can benefit generalization and lead to performance improve-ments @xcite . while perception is a cornerstone of understanding, the ability to imagine changes in the scene or language query, and predict outputs for that imagined input allows models to supplement "what" decision making (based on observed inputs) with "what if" decision making (based on imagined inputs). the mutant paradigm is an effort towards "what if" decision making. code is available here. b.1 selection of objects for each vqa sample, a list of words w is created, which contains words from the ground-truth answers and the question. all nouns in w are converted to their singular form. for yes-no questions, numeric questions, and questions about colors of objects, a list of objects o is obtained from coco. background and crowd objects are filtered out from o. from o critical objects o c and and non-critical objects o n c are obtained. critical objects are those objects in the image that when manipulated or removed, may change the answer to the question being asked. for this we follow a simple heuristic that states that if an object-word or it's synonym or hyponym is present in w , then it is a critical object. then a critical object @xmath6, and m instances of this object are chosen at random. the polygon annotations (a polygon border) for this object are obtained from the coco dataset as shown in figure 4 . using these annotations, either a removal or color-inversion operation is applied to create the mutant image.