A First Look: Towards Explainable T ext VQA Models via Visual and Textual Explanations (2021)

Abstract: Explainable deep learning models are advantageous in many situations. Prior work mostly provide unimodal explanations through post-hoc approaches not part of the original system design. Explanation mechanisms also ignore useful textual information present in images. In this paper, we propose MTXNet, an end-to-end trainable multimodal architecture to generate multimodal explanations, which focuses on the text in the image. We curate a novel dataset TextVQA-X, containing ground truth visual and multi-reference textual explanations that can be leveraged during both training and evaluation. We then quantitatively show that training with multimodal explanations complements model performance and surpasses unimodal baselines by up to 7% in CIDEr scores and 2% in IoU. More importantly, we demonstrate that the multimodal explanations are consistent with human interpretations, help justify the models’ decision, and provide useful insights to help diagnose an incorrect prediction. Finally, we describe a real-world e-commerce application for using the generated multimodal explanations.

## introduction the ability to explain decisions through voice, text and visual pointing, is inherently human. deep learning models on the other hand, are rather opaque black boxes that don't reveal very much about how they arrived at a specific prediction. recent research effort, aided by regulatory provisions such as gdprs "right to explanation" @xcite , have focused on peeking beneath the hood of these black boxes and designing systems that inherently enable explanation. explainable multimodal architectures can also be used to reduce the effort required for manual compliance ¶ product assurance, risk, and security https://www.amazon.jobs/en/teams/ product-assurance-risk-security figure 1 : sample ground truth labels checks of products sold by online retailers. further, explanations can be provided as evidence to justify decisions and help improve customer and seller partner experiences. we choose the textvqa task proposed by @xcite for realizing the system, motivated by two reasons. first, the task is multimodal and is naturally suited for generating multimodal explanations. second, the task specifically focuses on the text in the image, known to encode essential information for scene understanding and reasoning @xcite , and allows for better quality of explanations including the text recognized. several approaches have been proposed for the textvqa task @xcite @xcite @xcite ), but they do not include a means for explaining the model decision. in addition to allowing humans to interpret the model's decision, we believe the explanations can also provide valuable insight into what component could be improved. most prior explanation approaches @xcite @xcite have been unimodal and do not focus on the text in the image. only recently, @xcite and @xcite generated multimodal explanations for the vqa and activity recognition tasks. they curated datasets (vqa-x, act-x) consisting of single reference ground truth textual explanations and relied on implicit attention-based visual expla-nations without any access to labeled visual ground truth. however, their models cannot read and incorporate text in the image into the explanations. in addition, it is debatable whether attention mechanisms are indeed explanations @xcite . moreover, other works @xcite have shown that current vqa attention models do not seem to look at the same regions as humans, resulting in inconsistent explanations. the goal of our work is two-fold. first, to collect a multimodal explanations dataset (textvqa-x) thereby highlighting the need to curate datasets where explanations are not post-hoc but part of the initial interpretable model design. non posthoc explanations which may not be faithful to the model decision but are in line with human explanations are still beneficial to end users. figure 1 provides a representative example. second, to implement a multimodal explanation system that has the ability to not only read and reason about the text in the image, but more importantly justify its decision with natural language and visually highlight the evidence, useful to even non-experts @xcite . the explanations and model decision must be tightly coupled and mutually influence each other through an end-to-end trainable architecture. in summary, our contributions are as follows: the raw dataset is available publicly foot_0 . (section 3) • we propose the first end-to-end trainable mtxnet architecture that produces high quality textual and visual explanations, focusing on the text in the image. (section 4) • qualitative and quantitative results show that textual and visual explanations help justify a model's decision and help diagnose the reasons for an incorrect prediction. (section 5) • we describe a real-world e-commerce system that can leverage the multimodal explanations and also highlight its challenges. (section 6) explanations. prior explanation approaches @xcite @xcite @xcite @xcite focus on parts of the input that is relevant to the model's decision, but not on explicitly generating explanations as model predictions. hendricks et al. (2016, 2018) were the first to generate natural language justifications for image classifiers. unlike our model however, explanations are unimodal and there are no reference human explanations. closer to our objective huk park et al. (2018) generate multimodal explanations and curate a new vqa-x dataset. wu and mooney (2019) extend their work to ensure explanations can be traced back to an object ensuring local faithfulness. however, their explanations do not contain the text in the image. they use implicit attention for visual explanations and have no access to visual ground truth during training. further, they use a single textual explanation reference during training. in contrast, our work incorporates multimodal explanations which focuses on the text in the image. ## textvqa-x dataset to train and evaluate multimodal explanation models that focus on the text in the image, we collect the textvqa-x dataset by human annotation of a subset of samples from the textvqa dataset @xcite . ## multimodal text-in-image explanation network (mtxnet) we design our multimodal text-in-image explanation network (mtxnet) to allow for end-to-end multitask training of answer prediction, text generation and semantic segmentation extending the m4c model proposed in @xcite . in the subsequent subsections we describe each of the individual components in more detail. ## experiments in this section, we detail the experimental setup, present quantitative results with ablations and finally analyze qualitative results. ## applications to e-commerce businesses e-commerce businesses need to comply with industry-wide, and country-specific regulations, to provide accurate and useful information of products to improve customer experience that leads to more business. our long-term goal with explainable multimodal architectures is to automate and reduce manual effort required for compliance and product detail checks. this will enable businesses to scale compliance and customer experience improvement efficiently without linear increases in cost. further, these architectures help validate if models are performing as intended and used for the right purposes. a potential customer experience issue arises when the physical product in a warehouse is different from that uploaded by a seller on the product details page. a possible reason could be that the seller or manufacturer labeled the product erroneously when they packaged it. many sellers taking advantage of lower cost of manufacturing in a global supply chain, may not be able to audit every batch of product leaving the factory. such discrepancies will almost certainly lead to product returns, because the customer didn't get what they wanted and increases costs. such discrepancies may also be due to more nefarious reasons, such as opportunistic bad actors taking advantage of sellers that have successful products by introducing poorer quality or mismatched offers at a lower price to unsuspecting customers. examples of compliance issues include detecting products that contain batteries and chemicals to comply with transportation and logistics regulations, as well as identifying products that require additional safety documentation and checks, such as products that may have unintended use by children (e.g. toys and products that may end up as toys should not have heavy metals or other poisons that cause illness or death when accidentally ingested). while not all answers can be obtained with product images alone, manual investigation processes utilize these images to identify potential risks that warrant additional steps in the process (e.g. lab testing). rather than manually auditing products in a warehouse, product images can be automatically captured at scale, and passed through models that detect such discrepancies. with the help of subject matter experts, attributes such as quantity, color and brand names, and other common misleading attributes are identified apriori. relevant questions that target these attributes are formulated. the image and question are then inputs to a multimodal explainable system (such as mtxnet) that can provide an answer and justify its prediction through multimodal explanations. answers can then be compared against the information extracted from the product detail pages on the website. any discrepancies found can be noted and a selling partner can be provided evidence through the multimodal explanations to take corrective steps. an example use-case is as follows. given a large container of cereal, with smaller boxes within, a potential question is: "how many cereal boxes are within the container?" . this information is usually written on the larger container present in the warehouse and can be answered based on reading the text in the image. if there is any discrepancy encountered in the number of boxes of cereal in the warehouse and that listed on the website, appropriate action can be taken. other similar questions include: "how heavy is the product?", "is the chair red?", "does the item contain allergens?", and "did the product pass the lead test?". the challenges with the use of such explainable systems are two-fold. first, since there can be multiple stakeholders with diverse expertise and expectations, we need to clearly define the level of abstraction at which they interact with the system. for instance, while a scientist can use the explanations to improve the model, a business operations associate may use the explanations to identify and audit product discrepancies. second, we need fine grained evaluation methodologies and metrics that take into account the stakeholders as well. ## conclusion a central tenet of explainable ai is to create a suite of tools and frameworks that result in explainable models without sacrificing learning performance and allow humans to understand and trust ai models. as @xcite argues, for explainable ai to succeed, we should draw upon existing principles and create strategies that are more people-centric. unfortunately most prior explanation approaches have been post-hoc, unimodal, ignore text present in the image and not always in accordance with human interpretation. further, there is a paucity of labeled multimodal explanation datasets. the research presented in this paper shows that existing textvqa systems can be rather easily adapted to produce multimodal explanations that focus on the text in the image when given access to ground truth annotations. we curate the textvqa-x dataset consisting of visual and textual explanations. we then present a novel end-to-end trainable architecture, mtxnet, that generates multimodal explanations focusing on the text in the image, in line with human interpretation and surpasses unimodal baselines (7% in cider scores and 2% in iou) while complimenting model performance. we also show how the system may be applicable in the e-commerce space to reduce effort for manual audit of compliance checks and improve customer experience. results of this research open the door to design of explanainable models part of the original system design that effectively takes advantage of available ground truth multimodal explanation annotations. future work involves incorporating visual features as part of the transformer architecture.