Despite significant progress in Visual Question Answering over the years, robustness of today's VQA models leave much to be desired. We introduce a new evaluation protocol and associated dataset (VQA-Rephrasings) and show that state-of-the-art VQA models are notoriously brittle to linguistic variations in questions. VQA-Rephrasings contains 3 human-provided rephrasings for 40k questions spanning 40k images from the VQA v2.0 validation dataset. As a step towards improving robustness of VQA models, we propose a model-agnostic framework that exploits cycle consistency. Specifically, we train a model to not only answer a question, but also generate a question conditioned on the answer, such that the answer predicted for the generated question is the same as the ground truth answer to the original question. Without the use of additional annotations, we show that our approach is significantly more robust to linguistic variations than state-of-the-art VQA models, when evaluated on the VQA-Rephrasings dataset. In addition, our approach outperforms state-of-the-art approaches on the standard VQA and Visual Question Generation tasks on the challenging VQA v2.0 dataset.
Robustness of VQA models has been studied in several contexts @cite_27 @cite_33 @cite_50 . For example, @cite_27 studies the robustness of VQA models to changes in the answer distributions across training and test settings; @cite_7 analyzes the extent of visual grounding in VQA models by studying robustness of VQA models to meaningful semantic changes in images; @cite_6 shows that despite the use of an advanced attention mechanism, it is easy to fool a VQA model with very minor changes in the image. Our work, however, aims to complete the study in robustness by benchmarking and improving robustness of VQA models to linguistic and compositional variations in questions in the form of rephrasings. Robustness has also been studied in natural language processing (NLP) systems @cite_5 @cite_3 in contexts of bias @cite_51 @cite_35 , domain-shift @cite_44 and syntactic variations @cite_15 . To counter these issues in NLP systems, solutions like linguistically motivated data-augmentation @cite_44 and adversarial training @cite_15 have been proposed. We study this in the context of visual question answering which is a multi-modal task which grounds language into the visual world.
@cite_35 @cite_33 @cite_7 @cite_6 @cite_3 @cite_44 @cite_27 @cite_50 @cite_5 @cite_15 @cite_51
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The complex compositional structure of language makes problems at the intersection of vision and language challenging. But language also provides a strong prior that can result in good superficial performance, without the underlying models truly understanding the visual content. This can hinder progress in pushing state of art in the computer vision aspects of multi-modal AI. In this paper, we address binary Visual Question Answering (VQA) on abstract scenes. We formulate this problem as visual verification of concepts inquired in the questions. Specifically, we convert the question to a tuple that concisely summarizes the visual concept to be detected in the image. If the concept can be found in the image, the answer to the question is "yes", and otherwise "no". Abstract scenes play two roles (1) They allow us to focus on the highlevel semantics of the VQA task as opposed to the low-level recognition problems, and perhaps more importantly, (2) They provide us the modality to balance the dataset such that language priors are controlled, and the role of vision is essential. In particular, we collect fine-grained pairs of scenes for every question, such that the answer to the question is "yes" for one scene, and "no" for the other for the exact same question. Indeed, language priors alone do not perform better than chance on our balanced dataset. Moreover, our proposed approach matches the performance of a state-of-the-art VQA approach on the unbalanced dataset, and outperforms it on the balanced dataset. Adversarial attacks are known to succeed on classifiers, but it has been an open question whether more complex vision systems are vulnerable. In this paper, we study adversarial examples for vision and language models, which incorporate natural language understanding and complex structures such as attention, localization, and modular architectures. In particular, we investigate attacks on a dense captioning model and on two visual question answering (VQA) models. Our evaluation shows that we can generate adversarial examples with a high success rate (i.e., > 90 ) for these models. Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks. These observations will inform future work towards building effective defenses. This paper presents a summary of the first Workshop on Building Linguistically Generalizable Natural Language Processing Systems, and the associated Build It Break It, The Language Edition shared task. The goal of this workshop was to bring together researchers in NLP and linguistics with a shared task aimed at testing the generalizability of NLP systems beyond the distributions of their training data. We describe the motivation, setup, and participation of the shared task, provide discussion of some highlighted results, and discuss lessons learned. We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples. Given a sentence and a target syntactic form (e.g., a constituency parse), SCPNs are trained to produce a paraphrase of the sentence with the desired syntax. We show it is possible to create training data for this task by first doing backtranslation at a very large scale, and then using a parser to label the syntactic transformations that naturally occur during this process. Such data allows us to train a neural encoder-decoder model with extra inputs to specify the target syntax. A combination of automated and human evaluations show that SCPNs generate paraphrases that follow their target specifications without decreasing paraphrase quality when compared to baseline (uncontrolled) paraphrase systems. Furthermore, they are more capable of generating syntactically adversarial examples that both (1) "fool" pretrained models and (2) improve the robustness of these models to syntactic variation when used to augment their training data. Practical natural language understanding systems used to be concerned with very small miniature domains only: They knew exactly what potential text might be about, and what kind of sentence structures to expect. This optimistic assumption is no longer feasible if NLU is to scale up to deal with text that naturally occurs in the "real world". The key issue is robustness: The system needs to be prepared for cases where the input data does not correspond to the expectations encoded in the grammar. In this paper, we survey the approaches towards the robustness problem that have been developed throughout the last decade. We inspect techniques to overcome both syntactically and semantically ill-formed input in sentence parsing and then look briefly into more recent ideas concerning the extraction of information from texts, and the related question of the role that linguistic research plays in this game. Finally, the robust sentence parsing schemes are classified on a more abstract level of analysis.