Visual question answering (VQA) systems are emerging from a desire to empower users to ask any natural language question about visual content and receive a valid answer in response. However, close examination of the VQA problem reveals an unavoidable, entangled problem that multiple humans may or may not always agree on a single answer to a visual question. We train a model to automatically predict from a visual question whether a crowd would agree on a single answer. We then propose how to exploit this system in a novel application to efficiently allocate human effort to collect answers to visual questions. Specifically, we propose a crowdsourcing system that automatically solicits fewer human responses when answer agreement is expected and more human responses when answer disagreement is expected. Our system improves upon existing crowdsourcing systems, typically eliminating at least 20 of human effort with no loss to the information collected from the crowd.
* Answer Collection from a Crowd Our work relates to methods that propose how to employ crowd workers to answer questions about images. Such approaches aim to collect a pre-specified, fixed number of answers per visual question. For those systems that treat response time as a first priority, a variable number of answers may arise but this is due to varying crowdsourcing conditions such as the available supply of workers @cite_20 @cite_7 . Other systems ensure a fixed number of answers are collected per visual question @cite_6 @cite_1 . Unlike prior work, our goal is to collect answers in a way that is both economical complete in capturing the diversity of plausible answers for all visual questions. To our knowledge, our work is the first to predict the number of answers to collect for a visual question. Experiments demonstrate that our disagreement predictions are useful to significantly reduce human effort for capturing the diversity of valid answers for 121,512 visual questions.
@cite_1 @cite_7 @cite_20 @cite_6
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Visual question answering systems empower users to ask any question about any image and receive a valid answer. However, existing systems do not yet account for the fact that a visual question can lead to a single answer or multiple different answers. While a crowd often agrees, disagreements do arise for many reasons including that visual questions are ambiguous, subjective, or difficult. We propose a model, CrowdVerge, for automatically predicting from a visual question whether a crowd would agree on one answer. We then propose how to exploit these predictions in a novel application to efficiently collect all valid answers to visual questions. Specifically, we solicit fewer human responses when answer agreement is expected and more human responses otherwise. Experiments on 121,811 visual questions asked by sighted and blind people show that, compared to existing crowdsourcing systems, our system captures the same answer diversity with typically 14-23 less crowd involvement. Blind people face a range of accessibility challenges in their everyday lives, from reading the text on a package of food to traveling independently in a new place. Answering general questions about one's visual surroundings remains well beyond the capabilities of fully automated systems, but recent systems are showing the potential of engaging on-demand human workers (the crowd) to answer visual questions. The input to such systems has generally been a single image, which can limit the interaction with a worker to one question; or video streams where systems have paired the end user with a single worker, limiting the benefits of the crowd. In this paper, we introduce Chorus:View, a system that assists users over the course of longer interactions by engaging workers in a continuous conversation with the user about a video stream from the user's mobile device. We demonstrate the benefit of using multiple crowd workers instead of just one in terms of both latency and accuracy, then conduct a study with 10 blind users that shows Chorus:View answers common visual questions more quickly and accurately than existing approaches. We conclude with a discussion of users' feedback and potential future work on interactive crowd support of blind users. Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering. However, to tackle real-life question answering problems on multimedia collections such as personal photos, we have to look at whole collections with sequences of photos or videos. When answering questions from a large collection, a natural problem is to identify snippets to support the answer. In this paper, we describe a novel neural network called Focal Visual-Text Attention network (FVTA) for collective reasoning in visual question answering, where both visual and text sequence information such as images and text metadata are presented. FVTA introduces an end-to-end approach that makes use of a hierarchical process to dynamically determine what media and what time to focus on in the sequential data to answer the question. FVTA can not only answer the questions well but also provides the justifications which the system results are based upon to get the answers. FVTA achieves state-of-the-art performance on the MemexQA dataset and competitive results on the MovieQA dataset. The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings. We propose VizWiz, the first goal-oriented VQA dataset arising from a natural VQA setting. VizWiz consists of over 31,000 visual questions originating from blind people who each took a picture using a mobile phone and recorded a spoken question about it, together with 10 crowdsourced answers per visual question. VizWiz differs from the many existing VQA datasets because (1) images are captured by blind photographers and so are often poor quality, (2) questions are spoken and so are more conversational, and (3) often visual questions cannot be answered. Evaluation of modern algorithms for answering visual questions and deciding if a visual question is answerable reveals that VizWiz is a challenging dataset. We introduce this dataset to encourage a larger community to develop more generalized algorithms that can assist blind people.