text: 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.
  @cite_9: We describe a question answering model that applies to both images and structured knowledge bases. The model uses natural language strings to automatically assemble neural networks from a collection of composable modules. Parameters for these modules are learned jointly with network-assembly parameters via reinforcement learning, with only (world, question, answer) triples as supervision. Our approach, which we term a dynamic neural model network, achieves state-of-the-art results on benchmark datasets in both visual and structured domains.
  @cite_20: 
  @cite_6: We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.
  @cite_15: We propose a method for automatically answering questions about images by bringing together recent advances from natural language processing and computer vision. We combine discrete reasoning with uncertain predictions by a multi-world approach that represents uncertainty about the perceived world in a bayesian framework. Our approach can handle human questions of high complexity about realistic scenes and replies with range of answer like counts, object classes, instances and lists of them. The system is directly trained from question-answer pairs. We establish a first benchmark for this task that can be seen as a modern attempt at a visual turing test.
summary: * Visual Question Answering Services Researchers spanning communities as diverse as human computer interaction, machine learning, computational linguistics, and computer vision have proposed a variety of ways to answer questions about images @cite_9 @cite_6 @cite_20 @cite_15 . Yet a commonality across these communities is they adopt a one-size-fits-all approach when deciding the number of answers for any visual question. For example, crowd-powered systems aim to supply a pre-specified, fixed number of answers per visual question @cite_20 and automated systems return a single answer for every visual question @cite_9 @cite_6 @cite_15 . Inspired by the observation that there can be multiple plausible answers per visual question, we propose a richer representation of visual question answering that accounts for whether different people would agree on a single answer. We propose a system that automatically predicts whether humans will disagree. We demonstrate the predictive advantage of our system over relying on the uncertainty of a VQA algorithm in its predicted answer @cite_6 .