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. Crowdsourcing has attracted significant attention from the database community in recent years and several crowdsourced databases have been proposed to incorporate human power into traditional database systems. One big issue in crowdsourcing is to achieve high quality because workers may return incorrect answers. A typical solution to address this problem is to assign each question to multiple workers and combine workers’ answers to generate the final result. One big challenge arising in this strategy is to infer worker’s quality. Existing methods usually assume each worker has a fixed quality and compute the quality using qualification tests or historical performance. However these methods cannot accurately estimate a worker’s quality. To address this problem, we propose a worker model and devise an incremental inference strategy to accurately compute the workers’ quality. We also propose a question model and develop two efficient strategies to combine the worker’s model to compute the question’s result. We implement our method and compare with existing inference approaches on real crowdsourcing platforms using real-world datasets, and the experiments indicate that our method achieves high accuracy and outperforms existing approaches. To ensure quality results from crowdsourced tasks, requesters often aggregate worker responses and use one of a plethora of strategies to infer the correct answer from the set of noisy responses. However, all current models assume prior knowledge of all possible outcomes of the task. While not an unreasonable assumption for tasks that can be posited as multiple-choice questions (e.g. n-ary classification), we observe that many tasks do not naturally fit this paradigm, but instead demand a free-response formulation where the outcome space is of infinite size (e.g. audio transcription). We model such tasks with a novel probabilistic graphical model, and design and implement LazySusan, a decision-theoretic controller that dynamically requests responses as necessary in order to infer answers to these tasks. We also design an EM algorithm to jointly learn the parameters of our model while inferring the correct answers to multiple tasks at a time. Live experiments on Amazon Mechanical Turk demonstrate the superiority of LazySusan at solving SAT Math questions, eliminating 83.2 of the error and achieving greater net utility compared to the state-ofthe-art strategy, majority-voting. We also show in live experiments that our EM algorithm outperforms majority-voting on a visualization task that we design. Large-scale annotated datasets allow AI systems to learn from and build upon the knowledge of the crowd. Many crowdsourcing techniques have been developed for collecting image annotations. These techniques often implicitly rely on the fact that a new input image takes a negligible amount of time to perceive. In contrast, we investigate and determine the most cost-effective way of obtaining high-quality multi-label annotations for temporal data such as videos. Watching even a short 30-second video clip requires a significant time investment from a crowd worker; thus, requesting multiple annotations following a single viewing is an important cost-saving strategy. But how many questions should we ask per video? We conclude that the optimal strategy is to ask as many questions as possible in a HIT (up to 52 binary questions after watching a 30-second video clip in our experiments). We demonstrate that while workers may not correctly answer all questions, the cost-benefit analysis nevertheless favors consensus from multiple such cheap-yet-imperfect iterations over more complex alternatives. When compared with a one-question-per-video baseline, our method is able to achieve a 10 improvement in recall (76.7 ours versus 66.7 baseline) at comparable precision (83.8 ours versus 83.0 baseline) in about half the annotation time (3.8 minutes ours compared to 7.1 minutes baseline). We demonstrate the effectiveness of our method by collecting multi-label annotations of 157 human activities on 1,815 videos.