4,563 research outputs found

    Crowdsourcing in Computer Vision

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    Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

    Accurate and budget-efficient text, image, and video analysis systems powered by the crowd

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    Crowdsourcing systems empower individuals and companies to outsource labor-intensive tasks that cannot currently be solved by automated methods and are expensive to tackle by domain experts. Crowdsourcing platforms are traditionally used to provide training labels for supervised machine learning algorithms. Crowdsourced tasks are distributed among internet workers who typically have a range of skills and knowledge, differing previous exposure to the task at hand, and biases that may influence their work. This inhomogeneity of the workforce makes the design of accurate and efficient crowdsourcing systems challenging. This dissertation presents solutions to improve existing crowdsourcing systems in terms of accuracy and efficiency. It explores crowdsourcing tasks in two application areas, political discourse and annotation of biomedical and everyday images. The first part of the dissertation investigates how workers' behavioral factors and their unfamiliarity with data can be leveraged by crowdsourcing systems to control quality. Through studies that involve familiar and unfamiliar image content, the thesis demonstrates the benefit of explicitly accounting for a worker's familiarity with the data when designing annotation systems powered by the crowd. The thesis next presents Crowd-O-Meter, a system that automatically predicts the vulnerability of crowd workers to believe \enquote{fake news} in text and video. The second part of the dissertation explores the reversed relationship between machine learning and crowdsourcing by incorporating machine learning techniques for quality control of crowdsourced end products. In particular, it investigates if machine learning can be used to improve the quality of crowdsourced results and also consider budget constraints. The thesis proposes an image analysis system called ICORD that utilizes behavioral cues of the crowd worker, augmented by automated evaluation of image features, to infer the quality of a worker-drawn outline of a cell in a microscope image dynamically. ICORD determines the need to seek additional annotations from other workers in a budget-efficient manner. Next, the thesis proposes a budget-efficient machine learning system that uses fewer workers to analyze easy-to-label data and more workers for data that require extra scrutiny. The system learns a mapping from data features to number of allocated crowd workers for two case studies, sentiment analysis of twitter messages and segmentation of biomedical images. Finally, the thesis uncovers the potential for design of hybrid crowd-algorithm methods by describing an interactive system for cell tracking in time-lapse microscopy videos, based on a prediction model that determines when automated cell tracking algorithms fail and human interaction is needed to ensure accurate tracking

    A survey of spatial crowdsourcing

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    Construction of a 3D Object Recognition and Manipulation Database from Grasp Demonstrations

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    Object recognition and manipulation are critical for enabling robots to operate within a household environment. There are many grasp planners that can estimate grasps based on object shape, but these approaches often perform poorly because they miss key information about non-visual object characteristics, such as weight distribution, fragility of materials, and usability characteristics. Object model databases can account for this information, but existing methods for constructing 3D object recognition databases are time and resource intensive, often requiring specialized equipment, and are therefore difficult to apply to robots in the field. We present an easy-to-use system for constructing object models for 3D object recognition and manipulation made possible by advances in web robotics. The database consists of point clouds generated using a novel iterative point cloud registration algorithm, which includes the encoding of manipulation data and usability characteristics. The system requires no additional equipment other than the robot itself, and non-expert users can demonstrate grasps through an intuitive web interface with virtually no training required. We validate the system with data collected from both a crowdsourcing user study and a set of grasps demonstrated by an expert user. We show that the crowdsourced grasps can produce successful autonomous grasps, and furthermore the demonstration approach outperforms purely vision-based grasp planning approaches for a wide variety of object classes

    Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing

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    Abstract With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely
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