Showing papers by "Jia Deng published in 2012"

Crowdsourcing annotations for visual object detection

Abstract: A large number of images with ground truth object bounding boxes are critical for learning object detectors, which is a fundamental task in compute vision. In this paper, we study strategies to crowd-source bounding box annotations. The core challenge of building such a system is to effectively control the data quality with minimal cost. Our key observation is that drawing a bounding box is significantly more difficult and time consuming than giving answers to multiple choice questions. Thus quality control through additional verification tasks is more cost effective than consensus based algorithms. In particular, we present a system that consists of three simple sub-tasks — a drawing task, a quality verification task and a coverage verification task. Experimental results demonstrate that our system is scalable, accurate, and cost-effective.

Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition

Abstract: As visual recognition scales up to ever larger numbers of categories, maintaining high accuracy is increasingly difficult. In this work, we study the problem of optimizing accuracy-specificity trade-offs in large scale recognition, motivated by the observation that object categories form a semantic hierarchy consisting of many levels of abstraction. A classifier can select the appropriate level, trading off specificity for accuracy in case of uncertainty. By optimizing this trade-off, we obtain classifiers that try to be as specific as possible while guaranteeing an arbitrarily high accuracy. We formulate the problem as maximizing information gain while ensuring a fixed, arbitrarily small error rate with a semantic hierarchy. We propose the Dual Accuracy Reward Trade-off Search (DARTS) algorithm and prove that, under practical conditions, it converges to an optimal solution. Experiments demonstrate the effectiveness of our algorithm on datasets ranging from 65 to over 10,000 categories.

Large scale visual recognition

Abstract: : Visual recognition remains one of the grand goals of artificial intelligence research. One major challenge is endowing machines with human ability to recognize tens of thousands of categories. Moving beyond previous work that is mostly focused on hundreds of categories we make progress toward human scale visual recognition. Specifically, our contributions are as follows First, we have constructed ImageNet, a large scale image ontology. The Fall 2011 version consists of 22 thousand categories and 14 million images; it depicts each category by an average of 650 images collected from the Internet and verified by multiple humans. To the best of our knowledge this is currently the largest human-verified dataset in terms of both the number of categories and the number of images. Given the large amount of human effort required, the traditional approach to dataset collection, involving in-house annotation by a small number of human subjects, becomes infeasible. In this dissertation we describe how ImageNet has been built through quality controlled, cost effective, large scale online crowdsourcing. Next, we use ImageNet to conduct the first benchmarking study of state of the art recognition algorithms at the human scale. By experimenting on 10 thousand categories, we discover that the previous state of the art performance is still low (6.4%). We further observe that the confusion among categories is hierarchically structured at large scale, a key insight that leads to our subsequent contributions. Third, we study how to efficiently classify tens of thousands of categories by exploiting the structure of visual confusion among categories. We propose a novel learning technique that scales logarithmically with the number of classes in both training and testing, improving both accuracy and efficiency of the previous state of the art while reducing training time by 31 fold on 10 thousand classes.