Add to ORKGWe study strategies for scalable multi-label annotation, or for efficiently acquiring multiple labels from humans for a col-lection of items. We propose an algorithm that exploits corre-lation, hierarchy, and sparsity of the label distribution. A case study of labeling 200 objects using 20,000 images demon-strates the effectiveness of our approach. The algorithm re-sults in up to 6x reduction in human computation time com-pared to the naı̈ve method of querying a human annotator for the presence of every object in every image
10.1145/1873951.1873959MM'10 - Proceedings of the ACM Multimedia 2010 International Conference35-4
Automatic annotation of images with descriptive words is a challenging problem with vast application...
In this paper, we propose a novel approach of image annotation by constructing a hierarchical mappin...
Abstract—This paper presents a novel multi-label classification framework for domains with large num...
This paper presents a novel multi-label classification framework for domains with large numbers of l...
We introduce a method for efficiently crowdsourcing multiclass annotations in challenging, real worl...
Labeling large datasets has become faster, cheaper, and easier with the advent of crowdsourcing ser...
Distributing labeling tasks among hundreds or thousands of annotators is an increasingly important m...
The long-standing goal of localizing every object in an image remains elusive. Manually annotating o...
Image annotation tasks always lack accuracy and efficiency. Although many techniques that have been ...
International audienceLarge-scale annotated corpora have yielded impressive performance improvements...
In this paper we propose a novel biased random sampling strategy for image representation in Bag-of-...
It is very attractive to exploit weakly-labeled image dataset for multi-label annotation application...
It is very attractive to exploit weakly-labeled image dataset for multi-label annotation application...
This paper proposes a group annotation approach to interac-tive semantic labeling of data and demons...
10.1145/1873951.1873959MM'10 - Proceedings of the ACM Multimedia 2010 International Conference35-4
Automatic annotation of images with descriptive words is a challenging problem with vast application...
In this paper, we propose a novel approach of image annotation by constructing a hierarchical mappin...
Abstract—This paper presents a novel multi-label classification framework for domains with large num...
This paper presents a novel multi-label classification framework for domains with large numbers of l...
We introduce a method for efficiently crowdsourcing multiclass annotations in challenging, real worl...
Labeling large datasets has become faster, cheaper, and easier with the advent of crowdsourcing ser...
Distributing labeling tasks among hundreds or thousands of annotators is an increasingly important m...
The long-standing goal of localizing every object in an image remains elusive. Manually annotating o...
Image annotation tasks always lack accuracy and efficiency. Although many techniques that have been ...
International audienceLarge-scale annotated corpora have yielded impressive performance improvements...
In this paper we propose a novel biased random sampling strategy for image representation in Bag-of-...
It is very attractive to exploit weakly-labeled image dataset for multi-label annotation application...
It is very attractive to exploit weakly-labeled image dataset for multi-label annotation application...
This paper proposes a group annotation approach to interac-tive semantic labeling of data and demons...
10.1145/1873951.1873959MM'10 - Proceedings of the ACM Multimedia 2010 International Conference35-4
Automatic annotation of images with descriptive words is a challenging problem with vast application...
In this paper, we propose a novel approach of image annotation by constructing a hierarchical mappin...