Devi Parikh — Page 8

arXiv2014

Collecting Image Description Datasets using Crowdsourcing

We describe our two new datasets with images described by humans. Both the datasets were collected using Amazon Mechanical Turk, a crowdsourcing platform. The two datasets contain significantly more descriptions per image than other existing datasets. One is based on a popular image description dataset called the UIUC Pascal Sentence Dataset, whereas the other is based on the Abstract Scenes dataset con- taining images made from clipart objects. In this paper we describe our interfaces, analyze some properties of and show example descriptions from our two datasets.

cs.CV↗

arXiv2014

Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding

Recent trends in image understanding have pushed for holistic scene understanding models that jointly reason about various tasks such as object detection, scene recognition, shape analysis, contextual reasoning, and local appearance based classifiers. In this work, we are interested in understanding the roles of these different tasks in improved scene understanding, in particular semantic segmentation, object detection and scene recognition. Towards this goal, we "plug-in" human subjects for each of the various components in a state-of-the-art conditional random field model. Comparisons among various hybrid human-machine CRFs give us indications of how much "head room" there is to improve scene understanding by focusing research efforts on various individual tasks.

cs.CV↗