Given a short query with keyphrases about a research idea in 2016 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: A system for predicting cross-instance correspondences between images depicting objects of the same category.

Keyphrases: cross-instance correspondence images

Dataset description:
DOTA: DOTA is a large-scale dataset for object detection in aerial images. It can be used to develop and evaluate object detectors in aerial images.
PASCAL3D+: The Pascal3D+ multi-view dataset consists of images in the wild, i.e., images of object categories exhibiting high variability, captured under uncontrolled settings, in cluttered scenes and under many different poses. Pascal3D+ contains 12 categories of rigid objects selected from the PASCAL VOC 2012 dataset.
iSAID: iSAID contains 655,451 object instances for 15 categories across 2,806 high-resolution images. The images of iSAID is the same as the DOTA-v1.0 dataset, which are manily collected from the Google Earth, some are taken by satellite JL-1, the others are taken by satellite GF-2 of the China Centre for Resources Satellite Data and Application.
COCO: The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.

Options:- A: DOTA  B: PASCAL3D+  C: iSAID  D: COCO
PASCAL3D+