Karthik Suresh

TL;DR: A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.

TL;DR: This work proposes to utilize free meta-data in conjunction with associated UAV images to learn domain-robust features via an adversarial training framework dubbed Nuisance Disentangled Feature Transform (NDFT), for the specific challenging problem of object detection in Uav images, achieving a substantial gain in robustness to those nuisances.

TL;DR: In this paper, the authors proposed to utilize free meta-data in conjunction with associated UAV images to learn domain-robust features via an adversarial training framework dubbed Nuisance Disentangled Feature Transform (NDFT), achieving a substantial gain in robustness to those nuisances.

TL;DR: In this article , the authors introduce CoST, a new multilingual Code Snippet Translation dataset that contains parallel data from 7 commonly used programming languages, which provides much more fine-grained alignments between different languages than the existing translation datasets.

TL;DR: This paper introduces XLCoST, Cross-L ingual Co de S nippe T dataset, a new benchmark dataset for cross-lingual code intelligence, which is the largest parallel dataset for source code both in terms of size and the number of languages.