Omkar2118/datasets huggingface dataset:
DUC 2006
Full name: Document Understanding Conferences

There is currently much interest and activity aimed at building powerful multi-purpose information systems. The agencies involved include DARPA, ARDA and NIST. Their programmes, for example DARPA's TIDES (Translingual Information Detection Extraction and Summarization) programme, ARDA's Advanced Question & Answering Program and NIST's TREC (Text Retrieval Conferences) programme cover a range of subprogrammes. These focus on different tasks requiring their own evaluation designs.

Within TIDES and among other researchers interested in document understanding, a group grew up which has been focusing on summarization and the evaluation of summarization systems. Part of the initial evaluation for TIDES called for a workshop to be held in the fall of 2000 to explore different ways of summarizing a common set of documents. Additionally a road mapping effort was started in March of 2000 to lay plans for a long-term evaluation effort in summarization.

Out of the initial workshop and the roadmapping effort has grown a continuing evaluation in the area of text summarization called the Document Understanding Conferences (DUC). Sponsored by the Advanced Research and Development Activity (ARDA), the conference series is run by the National Institute of Standards and Technology (NIST) to further progress in summarization and enable researchers to participate in large-scale experiments.
DUC 2007
Full name: Document Understanding Conferences

There is currently much interest and activity aimed at building powerful multi-purpose information systems. The agencies involved include DARPA, ARDA and NIST. Their programmes, for example DARPA's TIDES (Translingual Information Detection Extraction and Summarization) programme, ARDA's Advanced Question & Answering Program and NIST's TREC (Text Retrieval Conferences) programme cover a range of subprogrammes. These focus on different tasks requiring their own evaluation designs.

Within TIDES and among other researchers interested in document understanding, a group grew up which has been focusing on summarization and the evaluation of summarization systems. Part of the initial evaluation for TIDES called for a workshop to be held in the fall of 2000 to explore different ways of summarizing a common set of documents. Additionally a road mapping effort was started in March of 2000 to lay plans for a long-term evaluation effort in summarization.

Out of the initial workshop and the roadmapping effort has grown a continuing evaluation in the area of text summarization called the Document Understanding Conferences (DUC). Sponsored by the Advanced Research and Development Activity (ARDA), the conference series is run by the National Institute of Standards and Technology (NIST) to further progress in summarization and enable researchers to participate in large-scale experiments.
ImageNet-VidVRD

ImageNet-VidVRD dataset contains 1,000 videos selected from ILVSRC2016-VID dataset based on whether the video contains clear visual relations. It is split into 800 training set and 200 test set, and covers common subject/objects of 35 categories and predicates of 132 categories. Ten people contributed to labeling the dataset, which includes object trajectory labeling and relation labeling. Since the ILVSRC2016-VID dataset has the object trajectory annotation for 30 categories already, we supplemented the annotations by labeling the remaining 5 categories. In order to save the labor of relation labeling, we labeled typical segments of the videos in the training set and the whole of the videos in the test set.
VidOR

VidOR (Video Object Relation) dataset contains 10,000 videos (98.6 hours) from YFCC100M collection together with a large amount of fine-grained annotations for relation understanding. In particular, 80 categories of objects are annotated with bounding-box trajectory to indicate their spatio-temporal location in the videos; and 50 categories of relation predicates are annotated among all pairs of annotated objects with starting and ending frame index. This results in around 50,000 object and 380,000 relation instances annotated. To use the dataset for model development, the dataset is split into 7,000 videos for training, 835 videos for validation, and 2,165 videos for testing.
Euro-PVI

The Euro-PVI dataset contains trajectories of pedestrians and bicyclists, with dense interactions with the ego-vehicle. The dataset is collected in Brussels and Leuven, Belgium. The goal of this dataset is to address the challenge of future trajectory prediction in urban environments with dense pedestrian (bicyclist) - vehicle interactions.

Introducing paper: Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers