Omkar2118/datasets huggingface dataset:
NYU-VPR

**NYU-VPR** is a dataset for Visual place recognition (VPR) that contains more than 200,000 images over a 2km×2km area near the New York University campus, taken within the whole year of 2016.

Introducing paper: NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences
CodRep

Five curated datasets of one-liner commits from open-source projects. In total,
they are composed of 58069 one-liner commits.

Introducing paper: SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
MAAD

The **Model for Attended Awareness in Driving** (**MAAD**) is a dataset of third-person estimates of a driver’s attended awareness. It consists of videos of a scene, as seen by a person performing a task in the scene, along with noisily registered ego-centric gaze sequences from that person.

Introducing paper: MAAD: A Model and Dataset for "Attended Awareness" in Driving
Experiment-data-for-UM-S-TM

0.This is experiment data for the following article:
@misc{liu2021topic,
      title={Topic Model Supervised by Understanding Map}, 
      author={Gangli Liu},
      year={2021},
      eprint={2110.06043},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

1.  *.txt files are the data of Table 4 of the paper.

2. The top lines of all the *.txt files are contents of the artificial documents. Column names are  : "Topic", "Distance", "Topic-len", "alpha"/"Noise" , "doc concept-length", and "Votes counter".

3.Coding of file names of *.txt files see "Table 4: Discovered SCOM of six documents". "all_topic" means the candidate topic set is all the topics in a domain.

4.For the "300docs-mentioned-in-section3.2.xlsx" file, its name tells its contents.

Introducing paper: Topic Model Supervised by Understanding Map
CNewSum

**CNewSum** is a large-scale Chinese news summarization dataset which consists of 304,307 documents and human-written summaries for the news feed. It has long documents with high-abstractive summaries, which can encourage document-level understanding and generation for current summarization models. An additional distinguishing feature of CNewSum is that its test set contains adequacy and deducibility annotations for the summaries.

Introducing paper: CNewSum: A Large-scale Chinese News Summarization Dataset with Human-annotated Adequacy and Deducibility Level
UVO
Full name: Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation

UVO is a new benchmark for open-world class-agnostic object segmentation in videos. Besides shifting the problem focus to the open-world setup, UVO is significantly larger, providing approximately 8 times more videos compared with [DAVIS](/dataset/davis), and 7 times more mask (instance) annotations per video compared with [YouTube-VOS](/dataset/youtube-vos) and [YouTube-VIS](/dataset/youtubevis). UVO is also more challenging as it includes many videos with crowded scenes and complex background motions. Some highlights of the dataset include:

- High quality instance masks densely annotated at 30 fps on 1024 YouTube videos and 1fps on 10337 videos from Kinetics dataset

- Open-world: annotating all objects in each video, 13.5 objects per video on average

- Diverse object categories: 57% of objects are not covered by COCO categories

Introducing paper: Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation
ASIRRA
Full name: (Animal Species Image Recognition for Restricting Access

Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords.

Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface:

Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research.

Introducing paper: Microsoft Research Asia's Systems for WMT19