Omkar2118/datasets huggingface dataset:
TextWorld KG

**TextWorld KG** is a dynamic Knowledge Graph (KG) extraction dataset. It is based on a set of text-based games generated using. That framework allows to extract the underlying partial KG for every state, i.e., the subgraph that represents the agent’s partial knowledge of the world – what it has observed so far. All games share the same overarching theme: the agent finds itself hungry in a simple modern house with the goal of gathering ingredients and cooking a meal.

Source: [https://arxiv.org/abs/1910.09532](https://arxiv.org/abs/1910.09532)

Introducing paper: Building Dynamic Knowledge Graphs from Text-based Games
TextZoom

**TextZoom** is a super-resolution dataset that consists of paired Low Resolution – High Resolution scene text images. The images are captured by cameras with different focal length in the wild.

Source: [https://github.com/JasonBoy1/TextZoom](https://github.com/JasonBoy1/TextZoom)
Image Source: [https://github.com/JasonBoy1/TextZoom](https://github.com/JasonBoy1/TextZoom)

Introducing paper: Scene Text Image Super-Resolution in the Wild
Texygen Platform

Texygen is a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the diversity, the quality and the consistency of the generated texts. The Texygen platform could help standardize the research on text generation and facilitate the sharing of fine-tuned open-source implementations among researchers for their work. As a consequence, this would help in improving the reproductivity and reliability of future research work in text generation.

Source: [Texygen Platform](https://github.com/geek-ai/Texygen)

Introducing paper: Texygen: A Benchmarking Platform for Text Generation Models
TG-ReDial

**TG-ReDial** is a a topic-guided conversational recommendation dataset for research on conversational/interactive recommender systems.

Source: [https://github.com/RUCAIBox/TG-ReDial](https://github.com/RUCAIBox/TG-ReDial)
Image Source: [https://github.com/RUCAIBox/TG-ReDial](https://github.com/RUCAIBox/TG-ReDial)

Introducing paper: Towards Topic-Guided Conversational Recommender System
The RobotriX

Photorealistic indoor dataset designed to enable the application of deep learning techniques to a wide variety of robotic vision problems. The RobotriX consists of hyperrealistic indoor scenes which are explored by robot agents which also interact with objects in a visually realistic manner in that simulated world. 

Source: [The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions](/paper/the-robotrix-an-extremely-photorealistic-and)

Introducing paper: The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions
VLOG Dataset

A large collection of interaction-rich video data which are annotated and analyzed.

Source: [From Lifestyle Vlogs to Everyday Interactions](/paper/from-lifestyle-vlogs-to-everyday-interactions)

Introducing paper: From Lifestyle Vlogs to Everyday Interactions
ThirdToFirst

Two datasets (synthetic and natural/real) containing simultaneously recorded egocentric and exocentric videos.

Source: [From Third Person to First Person: Dataset and Baselines for Synthesis and Retrieval](/paper/from-third-person-to-first-person-dataset-and)

Introducing paper: From Third Person to First Person: Dataset and Baselines for Synthesis and Retrieval
TicketTalk

A movie ticketing dialog dataset with 23,789 annotated conversations. The movie ticketing conversations range from completely open-ended and unrestricted to more structured, both in terms of their knowledge base, discourse features, and number of turns. In qualitative human evaluations, model-generated responses trained on just 10,000 TicketTalk dialogs were rated to "make sense" 86.5 percent of the time, almost the same as human responses in the same contexts.

Source: [TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems](/paper/tickettalk-toward-human-level-performance)

Introducing paper: TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems
TikTok Comments

TikTok Comments is a domain specific lexicon based on TikTok comments dataset.

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Introducing paper: Building domain specific lexicon based on TikTok comment dataset
Tilde MODEL Corpus
Full name: Tilde Multilingual Open Data for European Languages

Tilde MODEL Corpus is a multilingual corpora for European languages – particularly focused on the smaller languages. The collected resources have been cleaned, aligned, and formatted into a corpora standard TMX format useable for developing new Language technology products and services.

It contains over 10M segments of multilingual open data.

The data has been collected from sites allowing free use and reuse of its content, as well as from Public Sector web sites.

Source: [Tilde MODEL - Multilingual Open Data for EU Languages](/paper/tilde-model-multilingual-open-data-for-eu)

Introducing paper: Tilde MODEL - Multilingual Open Data for EU Languages
Tilt-RGBD

Includes considerable roll and pitch camera motion.

Source: [Surface Normal Estimation of Tilted Images via Spatial Rectifier](/paper/surface-normal-estimation-of-tilted-images)

Introducing paper: Surface Normal Estimation of Tilted Images via Spatial Rectifier
Time-Lapse Hyperspectral Radiance Images

These sequences of hyperspectral radiance images have been taken from scenes undergoing natural illumination changes. In each scene, hyperspectral images were acquired at about 1-hour intervals. 

Source: [Time-Lapse Hyperspectral Radiance Images of Natural Scenes 2015](https://personalpages.manchester.ac.uk/staff/d.h.foster/Time-Lapse_HSIs/Time-Lapse_HSIs_2015.html)
TimeTravel

TimeTravel contains 29,849 counterfactual rewritings, each with the original story, a counterfactual event, and human-generated revision of the original story compatible with the counterfactual event. 

Source: [Counterfactual Story Reasoning and Generation](/paper/counterfactual-story-reasoning-and-generation)

Introducing paper: Counterfactual Story Reasoning and Generation
TIM-Tremor
Full name: Technology in Motion Tremor

Contains static tasks as well as a multitude of more dynamic tasks, involving larger motion of the hands. The dataset has 55 tremor patient recordings together with: associated ground truth accelerometer data from the most affected hand, RGB video data, and aligned depth data.

Source: [Hand-tremor frequency estimation in videos](/paper/hand-tremor-frequency-estimation-in-videos)

Introducing paper: Hand-tremor frequency estimation in videos
HAKE

HAKE is built upon existing activity datasets and provides human body part level atomic action labels (Part States).

Introducing paper: PaStaNet: Toward Human Activity Knowledge Engine
TinyPerson

**TinyPerson** is a benchmark for tiny object detection in a long distance and with massive backgrounds. The images in TinyPerson are collected from the Internet. First, videos with a high resolution are collected from different websites. Second, images from the video are sampled every 50 frames. Then images with a certain repetition (homogeneity) are deleted, and the resulting images are annotated with 72,651 objects with bounding boxes by hand.

Source: [https://arxiv.org/abs/1912.10664](https://arxiv.org/abs/1912.10664)
Image Source: [https://arxiv.org/pdf/1912.10664.pdf](https://arxiv.org/pdf/1912.10664.pdf)

Introducing paper: Scale Match for Tiny Person Detection
TinySocial

TinySocial is a dataset to enable research on Social Visual Question Answering.

Source: [Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset](/paper/characterizing-datasets-for-social-visual)

Introducing paper: Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset
TinyVIRAT

TinyVIRAT contains natural low-resolution activities. The actions in TinyVIRAT videos have multiple labels and they are extracted from surveillance videos which makes them realistic and more challenging.

Source: [TinyVIRAT: Low-resolution Video Action Recognition](/paper/tinyvirat-low-resolution-video-action)

Introducing paper: TinyVIRAT: Low-resolution Video Action Recognition
TJU-DHD

**TJU-DHD** is a high-resolution dataset for object detection and pedestrian detection. The dataset contains 115,354 high-resolution images (52% images have a resolution of 1624×1200 pixels and 48% images have a resolution of at least 2,560×1,440 pixels) and 709,330 labelled objects in total with a large variance in scale and appearance.

Source: [https://github.com/tjubiit/TJU-DHD](https://github.com/tjubiit/TJU-DHD)
Image Source: [https://github.com/tjubiit/TJU-DHD](https://github.com/tjubiit/TJU-DHD)

Introducing paper: TJU-DHD: A Diverse High-Resolution Dataset for Object Detection
TLL
Full name: Totally-Looks-Like

Contains 6016 image-pairs from the wild, shedding light upon a rich and diverse set of criteria employed by human beings.

Source: [Totally Looks Like - How Humans Compare, Compared to Machines](/paper/totally-looks-like-how-humans-compare)

Introducing paper: Totally Looks Like - How Humans Compare, Compared to Machines
TLP
Full name: Track Long and Prosper

A new long video dataset and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking.

Source: [Long-Term Visual Object Tracking Benchmark](/paper/long-term-visual-object-tracking-benchmark)

Introducing paper: Long-Term Visual Object Tracking Benchmark
TME Motorway Dataset

The “Toyota Motor Europe (TME) Motorway Dataset” is composed by 28 clips for a total of approximately 27 minutes (30000+ frames) with vehicle annotation. Annotation was semi-automatically generated using laser-scanner data. Image sequences were selected from acquisition made in North Italian motorways in December 2011. This selection includes variable traffic situations, number of lanes, road curvature, and lighting, covering most of the conditions present in the complete acquisition.

The dataset comprises:

- Image acquisition: stereo, 20 Hz frequency , 1024x768 grayscale losslessly compressed images, 32° horizontal field of view, bayer coded color information (in OpenCV use CV_BayerGB2GRAY and CV_BayerGB2BGR color conversion codes; please note that left camera was rotated upside down, convert to color/grayscale BEFORE flipping the image). A checkboard calibration sequence is made available.
- Laser-scanner generated vehicle annotation and classification (car/truck).
- A software evaluation toolkit (C++ source code).

Source: [TME Motorway Dataset](http://cmp.felk.cvut.cz/data/motorway/)