dataset_name stringlengths 2 128 | description stringlengths 1 9.7k | prompt stringlengths 59 185 |
|---|---|---|
Action Recognition in the Dark | ARID is a dataset for action recognition in dark videos. It consists of over 3,780 video clips with 11 action categories. | Provide a detailed description of the following dataset: Action Recognition in the Dark |
Kuzushiji-Kanji | Kuzushiji-Kanji is an imbalanced dataset of total 3832 Kanji characters (64x64 grayscale, 140,426 images), ranging from 1,766 examples to only a single example per class. Kuzushiji is a Japanese cursive writing style. | Provide a detailed description of the following dataset: Kuzushiji-Kanji |
UCO-LAEO | A dataset for building models that detect people Looking At Each Other (LAEO) in video sequences. | Provide a detailed description of the following dataset: UCO-LAEO |
Skeletics 152 | A curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset. | Provide a detailed description of the following dataset: Skeletics 152 |
TVQA+ | TVQA+ contains 310.8K bounding boxes, linking depicted objects to visual concepts in questions and answers. | Provide a detailed description of the following dataset: TVQA+ |
Spot the Difference Corpus | Spot the Difference Corpus is a corpus of task-oriented spontaneous dialogues which contains 54 interactions between pairs of subjects interacting to find differences in two very similar scenes. The corpus includes rich transcriptions, annotations, audio and video. | Provide a detailed description of the following dataset: Spot the Difference Corpus |
CCPE-M | A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language.
The corpus was constructed from dialogues between two paid crowd-workers using a Wizard-of-Oz methodology. One worker plays the role of an "assistant", while the other plays the role of a "user". The "assistant" is tasked with eliciting the "user" preferences about movies following a Coached Conversational Preference Elicitation (CCPE) methodology. In particular, the assistant is required to ask questions designed so as to minimize the bias in the terminology the "user" employs to convey his or her preferences, and obtain these in as natural language as possible. Each dialog is annotated with entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of entities. | Provide a detailed description of the following dataset: CCPE-M |
COCO-CN | COCO-CN is a bilingual image description dataset enriching MS-COCO with manually written Chinese sentences and tags. The new dataset can be used for multiple tasks including image tagging, captioning and retrieval, all in a cross-lingual setting. | Provide a detailed description of the following dataset: COCO-CN |
T2 Guiding | T2 Guiding is a dataset of 1000 images, each with six image labels. The images are from the Open Images Dataset (OID) and the dataset includes 2 sets of machine-generated labels for these images.
* Object labels: Three random object labels generated by a FRCNN model trained on Visual Genome.
* Image labels: Three random image labels obtained from Google Cloud Vision API. | Provide a detailed description of the following dataset: T2 Guiding |
FarsBase-KBP | FarsBase-KBP contains 22015 sentences, in which the entities and relation types are linked to the FarsBase ontology. This gold dataset can be reused for benchmarking KBP systems in the Persian language. | Provide a detailed description of the following dataset: FarsBase-KBP |
Visual Wake Words | Visual Wake Words represents a common microcontroller vision use-case of identifying whether a person is present in the image or not, and provides a realistic benchmark for tiny vision models. | Provide a detailed description of the following dataset: Visual Wake Words |
ReQA | Retrieval Question-Answering (ReQA) benchmark tests a model’s ability to retrieve relevant answers efficiently from a large set of documents. | Provide a detailed description of the following dataset: ReQA |
ROSE | Retinal OCTA SEgmentation dataset (ROSE) consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level. | Provide a detailed description of the following dataset: ROSE |
UDIVA | UDIVA is a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self and peer-reported personality, internal state, and relationship profiling from participants. | Provide a detailed description of the following dataset: UDIVA |
MuST-Cinema | MuST-Cinema is a Multilingual Speech-to-Subtitles corpus ideal for building subtitle-oriented machine and speech translation systems.
It comprises audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations.
MuST-Cinema was built by annotating MuST-C with subtitle breaks based on the original subtitle files. Special symbols have been inserted in the aligned sentences to mark subtitle breaks as follows:
- <eob>: block break (breaks between subtitle blocks)
- <eol>: line breaks (breaks between lines inside the same subtitle block) | Provide a detailed description of the following dataset: MuST-Cinema |
LIV360SV | The dataset contains 26,645, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th -- 18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405) (e.g., cars and broadband). | Provide a detailed description of the following dataset: LIV360SV |
i3-video | The i3-video dataset contains "is-it-instructional" annotations for 6.4k videos from [Youtube-8M](https://paperswithcode.com/dataset/youtube-8m). The videos are considered to be instructional if they focus on real-world human actions accompanied by procedural language that explains what’s happening on screen in reasonable details. | Provide a detailed description of the following dataset: i3-video |
Open Images V4 | Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, 15x more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images) are provided. The images often show complex scenes with several objects (8 annotated objects per image on average). Visual relationships between them are annotated, which support visual relationship detection, an emerging task that requires structured reasoning. | Provide a detailed description of the following dataset: Open Images V4 |
CLaRO | CLaRO is a new dataset of 234 Competency Questions that had been processed automatically into 106 patterns. The coverage of CLaRO, with its 93 main templates and 41 linguistic variants, is about 90% for unseen questions. | Provide a detailed description of the following dataset: CLaRO |
ImagiFilter | ImagiFilter focusses on photographic and/or natural images, a very common use-case in computer vision research. Annotations for coarse prediction are provided, i.e. photographic vs. non-photographic, and smaller fine-grained prediction tasks where the non-photographic class is broken down into five classes: maps, drawings, graphs, icons, and sketches. | Provide a detailed description of the following dataset: ImagiFilter |
CROSS | Cross-Reference Omnidirectional Stitching IQA is a novel omnidirectional image dataset containing stitched images as well as dual-fisheye images captured from standard quarters of 0◦, 90◦ , 180◦ and 270◦. In this manner, when evaluating the quality of an image stitched from a pair of fisheye images (e.g., 0◦ and 180◦), the other pair of fisheye images (e.g., 90◦ and 270◦) can be used as the cross-reference to provide ground-truth observations of the stitching regions. | Provide a detailed description of the following dataset: CROSS |
BIRD | Blocksworld Image Reasoning Dataset (BIRD) contains images of wooden blocks in different configurations, and the sequence of moves to rearrange one configuration to the other. | Provide a detailed description of the following dataset: BIRD |
Almawave-SLU | Almawave-SLU is the first Italian dataset for Spoken Language Understanding (SLU). It is derived through a semi-automatic procedure and is used as a benchmark of various open source and commercial systems. | Provide a detailed description of the following dataset: Almawave-SLU |
EPIC30M | EPIC30M contains a subset of 26.2 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 4.7 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola. | Provide a detailed description of the following dataset: EPIC30M |
The Spoken Wikipedia Corpora | The SWC is a corpus of aligned Spoken Wikipedia articles from the English, German, and Dutch Wikipedia. This corpus has several outstanding characteristics:
- hundreds of hours of aligned audio
- from a diverse set of readers
- about a diverse set of topics
- in a well-researched textual genre
- licensed under a free license (CC BY-SA 4.0)
- Annotations can be mapped back to the original html
- phoneme-level alignments | Provide a detailed description of the following dataset: The Spoken Wikipedia Corpora |
Flickr Audio Caption Corpus | The Flickr 8k Audio Caption Corpus contains 40,000 spoken captions of 8,000 natural images. It was collected in 2015 to investigate multimodal learning schemes for unsupervised speech pattern discovery. For a description of the corpus, see:
D. Harwath and J. Glass, "Deep Multimodal Semantic Embeddings for Speech and Images," 2015 IEEE Automatic Speech Recognition and Understanding Workshop, pp. 237-244, Scottsdale, Arizona, USA, December 2015 | Provide a detailed description of the following dataset: Flickr Audio Caption Corpus |
PCVC | The **Persian Consonant Vowel Combination (PCVC)** dataset is a phoneme based speech dataset, and also the first free Persian speech dataset to help Persian speech researchers. This dataset contains of 23 Persian consonants and 6 vowels. The sound samples are all possible combinations of vowels and consonants (138 samples for each speaker) with a length of 30000 data samples. The sample rate of all speech samples is 48000 which means there are 48000 sound samples in every 1 second. In each sample, sound starts with consonant and then there is a vowel sound and at last there is silent. length of silence is dependent on length of combination of consonant and vowel. For example if combination ends in 20000th data sample, so the rest of 10000 sample (until 30000, the length of each sound sample) are silence. | Provide a detailed description of the following dataset: PCVC |
DensePose-Track | DensePose-Track is a dataset of videos where selected frames are annotated in the traditional DensePose manner. | Provide a detailed description of the following dataset: DensePose-Track |
2000 HUB5 English | **2000 HUB5 English Evaluation Transcripts** was developed by the Linguistic Data Consortium (LDC) and consists of transcripts of 40 English telephone conversations used in the 2000 HUB5 evaluation sponsored by NIST (National Institute of Standards and Technology).
The Hub5 evaluation series focused on conversational speech over the telephone with the particular task of transcribing conversational speech into text. Its goals were to explore promising new areas in the recognition of conversational speech, to develop advanced technology incorporating those ideas and to measure the performance of new technology. | Provide a detailed description of the following dataset: 2000 HUB5 English |
Parkinson Speech Dataset | **Parkinson Speech Dataset** is an audio dataset consisting of recordings of 20 Parkinson's Disease (PD) patients and 20 healthy subjects. From all subjects, multiple types of sound recordings (26) are taken. The goal is to classify which patients have Parkinson's. | Provide a detailed description of the following dataset: Parkinson Speech Dataset |
MDID | The Multimodal Document Intent Dataset (MDID) is a dataset for computing author intent from multimodal data from Instagram. It contains 1,299 Instagram posts covering a variety of topics, annotated with labels from three taxonomies. The samples are labelled with 7 labels of intent: Provocative, Informative, Advocative, Entertainment, Expositive, Expressive, Promotive | Provide a detailed description of the following dataset: MDID |
Ciona17 | Ciona17 is a semantic segmentation dataset with pixel-level annotations pertaining to invasive species in a marine environment. Diverse outdoor illumination, a range of object shapes, colour, and severe occlusion provide a significant real world challenge for the computer vision community. | Provide a detailed description of the following dataset: Ciona17 |
Arabic Speech Corpus | The **Arabic Speech Corpus** (1.5 GB) is a Modern Standard Arabic (MSA) speech corpus for speech synthesis. The corpus contains phonetic and orthographic transcriptions of more than 3.7 hours of MSA speech aligned with recorded speech on the phoneme level. The annotations include word stress marks on the individual phonemes The Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton. The corpus was recorded in south Levantine Arabic (Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice. | Provide a detailed description of the following dataset: Arabic Speech Corpus |
Mivia Audio Events Dataset | The **MIVIA audio events** data set is composed of a total of 6000 events for surveillance applications, namely glass breaking, gun shots and screams. The 6000 events are divided into a training set (composed of 4200 events) and a test set (composed of 1800 events).
In audio surveillance applications, the events of interest (for instance a scream) can occur at different distances from the microphone that correspond to different levels of the signal-to-noise ratio. Moreover, in these applications the events are generally mixed with a complex background, usually composed of several types of different sounds depending on the specific environments both indoor and outdoor (household appliances, cheering of crowds, talking people, traffic jam, passing cars or motorbikes etc.).
The data set is designed to provide each audio event at 6 different values of signal-to-noise ratio (namely 5dB, 10dB, 15dB, 20dB, 25dB and 30dB) and overimposed to different combinations of environmental sounds in order to simulate their occurrence in different ambiences. | Provide a detailed description of the following dataset: Mivia Audio Events Dataset |
VRAI | VRAI is a large-scale vehicle ReID dataset for UAV-based intelligent applications. The dataset consists of 137, 613 images of 13, 022 vehicle instances. The images of each vehicle instance are captured by cameras of two DJI consumer UAVs at different locations, with a variety of view angles and flight-altitudes (15m to 80m). | Provide a detailed description of the following dataset: VRAI |
RWCP Sound Scene Database | The **RWCP Sound Scene Database** includes non-speech sounds recorded in an anechoic room, reconstructed signals in various rooms, impulse responses for a microphone array, speech data recorded with the same array, and recordings of background noises. It is intended for use when simulating sound scenes. It was developed by the Real Acoustic Environments Working Group of the Real World Computing Partnership (RWCP). The data was recorded from 1998 to 2000. | Provide a detailed description of the following dataset: RWCP Sound Scene Database |
UIT-ViIC | UIT-ViIC contains manually written captions for images from Microsoft COCO dataset relating to sports played with ball. UIT-ViIC consists of 19,250 Vietnamese captions for 3,850 images. | Provide a detailed description of the following dataset: UIT-ViIC |
NAR | **NAR** is a dataset of audio recordings made with the humanoid robot Nao in real world conditions for sound recognition benchmarking. All the recordings were collected using the robot’s microphone and thus have the following characteristics:
- recorded with low-quality sensors (300 Hz – 18 kHz bandpass)
- suffering from typical fan noise from the robot’s internal hardware
- recorded in mutiple real domestic environments (no special acoustic charateristics, reverberations, presence of multiple sound sources and unknown locations) | Provide a detailed description of the following dataset: NAR |
IISc VINE | Indian Institute of Science VIdeo Naturalness Evaluation (IISc VINE) is a database consisting of 300 videos, obtained by applying different prediction models on different datasets, and accompanying human opinion scores. | Provide a detailed description of the following dataset: IISc VINE |
MineNav | MinNav is a synthetic dataset based on the sandbox game Minecraft. The dataset uses several plug-in program to generate rendered image sequences with time-aligned depth maps, surface normal maps and camera poses. Thanks for the large game's community, there is an extremely large number of 3D open-world environment, users can find suitable scenes for shooting and build data sets through it and they can also build scenes in-game. | Provide a detailed description of the following dataset: MineNav |
Minecraft House | **Minecraft House** is a crowd sourced dataset that collects examples of humans building houses in Minecraft. Each user is asked to build a CraftAssist: A Framework for Dialogue-enabled Interactive Agents house on a fixed time budget (30 minutes), without any additional guidance or instructions. Every action of the user is recorded using the Cuberite server.
The data collection was performed in Minecraft’s creative mode, where the user is given unlimited resources, has access to all material block types and can freely move in
the game world. The action space of the environment is straight-forward: moving in x-y-z dimensions, choosing a block type, and placing or breaking a block.
There are 2586 houses in total. | Provide a detailed description of the following dataset: Minecraft House |
ARVSU | ARVSU contains a vast body of image variations in visual scenes with an annotated utterance and a corresponding addressee for each scenario. | Provide a detailed description of the following dataset: ARVSU |
m2cai16-tool-locations | The m2cai16-tool-locations dataset contains spatial tool annotations for 2,532 frames across the first 10 videos in the m2cai16-tool dataset, which includes 15 videos in total. The dataset consists of 3,141 annotations of 7 surgical instrument classes, with an average of 1.2 labels per frame and 7 instrument classes per video. | Provide a detailed description of the following dataset: m2cai16-tool-locations |
Minecraft Segmentation | **Minecraft Segmentation** is a segmentation dataset for the [Minecraft House](https://www.paperswithcode.com/dataset/minecraft-house) that adds semantic
segmentation labels for sub-components of the house. There are 2050 houses in total and 1038 distinct labels of subcomponents. | Provide a detailed description of the following dataset: Minecraft Segmentation |
CryoNuSeg | CryoNuSeg is a fully annotated FS-derived cryosectioned and H&E-stained nuclei instance segmentation dataset. The dataset contains images from 10 human organs that were not exploited in other publicly available datasets, and is provided with three manual mark-ups to allow measuring intra-observer and inter-observer variability. | Provide a detailed description of the following dataset: CryoNuSeg |
Princeton Shape | The **Princeton Shape** dataset provides a repository of 3D models and software tools for evaluating shape-based retrieval and analysis algorithms. The motivation is to promote the use of standardized data sets and evaluation methods for research in matching, classification, clustering, and recognition of 3D models. Researchers are encouraged to use these resources to produce comparisons of competing algorithms in future publications. There are 1,814 models in total. | Provide a detailed description of the following dataset: Princeton Shape |
Opusparcus | Opusparcus is a paraphrase corpus for six European languages: German, English, Finnish, French, Russian, and Swedish. The paraphrases are extracted from the OpenSubtitles2016 corpus, which contains subtitles from movies and TV shows.
For each target language, the Opusparcus data have been partitioned into three types of data sets: training, development and test sets. The training sets are large, consisting of millions of sentence pairs, and have been compiled automatically, with the help of probabilistic ranking functions. The development and test sets consist of sentence pairs that have been annotated manually; each set contains approximately 1000 sentence pairs that have been verified to be acceptable paraphrases by two annotators. | Provide a detailed description of the following dataset: Opusparcus |
IKEA 3D | **IKEA 3D** is a dataset of IKEA 3D models and aligned images, which is suitable for pose estimation. There are 759 images and 219 models including Sketchup (skp) and Wavefront (obj) files. | Provide a detailed description of the following dataset: IKEA 3D |
RSOC | RSOC is a large-scale object counting dataset with remote sensing images, which contains four important geographic objects: buildings, crowded ships in harbors, large-vehicles and small-vehicles in parking lots. | Provide a detailed description of the following dataset: RSOC |
A Large Dataset of Object Scans | **A Large Dataset of Object Scans** is a dataset of more than ten thousand 3D scans of real objects. To create the dataset, the authors recruited 70 operators, equipped them with consumer-grade mobile 3D scanning setups, and paid them to scan objects in their environments. The operators scanned objects of their choosing, outside the laboratory and without direct supervision by computer vision professionals. The result is a large and diverse collection of object scans: from shoes, mugs, and toys to grand pianos, construction vehicles, and large outdoor sculptures. The authors worked with an attorney to ensure that data acquisition did not violate privacy constraints. The acquired data was placed in the public domain and is available freely. | Provide a detailed description of the following dataset: A Large Dataset of Object Scans |
RGRS | RGRS is a dataset for collaboratior recommendation on the ResearchGate academic social network. The data has been collected from Jan. 2019 to April 2019 and
includes raw data of 3980 RG users. | Provide a detailed description of the following dataset: RGRS |
ObjectNet3D | **ObjectNet3D** is a large scale database for 3D object recognition, named, that consists of 100 categories, 90,127 images, 201,888 objects in these images and 44,147 3D shapes. Objects in the images in the database are aligned with the 3D shapes, and the alignment provides both accurate 3D pose annotation and the closest 3D shape annotation for each 2D object. Consequently, the database is useful for recognizing the 3D pose and 3D shape of objects from 2D images. Authors also provide baseline experiments on four tasks: region proposal generation, 2D object detection, joint 2D detection and 3D object pose estimation, and image-based 3D shape retrieval, which can serve as baselines for future research. | Provide a detailed description of the following dataset: ObjectNet3D |
Event-Stream Dataset | Event-Stream Dataset is a robotic grasping dataset with 91 objects. | Provide a detailed description of the following dataset: Event-Stream Dataset |
PersonalDialog | PersonalDialog is a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. | Provide a detailed description of the following dataset: PersonalDialog |
Thingi10K | **Thingi10K** is a dataset of 3D-Printing Models. Specifically there are 10,000 models from featured “things” on thingiverse.com, suitable for testing 3D printing techniques such as structural analysis , shape optimization, or solid geometry operations. | Provide a detailed description of the following dataset: Thingi10K |
CocoDoom | CocoDoom is a collection of pre-recorded data extracted from Doom gaming sessions along with annotations in the MS Coco format. | Provide a detailed description of the following dataset: CocoDoom |
VOCASET | **VOCASET** is a 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio. The dataset has 12 subjects and 480 sequences of about 3-4 seconds each with sentences chosen from an array of standard protocols that maximize phonetic diversity. | Provide a detailed description of the following dataset: VOCASET |
MSAW | Multi-Sensor All Weather Mapping (MSAW) is a dataset and challenge, which features two collection modalities (both SAR and optical). The dataset and challenge focus on mapping and building footprint extraction using a combination of these data sources. MSAW covers 120 km^2 over multiple overlapping collects and is annotated with over 48,000 unique building footprints labels, enabling the creation and evaluation of mapping algorithms for multi-modal data. | Provide a detailed description of the following dataset: MSAW |
ADE-Affordance | ADE-Affordance is a new dataset that builds upon ADE20k, which contains annotations enabling such rich visual reasoning. | Provide a detailed description of the following dataset: ADE-Affordance |
PISC | The People in Social Context (PISC) dataset is a dataset that focuses on social relationships. It consists of 22,670 images of 9 types of social relationships. It has annotations for the bounding boxes of all people, as well as the social relationship between all pairs of people in the images. In addition, it also contains occupation annotation. | Provide a detailed description of the following dataset: PISC |
MINOS | **MINOS** is a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. MINOS leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites. | Provide a detailed description of the following dataset: MINOS |
WIKIOG | WIKIOG is a public collection which consists of over 1.75 million document-outline pairs for research on the OG task. | Provide a detailed description of the following dataset: WIKIOG |
SemanticUSL | SemanticUSL is a dataset for domain adaptation for LiDAR point cloud semantic segmentation. The dataset has the same data format and ontology as SemanticKITTI. | Provide a detailed description of the following dataset: SemanticUSL |
Jericho | Jericho is a learning environment for man-made Interactive Fiction (IF) games. | Provide a detailed description of the following dataset: Jericho |
3D-FRONT | **3D-FRONT** (3D Furnished Rooms with layOuts and semaNTics) is large-scale, and comprehensive repository of synthetic indoor scenes highlighted by professionally designed layouts and a large number of rooms populated by high-quality textured 3D models with style compatibility. From layout semantics down to texture details of individual objects, the dataset is freely available to the academic community and beyond.
3D-FRONT contains 18,797 rooms diversely furnished by 3D objects. In addition, the 7,302 furniture objects all come with high-quality textures. While the floorplans and layout designs are directly sourced from professional creations, the interior designs in terms of furniture styles, color, and textures have been carefully curated based on a recommender system to attain consistent styles as expert designs. | Provide a detailed description of the following dataset: 3D-FRONT |
JParaCrawl | JParaCrawl is a parallel corpus for English-Japanese, for which the amount of publicly available parallel corpora is still limited. The parallel corpus was constructed by broadly crawling the web and automatically aligning parallel sentences. The corpus amassed over 8.7 million sentence pairs. | Provide a detailed description of the following dataset: JParaCrawl |
3ThreeDWorld | **TDW** is a 3D virtual world simulation platform, utilizing state-of-the-art video game engine technology. A TDW simulation consists of two components: a) the Build, a compiled executable running on the Unity3D Engine, which is responsible for image rendering, audio synthesis and physics simulations; and b) the Controller, an external Python interface to communicate with the build. | Provide a detailed description of the following dataset: 3ThreeDWorld |
MUSIC | The Multi-Spectral Imaging via Computed Tomography (MUSIC) dataset is a two-part (2D- and 3D spectral) open access dataset for advanced image analysis of spectral radiographic (x-ray) scans, their tomographic reconstruction and the detection of specific materials within such scans. The scans operate at a photon energy range of around 20 keV up to 160 keV.
The dataset includes — for 2D- as well as 3D spectral data — the corrected (e.g. calibrated) radiographic projections, their tomographic reconstructions (based on 37 projections of 256 detector pixels into a 100×100 pixel CT image per slice) and the corresponding set of segmentation variants. | Provide a detailed description of the following dataset: MUSIC |
MuMu | MuMu is a new dataset of more than 31k albums classified into 250 genre classes. | Provide a detailed description of the following dataset: MuMu |
FSVQA | Full-Sentence Visual Question Answering (FSVQA) dataset, consisting of nearly 1 million pairs of questions and full-sentence answers for images, built by applying a number of rule-based natural language processing techniques to original VQA dataset and captions in the MS COCO dataset. | Provide a detailed description of the following dataset: FSVQA |
Taskmaster-1 | **Taskmaster-1** is a dialog dataset consisting of 13,215 task-based dialogs in English, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations.
Image Source: [https://arxiv.org/pdf/1909.05358v1.pdf](https://arxiv.org/pdf/1909.05358v1.pdf) | Provide a detailed description of the following dataset: Taskmaster-1 |
RealEstate10K | **RealEstate10K** is a large dataset of camera poses corresponding to 10 million frames derived from about 80,000 video clips, gathered from about 10,000 YouTube videos. For each clip, the poses form a trajectory where each pose specifies the camera position and orientation along the trajectory. These poses are derived by running SLAM and bundle adjustment algorithms on a large set of videos. | Provide a detailed description of the following dataset: RealEstate10K |
Wikipedia Generation | **Wikipedia Generation** is a dataset for article generation from Wikipedia from references at the end of Wikipedia page and the top 10 search results for the Wikipedia topic. | Provide a detailed description of the following dataset: Wikipedia Generation |
WildestFaces | WildestFaces is tailored to study cross-domain recognition under a variety of adverse conditions. | Provide a detailed description of the following dataset: WildestFaces |
FAD | FAD is a dataset that have roughly 200,000 attribute labels for the above traits, for over 10,000 facial images. | Provide a detailed description of the following dataset: FAD |
VQA 360° | VQA 360° is a dataset for visual question answering on 360° images containing around 17,000 real-world image-question-answer triplets for a variety of question types. | Provide a detailed description of the following dataset: VQA 360° |
StreetStyle | StreetStyle is a large-scale dataset of photos of people annotated with clothing attributes, and use this dataset to train attribute classifiers via deep learning. | Provide a detailed description of the following dataset: StreetStyle |
PHSPD | PHSPD is a home-grown polarization image dataset of various human shapes and poses. | Provide a detailed description of the following dataset: PHSPD |
HARRISON | HARRISON dataset is a benchmark on hashtag recommendation for real world images in social networks. The HARRISON dataset is a realistic dataset, composed of 57,383 photos from Instagram and an average of 4.5 associated hashtags for each photo. | Provide a detailed description of the following dataset: HARRISON |
OC20 | **Open Catalyst 2020** is a dataset for catalysis in chemical engineering. Focusing on molecules that are important in renewable energy applications, the OC20 data set comprises over 1.3 million relaxations of molecular adsorptions onto surfaces, the largest data set of electrocatalyst structures to date. | Provide a detailed description of the following dataset: OC20 |
FSOD | Few-Shot Object Detection Dataset (FSOD) is a high-diverse dataset specifically designed for few-shot object detection and intrinsically designed to evaluate thegenerality of a model on novel categories. | Provide a detailed description of the following dataset: FSOD |
DUS | The **Daimler Urban Segmentation Dataset** is a dataset for semantic segmentation. It consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames (every 10th frame of the sequence) come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching (sgm). | Provide a detailed description of the following dataset: DUS |
HumanAct12 | **HumanAct12** is a new 3D human motion dataset adopted from the polar image and 3D pose dataset PHSPD, with proper temporal cropping and action annotating. Statistically, there are 1191 3D motion clips(and 90,099 poses in total) which are categorized into 12 action classes, and 34 fine-grained sub-classes. The action types includes daily actions such as walk, run, sit down, jump up, warm up, etc. Fine-grained action types contain more specific information like Warm up by bowing left side, Warm up by pressing left leg, etc. | Provide a detailed description of the following dataset: HumanAct12 |
Large Age-Gap | **Large Age-Gap (LAG)** is a dataset for face verification, The dataset contains 3,828 images of 1,010 celebrities. For each identity at least one child/young image and one adult/old image are present. | Provide a detailed description of the following dataset: Large Age-Gap |
Interestingness | The **Interestingness** dataset contains movie excerpts and key-frames and corresponding ground truth files based on classification into interesting and non-interesting samples. It is used for multimedia content interestingness classification. The dataset is composed of:
- Shots and key-frames from a set of 78 Hollywood-like movie trailers of different genres
- The corresponding ground truth
- Additional low-level and mid-level features | Provide a detailed description of the following dataset: Interestingness |
AKCES-GEC | AKCES-GEC is a new dataset on grammatical error correction for Czech. | Provide a detailed description of the following dataset: AKCES-GEC |
LASIESTA | **LASIESTA (Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms)** is a segmentation and detection dataset composed by many real indoor and outdoor sequences organized into categories, each of one covering a specific challenge in moving object detection strategies. | Provide a detailed description of the following dataset: LASIESTA |
E-GMD | Expanded Groove MIDI dataset (E-GMD) is an automatic drum transcription (ADT) dataset that contains 444 hours of audio from 43 drum kits, making it an order of magnitude larger than similar datasets, and the first with human-performed velocity annotations. | Provide a detailed description of the following dataset: E-GMD |
FIRE | **Fundus Image Registration Dataset (FIRE)** is a dataset consisting of 129 retinal images forming 134 image pairs. These image pairs are split into 3 different categories depending on their characteristics. The images were acquired with a Nidek AFC-210 fundus camera, which acquires images with a resolution of 2912x2912 pixels and a FOV of 45° both in the x and y dimensions. Images were acquired at the Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki from 39 patients. | Provide a detailed description of the following dataset: FIRE |
MVB | MVB (Multi View Baggage) is a dataset for baggage ReID task which has some essential differences from person ReID. The features of MVB are three-fold. First, MVB is the first publicly released large-scale dataset that contains 4519 baggage identities and 22660 annotated baggage images as well as its surface material labels. Second, all baggage images are captured by specially-designed multi-view camera system to handle pose variation and occlusion, in order to obtain the 3D information of baggage surface as complete as possible. Third, MVB has remarkable inter-class similarity and intra-class dissimilarity, considering the fact that baggage might have very similar appearance while the data is collected in two real airport environments, where imaging factors varies significantly from each other. | Provide a detailed description of the following dataset: MVB |
300-VW | **300 Videos in the Wild (300-VW)** is a dataset for evaluating facial landmark tracking algorithms in the wild. The dataset authors collected a large number of long facial videos recorded in the wild. Each video has duration of ~1 minute (at 25-30 fps). All frames have been annotated with regards to the same mark-up (i.e. set of facial landmarks) used in the 300 W competition as well (a total of 68 landmarks). The dataset includes 114 videos (circa 1 min each). | Provide a detailed description of the following dataset: 300-VW |
Imp1k | Imp1k is a new dataset of designs annotated with importance information. | Provide a detailed description of the following dataset: Imp1k |
PIROPO | The **PIROPO database** (People in Indoor ROoms with Perspective and Omnidirectional cameras) comprises multiple sequences recorded in two different indoor rooms, using both omnidirectional and perspective cameras. The sequences contain people in a variety of situations, including people walking, standing, and sitting. Both annotated and non-annotated sequences are provided, where ground truth is point-based (each person in the scene is represented by the point located in the center of its head). In total, more than 100,000 annotated frames are available. | Provide a detailed description of the following dataset: PIROPO |
Million-AID | Million-AID is a large-scale benchmark dataset containing a million instances for RS scene classification. There are 51 semantic scene categories in Million-AID. And the scene categories are customized to match the land-use classification standards, which greatly enhance the practicability of the constructed Million-AID. Different form the existing scene classification datasets of which categories are organized with parallel or uncertain relationships, scene categories in Million-AID are organized with systematic relationship architecture, giving it superiority in management and scalability. Specifically, the scene categories in Million-AID are organized by the hierarchical category network of a three-level tree: 51 leaf nodes fall into 28 parent nodes at the second level which are grouped into 8 nodes at the first level, representing the 8 underlying scene categories of agriculture land, commercial land, industrial land, public service land, residential land, transportation land, unutilized land, and water area. The scene category network provides the dataset with excellent organization of relationship among different scene categories and also the property of scalability. The number of images in each scene category ranges from 2,000 to 45,000, endowing the dataset with the property of long tail distribution. Besides, Million-AID has superiorities over the existing scene classification datasets owing to its high spatial resolution, large scale, and global distribution. | Provide a detailed description of the following dataset: Million-AID |
Edge Milling Heads | The **Edge Milling Heads** data set comprises 144 images of an edge profile cutting head of a milling machine. The head tool contains a total of 30 cutting inserts. The cutting head is formed by 6 diagonals of inserts in radial direction along the tool perimeter, encompassing 5 inserts per diagonal in axial direction. Positions of the last and first inserts of consecutive diagonals are aligned in the same vertical. Therefore, even though there are 30 inserts in total, there are 24 equally spaced positions of inserts along the tool perimeter. Additionally, inserts are squared shape with four 90º indexable cutting edges. Inserts are fastened with a screw. Rake angle is 0.
Images were taken with a monochrome camera Genie M1280 1/3’’ with active resolution of 1280 × 960 pixels. AZURE-2514MM fixed lens with 25 mm focal length were used. | Provide a detailed description of the following dataset: Edge Milling Heads |
MLe2e | MLe2 is a dataset for the evaluation of scene text end-to-end reading systems and all intermediate stages such as text detection, script identification and text recognition. The dataset contains a total of 711 scene images covering four different scripts (Latin, Chinese, Kannada, and Hangul). | Provide a detailed description of the following dataset: MLe2e |
VxC TSG | The **VXC TSG** is based on samples taken from the ceramic tile industry and is comprised of 14 ceramic tile models, 42 surface grades and 960 pieces. It has been built in the VxC laboratory, at the Polytechnic University of Valencia, in collaboration with Keraben S.A., a large ceramic tile company located at Nules province of Castellón (Spain). | Provide a detailed description of the following dataset: VxC TSG |
DADA-2000 | DADA-2000 is a large-scale benchmark with 2000 video sequences (named as DADA-2000) is contributed with laborious annotation for driver attention (fixation, saccade, focusing time), accident objects/intervals, as well as the accident categories, and superior performance to state-of-the-arts are provided by thorough evaluations. | Provide a detailed description of the following dataset: DADA-2000 |
Panoramic Image Database | The **Panoramic Image Database** is a panoramic image dataset. The databases were collected by Andrew Vardy while visiting with the Computer Engineering group in February and March of 2004. Images were captured by a robot-mounted camera, pointed upwards at a hyperbolic mirror. The camera was an ImagingSource DFK 4303. The robot was an ActivMedia Pioneer 3-DX. The mirror was a large wide-view hyperbolic mirror from Accowle Ltd. The hyperbolic mirror expands the camera's field of view to allow the capture of panoramic images. | Provide a detailed description of the following dataset: Panoramic Image Database |
SESIV | SEmantic Salient Instance Video (SESIV) dataset is obtained by augmenting the DAVIS-2017 benchmark dataset by assigning semantic ground-truth for salient instance labels. The SESIV dataset consists of 84 high-quality video sequences with pixel-wisely per-frame ground-truth labels. | Provide a detailed description of the following dataset: SESIV |
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