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43 dataset results for Zero-Shot Learning

CIFAR-10 (Canadian Institute for Advanced Research, 10 classes)

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.

15,464 PAPERS • 108 BENCHMARKS

ImageNet

The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.

14,795 PAPERS • 52 BENCHMARKS

CIFAR-100

The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 500 training images and 100 testing images per class.

8,595 PAPERS • 58 BENCHMARKS

CUB-200-2011 (Caltech-UCSD Birds-200-2011)

The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions.

2,174 PAPERS • 49 BENCHMARKS

UCF101 (UCF101 Human Actions dataset)

UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240.

1,790 PAPERS • 25 BENCHMARKS

Oxford 102 Flower (102 Category Flower Dataset)

Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers chosen to be flower commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images.

1,224 PAPERS • 17 BENCHMARKS

DTD (Describable Textures Dataset)

The Describable Textures Dataset (DTD) contains 5640 texture images in the wild. They are annotated with human-centric attributes inspired by the perceptual properties of textures.

784 PAPERS • 8 BENCHMARKS

Stanford Cars

The Stanford Cars dataset consists of 196 classes of cars with a total of 16,185 images, taken from the rear. The data is divided into almost a 50-50 train/test split with 8,144 training images and 8,041 testing images. Categories are typically at the level of Make, Model, Year. The images are 360×240.

737 PAPERS • 13 BENCHMARKS

Food-101

The Food-101 dataset consists of 101 food categories with 750 training and 250 test images per category, making a total of 101k images. The labels for the test images have been manually cleaned, while the training set contains some noise.

735 PAPERS • 15 BENCHMARKS

Caltech-101

The Caltech101 dataset contains images from 101 object categories (e.g., “helicopter”, “elephant” and “chair” etc.) and a background category that contains the images not from the 101 object categories. For each object category, there are about 40 to 800 images, while most classes have about 50 images. The resolution of the image is roughly about 300×200 pixels.

661 PAPERS • 10 BENCHMARKS

EuroSAT

Eurosat is a dataset and deep learning benchmark for land use and land cover classification. The dataset is based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images.

609 PAPERS • 8 BENCHMARKS

FGVC-Aircraft

FGVC-Aircraft contains 10,200 images of aircraft, with 100 images for each of 102 different aircraft model variants, most of which are airplanes. The (main) aircraft in each image is annotated with a tight bounding box and a hierarchical airplane model label. Aircraft models are organized in a four-levels hierarchy. The four levels, from finer to coarser, are:

482 PAPERS • 11 BENCHMARKS

PASCAL Context

The PASCAL Context dataset is an extension of the PASCAL VOC 2010 detection challenge, and it contains pixel-wise labels for all training images. It contains more than 400 classes (including the original 20 classes plus backgrounds from PASCAL VOC segmentation), divided into three categories (objects, stuff, and hybrids). Many of the object categories of this dataset are too sparse and; therefore, a subset of 59 frequent classes are usually selected for use.

303 PAPERS • 6 BENCHMARKS

AwA (Animals with Attributes)

Animals with Attributes (AwA) was a dataset for benchmarking transfer-learning algorithms, in particular attribute base classification. It consisted of 30475 images of 50 animals classes with six pre-extracted feature representations for each image. The animals classes are aligned with Osherson's classical class/attribute matrix, thereby providing 85 numeric attribute values for each class. Using the shared attributes, it is possible to transfer information between different classes. The Animals with Attributes dataset was suspended. Its images are not available anymore because of copyright restrictions. A drop-in replacement, Animals with Attributes 2, is available instead.

262 PAPERS • 7 BENCHMARKS

SNIPS (SNIPS Natural Language Understanding benchmark)

The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:

255 PAPERS • 6 BENCHMARKS

AwA2 (Animals with Attributes 2)

Animals with Attributes 2 (AwA2) is a dataset for benchmarking transfer-learning algorithms, such as attribute base classification and zero-shot learning. AwA2 is a drop-in replacement of original Animals with Attributes (AwA) dataset, with more images released for each category. Specifically, AwA2 consists of in total 37322 images distributed in 50 animal categories. The AwA2 also provides a category-attribute matrix, which contains an 85-dim attribute vector (e.g., color, stripe, furry, size, and habitat) for each category.

229 PAPERS • 5 BENCHMARKS

aPY (Attribute Pascal and Yahoo)

aPY is a coarse-grained dataset composed of 15339 images from 3 broad categories (animals, objects and vehicles), further divided into a total of 32 subcategories (aeroplane, …, zebra).

147 PAPERS • 5 BENCHMARKS

TVQA

The TVQA dataset is a large-scale video dataset for video question answering. It is based on 6 popular TV shows (Friends, The Big Bang Theory, How I Met Your Mother, House M.D., Grey's Anatomy, Castle). It includes 152,545 QA pairs from 21,793 TV show clips. The QA pairs are split into the ratio of 8:1:1 for training, validation, and test sets. The TVQA dataset provides the sequence of video frames extracted at 3 FPS, the corresponding subtitles with the video clips, and the query consisting of a question and four answer candidates. Among the four answer candidates, there is only one correct answer.

137 PAPERS • 3 BENCHMARKS

LSMDC (Large Scale Movie Description Challenge)

This dataset contains 118,081 short video clips extracted from 202 movies. Each video has a caption, either extracted from the movie script or from transcribed DVS (descriptive video services) for the visually impaired. The validation set contains 7408 clips and evaluation is performed on a test set of 1000 videos from movies disjoint from the training and val sets.

123 PAPERS • 4 BENCHMARKS

MIT-States

The MIT-States dataset has 245 object classes, 115 attribute classes and ∼53K images. There is a wide range of objects (e.g., fish, persimmon, room) and attributes (e.g., mossy, deflated, dirty). On average, each object instance is modified by one of the 9 attributes it affords.

83 PAPERS • 4 BENCHMARKS

MSRVTT-QA

The MSR-VTT-QA dataset is a benchmark for the task of Visual Question Answering (VQA) on the MSR-VTT (Microsoft Research Video to Text) dataset. The MSR-VTT-QA benchmark is used to evaluate models on their ability to answer questions based on these videos. It's part of the tasks that this dataset is used for, along with Video Retrieval, Video Captioning, Zero-Shot Video Question Answering, Zero-Shot Video Retrieval, and Text-to-Video Generation.

66 PAPERS • 6 BENCHMARKS

MSVD-QA

The MSVD-QA dataset is a Video Question Answering (VideoQA) dataset. It is based on the existing Microsoft Research Video Description (MSVD) dataset, which consists of about 120K sentences describing more than 2,000 video snippets. In the MSVD-QA dataset, Question-Answer (QA) pairs are generated from these descriptions. The dataset is mainly used in video captioning experiments but due to its large data size, it is also used for VideoQA. It contains 1970 video clips and approximately 50.5K QA pairs.

61 PAPERS • 6 BENCHMARKS

TVQA+

TVQA+ contains 310.8K bounding boxes, linking depicted objects to visual concepts in questions and answers.

58 PAPERS • 3 BENCHMARKS

Oxford-IIIT Pets

The Oxford-IIIT Pet Dataset is a 37-category pet dataset with roughly 200 images for each class. The images have large variations in scale, pose, and lighting. All images have an associated ground truth annotation of breed, head ROI, and pixel-level trimap segmentation.

50 PAPERS • 10 BENCHMARKS

OCNLI (Original Chinese Natural Language Inference)

OCNLI stands for Original Chinese Natural Language Inference. It is corpus for Chinese Natural Language Inference, collected following closely the procedures of MNLI, but with enhanced strategies aiming for more challenging inference pairs. No human/machine translation is used in creating the dataset, and thus the Chinese texts are original and not translated.

43 PAPERS • 3 BENCHMARKS

SUN397

The Scene UNderstanding (SUN) database contains 899 categories and 130,519 images. There are 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition.

40 PAPERS • 8 BENCHMARKS

SUN Attribute

The SUN Attribute dataset consists of 14,340 images from 717 scene categories, and each category is annotated with a taxonomy of 102 discriminate attributes. The dataset can be used for high-level scene understanding and fine-grained scene recognition.

38 PAPERS • 2 BENCHMARKS

How2QA

To collect How2QA for video QA task, the same set of selected video clips are presented to another group of AMT workers for multichoice QA annotation. Each worker is assigned with one video segment and asked to write one question with four answer candidates (one correctand three distractors). Similarly, narrations are hidden from the workers to ensure the collected QA pairs are not biased by subtitles. Similar to TVQA, the start and end points are provided for the relevant moment for each question. After filtering low-quality annotations, the final dataset contains 44,007 QA pairs for 22k 60-second clips selected from 9035 videos.

26 PAPERS • 2 BENCHMARKS

EURLEX57K

EURLEX57K is a new publicly available legal LMTC dataset, dubbed EURLEX57K, containing 57k English EU legislative documents from the EUR-LEX portal, tagged with ∼4.3k labels (concepts) from the European Vocabulary (EUROVOC).

22 PAPERS • 1 BENCHMARK

iVQA (Instructional Video Question Answering)

An open-ended VideoQA benchmark that aims to: i) provide a well-defined evaluation by including five correct answer annotations per question and ii) avoid questions which can be answered without the video.

22 PAPERS • 2 BENCHMARKS

COCO-MLT

The COCO-MLT is created from MS COCO-2017, containing 1,909 images from 80 classes. The maximum of training number per class is 1,128 and the minimum is 6. We use the test set of COCO2017 with 5,000 for evaluation. The ratio of head, medium, and tail classes is 22:33:25 in COCO-MLT.

12 PAPERS • 2 BENCHMARKS

MedConceptsQA

MedConceptsQA - Open Source Medical Concepts QA Benchmark

12 PAPERS • 2 BENCHMARKS

VOC-MLT

We construct the long-tailed version of VOC from its 2012 train-val set. It contains 1,142 images from 20 classes, with a maximum of 775 images per class and a minimum of 4 images per class. The ratio of head, medium, and tail classes after splitting is 6:6:8. We evaluate the performance on VOC2007 test set with 4952 images.

12 PAPERS • 2 BENCHMARKS

LAD (Large-scale Attribute Dataset)

LAD (Large-scale Attribute Dataset) has 78,017 images of 5 super-classes and 230 classes. The image number of LAD is larger than the sum of the four most popular attribute datasets (AwA, CUB, aP/aY and SUN). 359 attributes of visual, semantic and subjective properties are defined and annotated in instance-level.

10 PAPERS • NO BENCHMARKS YET

AO-CLEVr

AO-CLEVr is a new synthetic-images dataset containing images of "easy" Attribute-Object categories, based on the CLEVr. AO-CLEVr has attribute-object pairs created from 8 attributes: { red, purple, yellow, blue, green, cyan, gray, brown } and 3 object shapes {sphere, cube, cylinder}, yielding 24 attribute-object pairs. Each pair consists of 7500 images. Each image has a single object that consists of the attribute-object pair. The object is randomly assigned one of two sizes (small/large), one of two materials (rubber/metallic), a random position, and random lightning according to CLEVr defaults.

6 PAPERS • NO BENCHMARKS YET

Tasty Videos

A collection of 2511 recipes for zero-shot learning, recognition and anticipation.

5 PAPERS • NO BENCHMARKS YET

ImageNet_CN

ImageNet_CN (Chinese ImageNet Classification)

transform the ImageNet-1K classification datatset for Chinese models by translating labels and prompts into Chinese.

3 PAPERS • 1 BENCHMARK

XL-R2R

XL-R2R (Cross-lingual Room-to-Room)

The XL-R2R dataset is built upon the R2R dataset and extends it with Chinese instructions. XL-R2R preserves the same splits as in R2R and thus consists of train, val-seen, and val-unseen splits with both English and Chinese instructions, and test split with English instructions only.

2 PAPERS • NO BENCHMARKS YET

Edge-Map-345C

Edge-Map-345C is a large-scale edge-map dataset including 290,281 edge-maps corresponding to 345 object categories of QuickDraw dataset. In particular, these 345 categories are corresponding to the 345 free-hand sketch categories of Google QuickDraw dataset.

1 PAPER • NO BENCHMARKS YET

GOZ

GOZ (Generic Object ZSL Dataset)

The Generix Object Zero-shot Learning (GOZ) dataset is a benchmark dataset for zero-shot learning.

1 PAPER • NO BENCHMARKS YET

PubChem18

PubChem18 (PubChem 2018)

A.2.1 AN OPEN, LARGE-SCALE DATASET FOR ZERO-SHOT DRUG DISCOVERY DERIVED FROM PUBCHEM We constructed a large public dataset extracted from PubChem (Kim et al., 2019; Preuer et al., 2018), an open chemistry database, and the largest collection of readily available chemical data. We take assays ranging from 2004 to 2018-05. It initially comprises 224,290,250 records of molecule-bioassay activity, corresponding to 2,120,854 unique molecules and 21,003 unique bioassays. We find that some molecule-bioassay pairs have multiple activity records, which may not all agree. We reduce every molecule-bioassay pair to exactly one activity measurement by applying majority voting. Molecule-bioassay pairs with ties are discarded. This step yields our final bioactivity dataset, which features 223,219,241 records of molecule-bioassay activity, corresponding to 2,120,811 unique molecules and 21,002 unique bioassays ranging from AID 1 to AID 1259411. Molecules range up to CID 132472079. The dataset has 3 di

1 PAPER • NO BENCHMARKS YET

Sequence Consistency Evaluation (SCE) tests

Sequence Consistency Evaluation (SCE) consists of a benchmark task for sequence consistency evaluation (SCE).

1 PAPER • NO BENCHMARKS YET

Events classification - Biotech news

A dataset specifically tailored to the biotech news sector, aiming to transcend the limitations of existing benchmarks. This dataset is rich in complex content, comprising various biotech news articles covering various events, thus providing a more nuanced view of information extraction challenges.

0 PAPER • NO BENCHMARKS YET

Datasets

43 dataset results for Zero-Shot Learning