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41 dataset results for Few-Shot Learning

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.

13,433 PAPERS • 40 BENCHMARKS

GLUE (General Language Understanding Evaluation benchmark)

General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding tasks, including single-sentence tasks CoLA and SST-2, similarity and paraphrasing tasks MRPC, STS-B and QQP, and natural language inference tasks MNLI, QNLI, RTE and WNLI.

2,708 PAPERS • 25 BENCHMARKS

SST (Stanford Sentiment Treebank)

The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges.

2,015 PAPERS • 9 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.

1,955 PAPERS • 44 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.

1,613 PAPERS • 22 BENCHMARKS

SST-2

1,502 PAPERS • 4 BENCHMARKS

mini-Imagenet

mini-Imagenet is proposed by Matching Networks for One Shot Learning . In NeurIPS, 2016. This dataset consists of 50000 training images and 10000 testing images, evenly distributed across 100 classes.

1,251 PAPERS • 19 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.

1,044 PAPERS • 14 BENCHMARKS

MRPC (Microsoft Research Paraphrase Corpus)

Microsoft Research Paraphrase Corpus (MRPC) is a corpus consists of 5,801 sentence pairs collected from newswire articles. Each pair is labelled if it is a paraphrase or not by human annotators. The whole set is divided into a training subset (4,076 sentence pairs of which 2,753 are paraphrases) and a test subset (1,725 pairs of which 1,147 are paraphrases).

694 PAPERS • 5 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.

633 PAPERS • 4 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.

589 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.

451 PAPERS • 4 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:

418 PAPERS • 7 BENCHMARKS

PASCAL-5i

PASCAL-5i is a dataset used to evaluate few-shot segmentation. The dataset is sub-divided into 4 folds each containing 5 classes. A fold contains labelled samples from 5 classes that are used for evaluating the few-shot learning method. The rest 15 classes are used for training.

150 PAPERS • 1 BENCHMARK

PubMedQA

The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts.

143 PAPERS • 2 BENCHMARKS

Meta-Dataset

The Meta-Dataset benchmark is a large few-shot learning benchmark and consists of multiple datasets of different data distributions. It does not restrict few-shot tasks to have fixed ways and shots, thus representing a more realistic scenario. It consists of 10 datasets from diverse domains:

120 PAPERS • 2 BENCHMARKS

FLEURS

FLEURS (Few-shot Learning Evaluation of Universal Representations of Speech)

We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark, with approximately 12 hours of speech supervision per language. FLEURS can be used for a variety of speech tasks, including Automatic Speech Recognition (ASR), Speech Language Identification (Speech LangID), Translation and Retrieval. In this paper, we provide baselines for the tasks based on multilingual pre-trained models like mSLAM. The goal of FLEURS is to enable speech technology in more languages and catalyze research in low-resource speech understanding.

59 PAPERS • 1 BENCHMARK

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.

34 PAPERS • 5 BENCHMARKS

MR (MR Movie Reviews)

MR Movie Reviews is a dataset for use in sentiment-analysis experiments. Available are collections of movie-review documents labeled with respect to their overall sentiment polarity (positive or negative) or subjective rating (e.g., "two and a half stars") and sentences labeled with respect to their subjectivity status (subjective or objective) or polarity.

27 PAPERS • 3 BENCHMARKS

CaseHOLD

CaseHOLD (Case Holdings On Legal Decisions)

CaseHOLD (Case Holdings On Legal Decisions) is a law dataset comprised of over 53,000+ multiple choice questions to identify the relevant holding of a cited case. This dataset presents a fundamental task to lawyers and is both legally meaningful and difficult from an NLP perspective (F1 of 0.4 with a BiLSTM baseline). The citing context from the judicial decision serves as the prompt for the question. The answer choices are holding statements derived from citations following text in a legal decision. There are five answer choices for each citing text. The correct answer is the holding statement that corresponds to the citing text. The four incorrect answers are other holding statements.

23 PAPERS • 2 BENCHMARKS

Wiki-One

This dataset is a Wikipedia dump, split by relations to perform Few-Shot Knowledge Graph Completion.

15 PAPERS • NO BENCHMARKS YET

Paris-Lille-3D

The Paris-Lille-3D is a Benchmark on Point Cloud Classification. The Point Cloud has been labeled entirely by hand with 50 different classes. The dataset consists of around 2km of Mobile Laser System point cloud acquired in two cities in France (Paris and Lille).

13 PAPERS • 1 BENCHMARK

MedNLI (Medical Natural Language Inference)

The MedNLI dataset consists of the sentence pairs developed by Physicians from the Past Medical History section of MIMIC-III clinical notes annotated for Definitely True, Maybe True and Definitely False. The dataset contains 11,232 training, 1,395 development and 1,422 test instances. This provides a natural language inference task (NLI) grounded in the medical history of patients.

8 PAPERS • 2 BENCHMARKS

ORBIT

ORBIT is a real-world few-shot dataset and benchmark grounded in a real-world application of teachable object recognizers for people who are blind/low vision. The dataset contains 3,822 videos of 486 objects recorded by people who are blind/low-vision on their mobile phones, and the benchmark reflects a realistic, highly challenging recognition problem, providing a rich playground to drive research in robustness to few-shot, high-variation conditions.

7 PAPERS • 2 BENCHMARKS

GINC

GINC (Generative IN-Context learning Dataset)

GINC (Generative In-Context learning Dataset) is a small-scale synthetic dataset for studying in-context learning. The pretraining data is generated by a mixture of HMMs and the in-context learning prompt examples are also generated from HMMs (either from the mixture or not). The prompt examples are out-of-distribution with respect to the pretraining data since every example is independent, concatenated, and separated by delimiters. The GitHub repository provides code to generate GINC-style datasets of varying vocabulary sizes, number of HMMs, and other parameters.

5 PAPERS • NO BENCHMARKS YET

N-Digit MNIST

N-Digit MNIST is a multi-digit MNIST-like dataset.

5 PAPERS • NO BENCHMARKS YET

ExVo2022

ExVo2022 (ICML ExVo 2022 Workshop & Competition Data)

Baseline code for the three tracks of ExVo 2022 competition.

4 PAPERS • NO BENCHMARKS YET

FewSOL (A Dataset for Few-Shot Object Learning in Robotic Environments)

The Few-Shot Object Learning (FewSOL) dataset can be used for object recognition with a few images per object. It contains 336 real-world objects with 9 RGB-D images per object from different views. Object segmentation masks, object poses and object attributes are provided. In addition, synthetic images generated using 330 3D object models are used to augment the dataset. FewSOL dataset can be used to study a set of few-shot object recognition problems such as classification, detection and segmentation, shape reconstruction, pose estimation, keypoint correspondences and attribute recognition.

4 PAPERS • NO BENCHMARKS YET

DOGC

Intended to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies.

3 PAPERS • NO BENCHMARKS YET

F-SIOL-310 (Few-Shot Incremental Object Learning)

F-SIOL-310 is a robotic dataset and benchmark for Few-Shot Incremental Object Learning, which is used to test incremental learning capabilities for robotic vision from a few examples.

3 PAPERS • NO BENCHMARKS YET

N-Omniglot

N-Omniglot is a neuromorphic dataset for few-shot learning. It contains 1,623 categories of handwritten characters, with only 20 samples per class.

3 PAPERS • NO BENCHMARKS YET

BigNews

Contains 3,689,229 English news articles on politics, gathered from 11 United States (US) media outlets covering a broad ideological spectrum.

2 PAPERS • NO BENCHMARKS YET

CareCall

CareCall (CareCall for Seniors)

carecall is a Korean dialogue dataset for role-satisfying dialogue systems. The dataset was composed with a few samples of human-written dialogues using in-context few-shot learning of large-scale LMs. Large-scale LMs can generate dialogues with a specific personality, given a prompt consisting of a brief description of the chatbot’s properties and few dialogue examples. We use this method to build the entire dataset.

2 PAPERS • NO BENCHMARKS YET

FIGR-8

The FIGR-8 database is a dataset containing 17,375 classes of 1,548,256 images representing pictograms, ideograms, icons, emoticons or object or conception depictions. Its aim is to set a benchmark for Few-shot Image Generation tasks, albeit not being limited to it. Each image is represented by 192x192 pixels with grayscale value of 0-255. Classes are not balanced (they do not all contain the same number of elements), but they all do contain at the very least 8 images.

2 PAPERS • NO BENCHMARKS YET

Large COVID-19 CT scan slice dataset

"We built a large lung CT scan dataset for COVID-19 by curating data from 7 public datasets listed in the acknowledgements. These datasets have been publicly used in COVID-19 diagnosis literature and proven their efficiency in deep learning applications. Therefore, the merged dataset is expected to improve the generalization ability of deep learning methods by learning from all these resources together.

2 PAPERS • 2 BENCHMARKS

ToM-in-AMC

ToM-in-AMC is a novel NLP benchmark, Short for Theory-of-Mind meta-learning Assessment with Movie Characters. The benchmark consists of 1,000 parsed movie scripts for this purpose, each corresponding to a few-shot character understanding task.

2 PAPERS • NO BENCHMARKS YET

BankNote-Net

Millions of people around the world have low or no vision. Assistive software applications have been developed for a variety of day-to-day tasks, including currency recognition. To aid with this task, we present BankNote-Net, an open dataset for assistive currency recognition. The dataset consists of a total of 24,816 embeddings of banknote images captured in a variety of assistive scenarios, spanning 17 currencies and 112 denominations. These compliant embeddings were learned using supervised contrastive learning and a MobileNetV2 architecture, and they can be used to train and test specialized downstream models for any currency, including those not covered by our dataset or for which only a few real images per denomination are available (few-shot learning). We deploy a variation of this model for public use in the last version of the Seeing AI app developed by Microsoft, which has over a 100 thousand monthly active users.

1 PAPER • NO BENCHMARKS YET

Bongard-OpenWorld

Bongard-OpenWorld is a new benchmark for evaluating real-world few-shot reasoning for machine vision. We hope it can help us better understand the limitations of current visual intelligence and facilitate future research on visual agents with stronger few-shot visual reasoning capabilities.

1 PAPER • 1 BENCHMARK

FewGLUE_64_labeled

FewGLUE_64_labeled (A new version of FewGLUE with 64 training examples)

Introduction The FewGLUE_64_labeled dataset is a new version of FewGLUE dataset. It contains a 64-sample training set, a development set (the original SuperGLUE development set), a test set, and an unlabeled set. It is constructed to facilitate the research of few-shot learning for natural language understanding tasks.

1 PAPER • NO BENCHMARKS YET

Open MIC (Open Museum Identification Challenge)

Open MIC (Open Museum Identification Challenge) contains photos of exhibits captured in 10 distinct exhibition spaces of several museums which showcase paintings, timepieces, sculptures, glassware, relics, science exhibits, natural history pieces, ceramics, pottery, tools and indigenous crafts. The goal of Open MIC is to stimulate research in domain adaptation, egocentric recognition and few-shot learning by providing a testbed complementary to the famous Office 31.

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

41 dataset results for Few-Shot Learning