Transfer Learning

Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void). The dataset consists of around 5000 fine annotated images and 20000 coarse annotated ones. Data was captured in 50 cities during several months, daytimes, and good weather conditions. It was originally recorded as video so the frames were manually selected to have the following features: large number of dynamic objects, varying scene layout, and varying background. Source: A Review on Deep Learning Techniques Applied to Semantic Segmentation Image Source: https://www.cityscapes-dataset.com/dataset-overview/

KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist. Source: A Review on Deep Learning Techniques Applied to Semantic Segmentation Image Source: http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d

CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age. Source: Show, Attend and Translate: Unpaired Multi-Domain Image-to-Image Translation with Visual Attention Image Source: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

MMLU (Massive Multitask Language Understanding) is a new benchmark designed to measure knowledge acquired during pretraining by evaluating models exclusively in zero-shot and few-shot settings. This makes the benchmark more challenging and more similar to how we evaluate humans. The benchmark covers 57 subjects across STEM, the humanities, the social sciences, and more. It ranges in difficulty from an elementary level to an advanced professional level, and it tests both world knowledge and problem solving ability. Subjects range from traditional areas, such as mathematics and history, to more specialized areas like law and ethics. The granularity and breadth of the subjects makes the benchmark ideal for identifying a model’s blind spots. Image source: https://arxiv.org/pdf/2009.03300v3.pdf

Office-Home is a benchmark dataset for domain adaptation which contains 4 domains where each domain consists of 65 categories. The four domains are: Art – artistic images in the form of sketches, paintings, ornamentation, etc.; Clipart – collection of clipart images; Product – images of objects without a background and Real-World – images of objects captured with a regular camera. It contains 15,500 images, with an average of around 70 images per class and a maximum of 99 images in a class. Source: Multi-component Image Translation for Deep Domain Generalization Image Source: Wen et al

The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. It features: * 1449 densely labeled pairs of aligned RGB and depth images * 464 new scenes taken from 3 cities * 407,024 new unlabeled frames * Each object is labeled with a class and an instance number. The dataset has several components: * Labeled: A subset of the video data accompanied by dense multi-class labels. This data has also been preprocessed to fill in missing depth labels. * Raw: The raw RGB, depth and accelerometer data as provided by the Kinect. * Toolbox: Useful functions for manipulating the data and labels. Source: https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html Image Source: https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html

BIG-Bench Hard (BBH) is a subset of the BIG-Bench, a diverse evaluation suite for language models. BBH focuses on a suite of 23 challenging tasks from BIG-Bench that were found to be beyond the capabilities of current language models. These tasks are ones where prior language model evaluations did not outperform the average human-rater. The BBH tasks require multi-step reasoning, and it was found that few-shot prompting without Chain-of-Thought (CoT), as done in the BIG-Bench evaluations, substantially underestimates the best performance and capabilities of language models. When CoT prompting was applied to BBH tasks, it enabled PaLM to surpass the average human-rater performance on 10 of the 23 tasks, and Codex to surpass the average human-rater performance on 17 of the 23 tasks.

The Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to probe large language models and extrapolate their future capabilities. Big-bench include more than 200 tasks. Image source: https://arxiv.org/pdf/2206.04615.pdf

The UTKFace dataset is a large-scale face dataset with long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variation in pose, facial expression, illumination, occlusion, resolution, etc. This dataset could be used on a variety of tasks, e.g., face detection, age estimation, age progression/regression, landmark localization, etc. Source: https://susanqq.github.io/UTKFace/ Image Source: https://susanqq.github.io/UTKFace/

ChestX-ray14 is a medical imaging dataset which comprises 112,120 frontal-view X-ray images of 30,805 (collected from the year of 1992 to 2015) unique patients with the text-mined fourteen common disease labels, mined from the text radiological reports via NLP techniques. It expands on ChestX-ray8 by adding six additional thorax diseases: Edema, Emphysema, Fibrosis, Pleural Thickening and Hernia. Source: https://nihcc.app.box.com/v/ChestXray-NIHCC/file/220660789610 Image Source: https://nihcc.app.box.com/v/ChestXray-NIHCC

Clotho is an audio captioning dataset, consisting of 4981 audio samples, and each audio sample has five captions (a total of 24 905 captions). Audio samples are of 15 to 30 s duration and captions are eight to 20 words long. Source: https://zenodo.org/record/3490684 Image Source: https://arxiv.org/abs/1910.09387

A new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Source: Aligning AI With Shared Human Values

Aff-Wild2 is a large-scale in-the-wild database and an extension of the Aff-Wild dataset for affect recognition. It approximately doubles the number of included video frames and the number of subjects; thus, improving the variability of the included behaviors and of the involved persons. It is the only existing in-the-wild database with annotations for all 3 main behaviour tasks. The Aff-Wild2 is annotated in a per frame basis for the seven basic expressions (i.e., happiness, surprise, anger, disgust, fear, sadness and the neutral state), twelve action units (AUs 1,2,4,6,7,10,12,15,23,24,25, 26) and valence and arousal. In total Aff-Wild2 consists of 564 videos of around 2.8M frames with 554 subjects. Aff-Wild2 displays a big diversity in terms of subjects' ages, ethnicities and nationalities; it has also great variations and diversities of environments. Sources: 1) Expression, affect, action unit recognition: Aff-wild2, multi-task learning and arcface; 2) The 6th affective behavior analysis in-the-wild (abaw) competition

For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. Hypersim is a photorealistic synthetic dataset for holistic indoor scene understanding. It contains 77,400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry. Source: https://github.com/apple/ml-hypersim Image Source: https://github.com/apple/ml-hypersim

Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems. The same 250 problems from GSM8K are each translated via human annotators in 10 languages. GSM8K (Grade School Math 8K) is a dataset of 8.5K high-quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.

A dataset for robot navigation task and more. The data is collected in photo-realistic simulation environments in the presence of various light conditions, weather and moving objects. Source: TartanAir: A Dataset to Push the Limits of Visual SLAM

KP20k is a large-scale scholarly articles dataset with 528K articles for training, 20K articles for validation and 20K articles for testing. Source: Keyphrase Prediction With Pre-trained Language Model Image Source: https://arxiv.org/pdf/1704.06879.pdf

QM9 provides quantum chemical properties (at DFT level) for a relevant, consistent, and comprehensive chemical space of small organic molecules. This database may serve the benchmarking of existing methods, development of new methods, such as hybrid quantum mechanics/machine learning, and systematic identification of structure-property relationships. Source: QM9 Dataset Image Source: https://pubs.acs.org/doi/pdf/10.1021/ci300415d

An open-source simulated benchmark for meta-reinforcement learning and multi-task learning consisting of 50 distinct robotic manipulation tasks. Source: Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

CELEX database comprises three different searchable lexical databases, Dutch, English and German. The lexical data contained in each database is divided into five categories: orthography, phonology, morphology, syntax (word class) and word frequency. Source: Polysemy and Brevity versus Frequency in Language Image Source: https://www.aclweb.org/anthology/W17-7619.pdf

The Wireframe dataset consists of 5,462 images (5,000 for training, 462 for test) of indoor and outdoor man-made scenes. Source: MCMLSD: A Probabilistic Algorithm and Evaluation Framework for Line Segment Detection Image Source: https://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Learning_to_Parse_CVPR_2018_paper.pdf

KuaiRand is an unbiased sequential recommendation dataset collected from the recommendation logs of the video-sharing mobile app, Kuaishou (快手). It is the first recommendation dataset with millions of intervened interactions of randomly exposed items inserted in the standard recommendation feeds!

THCHS-30 is a free Chinese speech database THCHS-30 that can be used to build a full-fledged Chinese speech recognition system. Source: THCHS-30 : A Free Chinese Speech Corpus

A new multitask action quality assessment (AQA) dataset, the largest to date, comprising of more than 1600 diving samples; contains detailed annotations for fine-grained action recognition, commentary generation, and estimating the AQA score. Videos from multiple angles provided wherever available. Source: What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

CH-SIMS is a Chinese single- and multimodal sentiment analysis dataset which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis. Source: CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotations of Modality

WIDER is a dataset for complex event recognition from static images. As of v0.1, it contains 61 event categories and around 50574 images annotated with event class labels.

CAL500 (Computer Audition Lab 500) is a dataset aimed for evaluation of music information retrieval systems. It consists of 502 songs picked from western popular music. The audio is represented as a time series of the first 13 Mel-frequency cepstral coefficients (and their first and second derivatives) extracted by sliding a 12 ms half-overlapping short-time window over the waveform of each song. Each song has been annotated by at least 3 people with 135 musically-relevant concepts spanning six semantic categories: * 29 instruments were annotated as present in the song or not, * 22 vocal characteristics were annotated as relevant to the singer or not, * 36 genres, * 18 emotions were rated on a scale from one to three (e.g., not happy", neutral", ``happy"), * 15 song concepts describing the acoustic qualities of the song, artist and recording (e.g., tempo, energy, sound quality), * 15 usage terms (e.g., "I would listen to this song while driving, sleeping, etc."). Source: http://calab1.ucsd.edu/~datasets/cal500/details_cal500.txt Audio Source: http://calab1.ucsd.edu/~datasets/cal500/cal500data/

Consists of 1106 action samples from seven actions with quality scores as measured by expert human judges. Source: Action Quality Assessment Across Multiple Actions

A suite of open-source federated datasets, a rigorous evaluation framework, and a set of reference implementations, all geared towards capturing the obstacles and intricacies of practical federated environments. Source: LEAF: A Benchmark for Federated Settings

JuICe is a corpus of 1.5 million examples with a curated test set of 3.7K instances based on online programming assignments. Compared with existing contextual code generation datasets, JuICe provides refined human-curated data, open-domain code, and an order of magnitude more training data. Source: JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation

SemArt is a multi-modal dataset for semantic art understanding. SemArt is a collection of fine-art painting images in which each image is associated to a number of attributes and a textual artistic comment, such as those that appear in art catalogues or museum collections. It contains 21,384 samples that provides artistic comments along with fine-art paintings and their attributes for studying semantic art understanding. Source: How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval

A corpus of real-world texts. Source: Discourse Coherence in the Wild: A Dataset, Evaluation and Methods

FSDKaggle2018 is an audio dataset containing 11,073 audio files annotated with 41 labels of the AudioSet Ontology. FSDKaggle2018 has been used for the DCASE Challenge 2018 Task 2. All audio samples are gathered from Freesound and are provided as uncompressed PCM 16 bit, 44.1 kHz mono audio files. The 41 categories of the AudioSet Ontology are: "Acoustic_guitar", "Applause", "Bark", "Bass_drum", "Burping_or_eructation", "Bus", "Cello", "Chime", "Clarinet", "Computer_keyboard", "Cough", "Cowbell", "Double_bass", "Drawer_open_or_close", "Electric_piano", "Fart", "Finger_snapping", "Fireworks", "Flute", "Glockenspiel", "Gong", "Gunshot_or_gunfire", "Harmonica", "Hi-hat", "Keys_jangling", "Knock", "Laughter", "Meow", "Microwave_oven", "Oboe", "Saxophone", "Scissors", "Shatter", "Snare_drum", "Squeak", "Tambourine", "Tearing", "Telephone", "Trumpet", "Violin_or_fiddle", "Writing". Source: https://zenodo.org/record/2552860 Image Source: https://labs.freesound.org/datasets/

The CropAndWeed dataset is focused on the fine-grained identification of 74 relevant crop and weed species with a strong emphasis on data variability. Annotations of labeled bounding boxes, semantic masks and stem positions are provided for about 112k instances in more than 8k high-resolution images of both real-world agricultural sites and specifically cultivated outdoor plots of rare weed types. Additionally, each sample is enriched with meta-annotations regarding environmental conditions.

SkillSpan is a dataset for Skill Extraction (SE). It is an important and widely-studied task useful to gain insights into labor market dynamics. However, there is a lacuna of datasets and annotation guidelines; available datasets are few and contain crowd-sourced labels on the span-level or labels from a predefined skill inventory. To address this gap, the authors introduce SkillSpan, a novel SE dataset consisting of 14.5K sentences and over 12.5K annotated spans.

Moral Foundations Reddit Corpus (MFRC) is a collection of 16,123 Reddit comments that have been curated from 12 distinct subreddits, hand-annotated by at least three trained annotators for 8 categories of moral sentiment (i.e., Care, Proportionality, Equality, Purity, Authority, Loyalty, Thin Morality, Implicit/Explicit Morality) based on the updated Moral Foundations Theory (MFT) framework. Source: The Moral Foundations Reddit Corpus Image Source: https://arxiv.org/pdf/2208.05545v2.pdf