Domain Adaptation

Adaptiope is a domain adaptation dataset with 123 classes in the three domains synthetic, product and real life. One of the main goals of Adaptiope is to offer a clean and well curated set of images for domain adaptation. This was necessary as many other common datasets in the area suffer from label noise and low quality images. Additionally, Adaptiope's class set was chosen in a way that minimizes the overlap with the class set of the commonly used ImageNet pretraining, therefore preventing information leakage in a domain adaptation setup.

Mindboggle is a large publicly available dataset of manually labeled brain MRI. It consists of 101 subjects collected from different sites, with cortical meshes varying from 102K to 185K vertices. Each brain surface contains 25 or 31 manually labeled parcels. Source: Graph Convolutions on Spectral Embeddings: Learning of Cortical Surface Data Image Source: https://mindboggle.info/data.html

I.I.D. hypothesis between training and testing data is the basis of numerous image classification methods. Such property can hardly be guaranteed in practice where the Non-IIDness is common, causing in- stable performances of these models. In literature, however, the Non-I.I.D. image classification problem is largely understudied. A key reason is lacking of a well-designed dataset to support related research. In this paper, we construct and release a Non-I.I.D. image dataset called NICO, which uses contexts to create Non-IIDness consciously. Compared to other datasets, extended analyses prove NICO can support various Non-I.I.D. situations with sufficient flexibility. Meanwhile, we propose a baseline model with Con- vNet structure for General Non-I.I.D. image classification, where distribution of testing data is unknown but different from training data. The experimental results demonstrate that NICO can well support the training of ConvNet model from scratch, and a batch balancing module can help ConvNets to perform better in Non-I.I.D. settings.

Under a close collaboration with an expert radiologist team of the Hospital Universitario San Cecilio, the COVIDGR-1.0 dataset of patients' anonymized X-ray images has been built. 852 images have been collected following a strict labeling protocol. They are categorized into 426 positive cases and 426 negative cases. Positive images correspond to patients who have been tested positive for COVID-19 using RT-PCR within a time span of at most 24h between the X-ray image and the test. Every image has been taken using the same type of equipment and with the same format: only the posterior-anterior view is considered. Source: https://github.com/ari-dasci/covidgr

The Cross-dataset Testbed is a Decaf7 based cross-dataset image classification dataset, which contains 40 categories of images from 3 domains: 3,847 images in Caltech256, 4,000 images in ImageNet, and 2,626 images for SUN. In total there are 10,473 images of 40 categories from these three domains. Source: Probability Weighted Compact Feature for Domain Adaptive Retrieval Image Source: https://sites.google.com/site/crossdataset/

MegaAge is a large dataset that consists of 41,941 faces annotated with age posterior distributions.

The ClonedPerson dataset is a large-scale synthetic person re-identification dataset introduced in the paper "Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification" in CVPR 2022. It is generated by MakeHuman and Unity3D. Characters in this dataset use an automatic approach to directly clone the whole outfits from real-world person images to virtual 3D characters, such that any virtual person thus created will appear very similar to its real-world counterpart. The dataset contains 887,766 synthesized person images of 5,621 identities.

Modern Office-31 is a refurbished version of the commonly used Office-31 dataset. Modern Office-31 rectifies many of the annotation errors and low quality images in the Amazon domain of the original Office-31 dataset. Additionally, this dataset adds another synthetic domain based on the Adaptiope dataset.

Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV, a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions, and enables benchmarking models for image classification, object detection, and 3D pose estimation. In addition to this novel dataset, we contribute extensive experiments using popular baseline methods, which reveal that: 1. Some nuisance factors have a much stronger negative effect on the performance compared to others, also depending on the vision task. 2. Current approaches to enhance robustness have only marginal effects, and can even reduce robustness. 3. We do not observe significant differences between convolutional and transformer architectures. We believe our dataset provides a rich test bed to study robustness and will help push forward research in this area.

Plain Language Adaptation of Biomedical Abstracts (PLABA) is a dataset designed for automatic adaptation that is both document- and sentence-aligned. The dataset contains 750 adapted abstracts, totaling 7643 sentence pairs. Source: A Dataset for Plain Language Adaptation of Biomedical Abstracts Image Source: https://arxiv.org/pdf/2210.12242v1.pdf

Wild-Time is a benchmark of 5 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including patient prognosis and news classification. On these datasets, we systematically benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning. Source: Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time Image Source: https://arxiv.org/pdf/2211.14238v1.pdf

ActorShift is a dataset where the domain shift comes from the change in actor species: we use humans in the source domain and animals in the target domain. This causes large variances in the appearance and motion of activities. For the corresponding dataset we select 1,305 videos of 7 human activity classes from Kinetics-700 as the source domain: sleeping, watching tv, eating, drinking, swimming, running and opening a door. For the target domain we collect 200 videos from YouTube of animals performing the same activities. We divide them into 35 videos for training (5 per class) and 165 for evaluation. The target domain data is scarce, meaning there is the additional challenge of adapting to the target domain with few unlabeled examples.

A dataset for evaluating text classification, domain adaptation, and active learning models. The dataset consists of 22,660 documents (tweets) collected in 2018 and 2019. It spans across four domains: Alzheimer's, Parkinson's, Cancer, and Diabetes.

Libri-Adapt aims to support unsupervised domain adaptation research on speech recognition models. Source: Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation

NLPeer is a multidomain corpus of more than 5k papers and 11k review reports from five different venues. In addition to the new datasets of paper drafts, camera-ready versions and peer reviews from the NLP community, this dataset has a unified data representation, and augment previous peer review datasets to include parsed, structured paper representations, rich metadata and versioning information. Source: NLPeer: A Unified Resource for the Computational Study of Peer Review Image Source: https://arxiv.org/pdf/2211.06651v1.pdf

This dataset contains 114 individuals including 1824 images captured from two disjoint camera views. For each person, eight images are captured from eight different orientations under one camera view and are normalized to 128x48 pixels. This dataset is also split into two parts randomly. One contains 57 individuals for training, and the other contains 57 individuals for testing. Source: https://github.com/charliememory/PKU-Reid-Dataset Image Source: https://arxiv.org/pdf/1605.02464.pdf

RoCoG-v2 (Robot Control Gestures) is a dataset intended to support the study of synthetic-to-real and ground-to-air video domain adaptation. It contains over 100K synthetically-generated videos of human avatars performing gestures from seven (7) classes. It also provides videos of real humans performing the same gestures from both ground and air perspectives

• The Sims4Action Dataset: a videogame-based dataset for Synthetic→Real domain adaptation for human activity recognition. * Goal : Exploring the concept of constructing training examples for Activities of Daily Living (ADL) recognition by playing life simulation video games. * ** Sims4Action dataset is created with the commercial game THE SIMS 4** by executing actions-of-interest within the game in a "top-down" manner. It features ten hours of video material of eight diverse characters and multiple environments. Ten actions are selected to have a direct correspondence to categories covered in the real-life dataset Toyota Smarthome [2] to enable the research of Synthetic→Real transfer in action recognition. * Two benchmarks :* Gaming→Gaming* (training and evaluation on Sims4Action) and Gaming→Real (training on Sims4Action, evaluation on the real Toyota Smarthome data [2]). * Main challenge: Gaming→Real domain adaptation While ADL recognition on gaming data is interesting from a theoretical perspective, the key challenge arises from transferring knowledge learned from simulated data to real-world applications. Sims4Action specifically provides a benchmark for this scenario since it describes a Gaming→Real challenge, which evaluates models on real videos derived from the existing Toyota Smarthome dataset . # References [1] Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games. Alina Roitberg*, David Schneider*, Aulia Djamal, Constantin Seibold, Simon Reiß, Rainer Stiefelhagen, In International Conference on Intelligent Robots and Systems (IROS), 2021 (* denotes equal contribution.) [2] Toyota smarthome: Real-world activities of daily living. Srijan Das, Rui Dai, Michal Koperski, Luca Minciullo, Lorenzo Garattoni, Francois Bremond, Gianpiero Francesca, In International Conference on Computer Vision (ICCV), 2019.

As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, we present the BIOSCAN-5M Insect dataset to the machine learning community. BIOSCAN-5M is a comprehensive dataset containing multi-modal information for over 5 million insect specimens, and it significantly expands existing image-based biological datasets by including taxonomic labels, raw nucleotide barcode sequences, assigned barcode index numbers, geographical information, and specimen size. Every record has both image and DNA data. Each record of the BIOSCAN-5M dataset contains six primary attributes: RGB image DNA barcode sequence Barcode Index Number (BIN) Biological taxonomic classification Geographical information Specimen size

Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains. While Unsupervised Domain Adaptation has been applied to a wide variety of complex vision tasks, only few works focus on lane detection for autonomous driving. This can be attributed to the lack of publicly available datasets. To facilitate research in these directions, we propose CARLANE, a 3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE encompasses the single-target datasets MoLane and TuLane and the multi-target dataset MuLane. These datasets are built from three different domains, which cover diverse scenes and contain a total of 163K unique images, 118K of which are annotated. In addition we evaluate and report systematic baselines, including our own method, which builds upon Prototypical Cross-domain Self-supervised Learning. We find that false positive and false negative rates of the evaluated domain adaptation methods are high compared to those of fully supervised baselines. This affirms the need for benchmarks such as CARLANE to further strengthen research in Unsupervised Domain Adaptation for lane detection. CARLANE, all evaluated models and the corresponding implementations are publicly available at https://carlanebenchmark.github.io.

CocoDoom is a collection of pre-recorded data extracted from Doom gaming sessions along with annotations in the MS Coco format. Source: ResearchDoom and CocoDoom: Learning Computer Vision with Games

The French National Institute of Geographical and Forest Information (IGN) has the mission to document and measure land-cover on French territory and provides referential geographical datasets, including high-resolution aerial images and topographic maps. The monitoring of land-cover plays a crucial role in land management and planning initiatives, which can have significant socio-economic and environmental impact. Together with remote sensing technologies, artificial intelligence (IA) promises to become a powerful tool in determining land-cover and its evolution. IGN is currently exploring the potential of IA in the production of high-resolution land cover maps. Notably, deep learning methods are employed to obtain a semantic segmentation of aerial images. However, territories as large as France imply heterogeneous contexts: variations in landscapes and image acquisition make it challenging to provide uniform, reliable and accurate results across all of France. The FLAIR-one dataset presented is part of the dataset currently used at IGN to establish the French national reference land cover map "Occupation du sol `a grande 'echelle" (OCS- GE). It covers 810 km² and has 13 semantic classes.

Human-Animal-Cartoon (HAC) dataset consists of seven actions (‘sleeping’, ‘watching tv’, ‘eating’, ‘drinking’, ‘swimming’, ‘running’, and ‘opening door’) performed by humans, animals, and cartoon figures, forming three different domains. 3381 video clips are collected from the internet with around 1000 for each domain and three modalities are provided in the dataset: video, audio, and optical flow.

NHA12D is an annotated pavement crack dataset that contains images with different viewpoints and pavements types. This dataset is composed of 80 pavement images, including 40 concrete pavement images and 40 asphalt pavement images, captured by digital survey vehicles on the A12 network in the UK.

We design an all-day semantic segmentation benchmark all-day CityScapes. It is the first semantic segmentation benchmark that contains samples from all-day scenarios, i.e., from dawn to night. Our dataset will be made publicly available at [https://isis-data.science.uva.nl/cv/1ADcityscape.zip].

Caltech Fish Counting Dataset (CFC) is a large-scale dataset for detecting, tracking, and counting fish in sonar videos. This dataset contains over 1,500 videos sourced from seven different sonar cameras.

The Five-Billion-Pixels dataset contains more than 5 billion labeled pixels of 150 high-resolution Gaofen-2 (4 m) satellite images, annotated in a 24-category system covering artificial-constructed, agricultural, and natural classes. It possesses the advantage of rich categories, large coverage, wide distribution, and high-spatial resolution, which well reflects the distributions of real-world ground objects and can benefit to different land cover related studies.

Long-term visual localization provides a benchmark datasets aimed at evaluating 6 DoF pose estimation accuracy over large appearance variations caused by changes in seasonal (summer, winter, spring, etc.) and illumination (dawn, day, sunset, night) conditions. Each dataset consists of a set of reference images, together with their corresponding ground truth poses, and a set of query images. Source: Long-term visual localization Image Source: https://www.visuallocalization.net/

This prostate MRI segmentation dataset is collected from six different data sources. Source: https://github.com/liuquande/SAML

Stanceosaurus is a corpus of 28,033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims. The claims in Stanceosaurus originate from 15 fact-checking sources that cover diverse geographical regions and cultures. Unlike existing stance datasets, it introduces a more fine-grained 5-class labeling strategy with additional subcategories to distinguish implicit stance. Source: Stanceosaurus: Classifying Stance Towards Multilingual Misinformation Image Source: https://arxiv.aorg/pdf/2210.15954v1.pdf

Two datasets (synthetic and natural/real) containing simultaneously recorded egocentric and exocentric videos. Source: From Third Person to First Person: Dataset and Baselines for Synthesis and Retrieval

In our benchmark WHYSHIFT, we explore distribution shifts on 5 real-world tabular datasets from the economic and traffic sectors with natural spatiotemporal distribution shifts.We only pick 7 typical settings out of 22 settings and select only one representative target domain for each setting. In our benchmark, we specify the distribution shift pattern for each setting, and we provide the tools to identify risky regions with large $Y|X$ shifts and to diagnose the performance degradation.

Youtbean is a dataset created from closed captions of YouTube product review videos. It can be used for aspect extraction and sentiment classification. Source: https://arxiv.org/pdf/1708.02420.pdf

A benchmark dataset for training and evaluating global cloud classification models. It consists of one year of 1km resolution MODIS hyperspectral imagery merged with pixel-width 'tracks' of CloudSat cloud labels. Source: Cumulo: A Dataset for Learning Cloud Classes

The DAPlankton dataset consists of over 110k expert-labeled plankton images. The data is divided into two subsets: DAPlankton_LAB and DAPlankton_SEA. DAPlankton_LAB consists of images captured from multiple mono-specific phytoplankton cultures, which were analysed using three different imaging instruments: Imaging FlowCytoBot (IFCB), CytoSense (CS) flow cytometer, and FlowCam (FC) imaging microscope each producing cropped images with one plankton particle in each. An expert further verified the class of each image, ensuring that there was no cross contamination between different cultures. This process resulted in a balanced dataset with negligible label uncertainty. DAPlankton_SEA consists of images captured from water samples collected from the Baltic Sea using two different imaging instruments: IFCB and CS. Each image was manually labeled by an expert. DAPlankton_SEA provides a realistic and more challenging dataset with a large class imbalance and natural intra-class variance.

This is a dataset used to test deep learning-supported deep learning for fault diagnosis: - A digital twin model for a robot. - A synthetic data from the digital twin to train a deep learning-based fault diagnosis model. - A real dataset collected from the real robot to test the sim-to-real performance. Download the dataset from: https://nextcloud.centralesupelec.fr/s/7AR6aamBZNXcRM8/download