Domain Adaptation

The LeukemiaAttri dataset is a large-scale, multi-domain collection of microscopy images derived from leukemia patient samples, enriched with detailed morphological information. This dataset comprises a total of 28.9K images (2.4K × 2 × 3 × 2), which were captured using both low-cost and high-cost microscopes at three different resolutions: 10x, 40x, and 100x, utilizing various cameras. In addition to providing location annotations for each white blood cell (WBC), the dataset includes comprehensive morphological attributes for every WBC, enhancing its utility for research and analysis in the field. | Sr. # | Dataset Name | Microscope | Resolution | Camera | Image Count | WBC Count | Artifacts Count | |-------|---------------|---------------|------------|----------------|-------------|-----------|-----------------| | 1 | H_100x_C1 | Olympus CX23 | 100x | Honor 9X Lite | 2.4k | 7.8k | 2.5k | | 2 | H_100x_C2 | Olympus CX23 | 100x | HD1500T | 2.4k | 7.8k | 2.5k | | 3 | H_40x_C1 | Olympus CX23 | 40x | Honor 9X Lite | 2.4k | 7.8k | 2.5k | | 4 | H_40x_C2 | Olympus CX23 | 40x | HD1500T | 2.4k | 7.8k | 2.5k | | 5 | H_10x_C1 | Olympus CX23 | 10x | Honor 9X Lite | 2.4k | 7.8k | 2.5k | | 6 | H_10x_C2 | Olympus CX23 | 10x | HD1500T | 2.4k | 7.8k | 2.5k | | 7 | L_100x_C1 | XSZ_107BN | 100x | Honor 9X Lite | 2.4k | 4.8k | 1.1k | | 8 | L_100x_C2 | XSZ_107BN | 100x | ZZCAT-5MP | 2.4k | 4.8k | 1.1k | | 9 | L_40x_C1 | XSZ_107BN | 40x | Honor 9X Lite | 2.4k | 7.8k | 2.5k | | 10 | L_40x_C2 | XSZ_107BN | 40x | ZZCAT-5MP | 2.4k | 7.8k | 2.5k | | 11 | L_10x_C1 | XSZ_107BN | 10x | Honor 9X Lite | 2.4k | 7.8k | 2.5k | | 12 | L_10x_C2 | XSZ_107BN | 10x | ZZCAT-5MP | 2.4k | 7.8k | 2.5k | | | Total Count | | | | 29k | 88.2k | 27.8k |

Mila Simulated Floods Dataset is a 1.5 square km virtual world using the Unity3D game engine including urban, suburban and rural areas. The urban environment contains skyscrapers, large buildings, and roads, as well as objects such as traffic items and vehicles. The rural environment consists of a landscape of grassy hills, forests, and mountains, with sparse houses and other buildings such as a church, and no roads. The rural and urban areas make up for 1 square km of our virtual world. The suburban environment is a residential area of 0.5 square km with many individual houses with front yards. To gather the simulated dataset, we captured before and after flood pairs from 2000 viewpoints with the following modalities: - non-flooded RGB image, depth map, segmentation map - flooded RGB image, binary mask of the flooded area, segmentation map The camera was placed about 1.5 above ground, and has a field of view of 120 degree, and the resolution of the images is 1200 x 900. At each viewpoint, we took 10 pictures, by varying slightly the position of the camera in order to augment the dataset.

• 5 domains: synthetic domain, document domain, street view domain, handwritten domain, and car license domain * over five million images

Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. The Pano3D 360 depth estimation benchmark provides a standard Matterport3D train and test split, as well as a secondary GibsonV2 partioning for testing and training as well. The latter is used for zero-shot cross dataset transfer performance assessment and decomposes it into 3 different splits, each one focusing on a specific generalization axis.

Our proposed Synthetic-to-Real benchmark for more practical visual DA (termed S2RDA) includes two challenging transfer tasks of S2RDA-49 and S2RDA-MS-39. In each task, source/synthetic domain samples are synthesized by rendering 3D models from ShapeNet. The used 3D models are in the same label space as the target/real domain and each class has 12K rendered RGB images. The real domain of S2RDA-49 comprises 60,535 images of 49 classes, collected from ImageNet validation set, ObjectNet, VisDA-2017 validation set, and the web. For S2RDA-MS-39, the real domain collects 41,735 natural images exclusive for 39 classes from MetaShift, which contain complex and distinct contexts, e.g., object presence (co-occurrence of different objects), general contexts (indoor or outdoor), and object attributes (color or shape), leading to a much harder task. Compared to VisDA-2017, our S2RDA contains more categories, more realistically synthesized source domain data coming for free, and more complicated target domain data collected from diverse real-world sources, setting a more practical, challenging benchmark for future DA research.

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. Source: https://github.com/zzxslp/Crosslingual-VLN

The Apron Dataset focuses on training and evaluating classification and detection models for airport-apron logistics. In addition to bounding boxes and object categories the dataset is enriched with meta parameters to quantify the models’ robustness against environmental influences.

Microarray gene expression data on 57 bladder samples from 5 batches.

The goal of this project is to present two new datasets that seek to expand the capability of the Learning to See in the Dark Low-light enhancement CNN for the Canon 6D DSLR, and explore how the network performs when modified in various ways, both pruning it and making it deeper. The original paper Learning to See in the Dark was published in CVPR 2018, by Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun.

CFC-DAOD is a domain adaptation extension to the Caltech Fish Counting domain generalization benchmark. The goal is cross-domain object detection of a single class, "fish", in sonar videos. The source domain consists of data from one river, Kenai, and the target domain consists of data from another out-of-domain river, Channel. CFC-DAOD introduces new data from the target domain to be used for unsupervised domain adaptive object detection: 168k bounding box annotations in 29k frames sampled from 150 new videos captured over two days from 3 different sensors on the Channel river.

The DADE dataset, short for Driving Agents in Dynamic Environments, is a synthetic dataset designed for the training and evaluation of methods for the task of semantic segmentation in the context of autonomous driving agents navigating dynamic environments and weather conditions. This dataset was generated using the CARLA simulator (version 0.9.14) to provide perfect sensor synchronization and calibration, as well as precise semantic segmentation ground truths. All data were collected within the Town12 map in CARLA. DADE dataset is divided into two sub-datasets. For both subsets, each sequence is acquired by one agent (one ego vehicle) running for some time within a 5-hour time frame, amounting to a total of 990k frames for the entire dataset. The agents travel various locations such as forest, countryside, rural farmland, highway, low density residential, community buildings, and high density residential.

An adaption of the MVTec Anomaly Detection dataset, presented in the paper "Domain-independent detection of known anomalies". There are three different datasets, each covering one specific anomaly type: color, cut and hole. The datasets can be used to evaluate approaches on the hybrid task of detecting known anomalies across different, previously unseen objects: All object types except one are used for training. During testing, the images of the remaining object type should be classified on whether they contain an anomaly. Note: the authors are not affiliated with MVTec

The DRIFT dataset includes 25k image patches collected in five European countries sourced from aerial and nanosatellite image archives. Each image patch is associated with three target variables to predict: 1. Canopy height: average height value for pixels containing woody vegetation. 2. Tree count: number of overstory (visible from an overhead perspective) trees in the images. 3. Tree cover fraction: percentage of the image being covered by overstory tree crowns. The DRIFT dataset includes significant shifts between label and visual distributions due to sensor and area differences. Furthermore, vegetation tends to grow to fit the local climate, therefore introducing concept drift in the data: same tree species may appear differently in different subsets. The label distribution also varies among different subsets (countries). The dataset is a good choice for: * image-level regression * domain adaption for regression * remote sensing for forest applications

Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real- world scenarios. One representative of such challenging scenarios is to deploy a translation system for a conference with a specific topic, e.g., global warming or coronavirus, where there are usually extremely less resources due to the limited schedule. To motivate wider investigation in such a scenario, we present a real-world fine-grained domain adaptation task in machine translation (FGraDA). The FGraDA dataset consists of Chinese-English translation task for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. Each sub-domain is equipped with a development set and test set for evaluation pur- poses. To be closer to reality, FGraDA does not employ any in-domain bilingual training data but provides bilingual dictionaries and wiki knowledge base, which can be easier obtained within a short time. We benchmark the fine-grained domain adaptation task and present in-depth analyses showing that there are still challenging problems to further improve the performance with heterogeneous resources.

InfraParis is a novel and versatile dataset supporting multiple tasks across three modalities: RGB, depth, and infrared. From the city to the suburbs, it contains a variety of styles in different areas of the greater Paris area, providing rich semantic information. InfraParis contains 7301 images with bounding boxes and full semantic (19 classes) annotations. We assess various state-of-the-art baseline techniques, encompassing models for the tasks of semantic segmentation, object detection, and depth estimation.

LiDAR-CS is a dataset for 3D object detection in real traffic. It contains 84,000 point cloud frames under 6 groups of different sensors but with same corresponding scenarios, captured from hybrid realistic LivDAR simulator. Source: LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection Image Source: https://arxiv.org/pdf/2301.12515v1.pdf

Dataset release for the BMVC 2021 Paper "Few-Shot Domain Adaptation for Low Light RAW Image Enhancement" Abstract: Enhancing practical low light raw images is a difficult task due to severe noise and color distortions from short exposure time and limited illumination. Despite the success of existing Convolutional Neural Network (CNN) based methods, their performance is not adaptable to different camera domains. In addition, such methods also require large datasets with short-exposure and corresponding long-exposure ground truth raw images for each camera domain, which is tedious to compile. To address this issue, we present a novel few-shot domain adaptation method to utilize the existing source camera labeled data with few labeled samples from the target camera to improve the target domain’s enhancement quality in extreme low-light imaging. Our experiments show that only ten or fewer labeled samples from the target camera domain are sufficient to achieve similar or better enhancement performance than training a model with a large labeled target camera dataset. To support research in this direction, we also present a new low-light raw image dataset captured with a Nikon camera, comprising short-exposure and their corresponding long-exposure ground truth images. The code is available at https://val.cds.iisc.ac.in/HDR/BMVC21/index.html.

A cross-city UDA benchmark built upon nuScenes.

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. Source: Museum Exhibit Identification Challenge for Domain Adaptation and Beyond

OpenGDA is a benchmark for evaluating graph domain adaptation models. It provides abundant pre-processed and unified datasets for different types of tasks (node, edge, graph). They originate from diverse scenarios, covering web information systems, urban systems and natural systems. Furthermore, it integrates state-of-the-art models with standardized and end-to-end pipelines. Overall, OpenGDA provides a user-friendly, scalable and reproducible benchmark

We create Rwanda built-up regions dataset, a different and versatile in nature from previously available datasets. The varying structure size and formation, irregular patterns of construction, buildings in forests and deserts, and the existence of mud houses make it very challenging. A total of 787 satellite images of size 256 × 256 are collected at a high resolution (HR) of 1.193 meters per pixel and hand tagged for built-up region segmentation using an online tool Label-Box.

SemanticUSL is a dataset for domain adaptation for LiDAR point cloud semantic segmentation. The dataset has the same data format and ontology as SemanticKITTI. Source: LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation Image Source: https://unmannedlab.github.io/semanticusl

This dataset is a large-scale synthetic dataset to simulate the attack scenario for a keystroke inference attack. Source: https://arxiv.org/abs/2009.05796

UDA-CH contains 16 objects that cover a variety of artworks which can be found in a museum like sculptures, paintings and books. Specifically, the dataset has been collected inside the cultural site “Galleria Regionale di Palazzo Bellomo” located in Siracusa, Italy. Source: An Unsupervised Domain Adaptation Scheme for Single-Stage Artwork Recognition in Cultural Sites

The Medical Translation Task of WMT 2014 addresses the problem of domain-specific and genre-specific machine translation. The task is split into two subtasks: summary translation, focused on translation of sentences from summaries of medical articles, and query translation, focused on translation of queries entered by users into medical information search engines. Both subtasks included translation between English and Czech, German, and French, in both directions. Source: https://www.aclweb.org/anthology/W14-3302.pdf

The Tufts fNIRS to Mental Workload (fNIRS2MW) open-access dataset is a new dataset for building machine learning classifiers that can consume a short window (30 seconds) of multivariate fNIRS recordings and predict the mental workload intensity of the user during that window. You can use this dataset for tasks like - time series classification using sliding windows - domain adaptation or domain generalization (how well does your classifier generalize to a new subject?) - fairness of time series classifiers (does performance of your classifier vary by subject race or gender?) Useful Links: * Project Website (and data download links): https://tufts-hci-lab.github.io/code_and_datasets/fNIRS2MW.html * Code for benchmarks: https://github.com/tufts-ml/fNIRS-mental-workload-classifiers * DataSheet documentation: https://github.com/tufts-ml/fNIRS-mental-workload-classifiers/blob/main/Datasheet-Tufts-fNIRS2MW.pdf * Academic Paper (published at NeurIPS Datasets & Benchmarks '21) https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/bd686fd640be98efaae0091fa301e613-Paper-round2.pdf ## Motivation We are interested in building brain computer interfaces (BCIs) that would help out everyday computer users working at a desktop or laptop. In our target future use case, a user would actively use a keyboard and mouse as usual, but also wear a non-intrusive headband sensor that would passively provide real-time measurements of brain activity to the computer. Based on moment-to-moment estimates of mental workload, the computer could adjust the interface to support the user. Functional near-infrared spectroscopy (fNIRS) is a promising sensor technology for achieving this goal of "everyday BCI", compared to alternatives like EEG or fMRI. We have developed a prototype fNIRS probe mounted on a headband that we used to collect this dataset (see our paper for details). # Dataset Overview For a complete dataset summary, see our public DataSheet PDF For each participant (68 recommended; 87 total), the dataset contains the following records obtained during one 30-60 minute experimental session. Each subject contributes just over 21 minutes of fNIRS data from the desired n-back experimental conditions, with remaining time related to rest or instruction periods. - fNIRS recordings - Multivariate (D=8) time-series representing brain activity throughout the session, recorded by a sensor probe placed on the forehead and secured via headband - All measurements are recorded at a regular sampling rate of 5.2 Hz. - At each timestep, we record 8 real-valued measurements, one for each combination of - 2 blood chemical concentration changes (oxygenated hemoglobin and deoxygenated hemoglobin) - 2 optical data types used for the measurement (intensity and phase) - 2 spatial locations on the forehead. - The units of each measurement are micro-moles of (oxy-/deoxy-)hemoglobin per liter of tissue. - Activity labels - Annotations of the experimental task activity the subject performed throughout the session, including instruction, rest, and active experiment segments. - We label each segment of the active experiment as one of four possible n-back working memory intensity levels (0-back, 1-back, 2-back, or 3-back). Increased intensity levels are intended to induce an increased level of cognitive workload. - For all experiments reported in the paper, we focus on a binary task (0 vs 2 back) - Demographics - The participant’s age, gender, race, handedness, and other attributes. This lets us measure and audit performance by subpopulation (e.g. how does the classifier perform on white subjects vs. black subjects). ## Publications The Tufts fNIRS Mental Workload Dataset & Benchmark for Brain-Computer Interfaces that Generalize Zhe Huang, Liang Wang, Giles Blaney, Christopher Slaughter, Devon McKeon, Ziyu Zhou, Robert Jacob, and Michael C. Hughes To appear in the Proceedings of Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks , 2021 Link to Paper PDF: <a href="https://openreview.net/pdf?id=QzNHE7QHhut">https://openreview.net/pdf?id=QzNHE7QHhut</a>