Domain Adaptation

VeRi-776 is a vehicle re-identification dataset which contains 49,357 images of 776 vehicles from 20 cameras. The dataset is collected in the real traffic scenario, which is close to the setting of CityFlow. The dataset contains bounding boxes, types, colors and brands. Source: VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification Image Source: https://vehiclereid.github.io/VeRi/

Dark Zurich is an image dataset containing a total of 8779 images captured at nighttime, twilight, and daytime, along with the respective GPS coordinates of the camera for each image. These GPS annotations are used to construct cross-time-of-day correspondences, i.e., to match each nighttime or twilight image to its daytime counterpart. These attributes allow the usage of Dark Zurich as a dataset to build models and systems that perform: 1) domain adaptation (unsupervised, weakly supervised or semi-supervised), e.g. for semantic segmentation or object detection, 2) image translation / style transfer to different times of day, 3) robust image matching / visual localization across diverse domains, and 4) other visual perception tasks that are central for autonomous vehicles and other robotic applications.

The Machine Translation of Noisy Text (MTNT) dataset is a Machine Translation dataset that consists of noisy comments on Reddit and professionally sourced translation. The translation are between French, Japanese and French, with between 7k and 37k sentence per language pair. Source: https://arxiv.org/abs/1809.00388 Image Source: https://github.com/pmichel31415/mtnt

WildDash is a benchmark evaluation method is presented that uses the meta-information to calculate the robustness of a given algorithm with respect to the individual hazards. Source: WildDash - Creating Hazard-Aware Benchmarks Image Source: https://wilddash.cc/

Synscapes is a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validation as well as new types of analysis. Source: Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing Image Source: https://7dlabs.com/synscapes-overview

The Oxford RobotCar Dataset contains over 100 repetitions of a consistent route through Oxford, UK, captured over a period of over a year. The dataset captures many different combinations of weather, traffic and pedestrians, along with longer term changes such as construction and roadworks. Source: Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset

This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014. This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

V2V4Real is a large-scale real-world multi-modal dataset for V2V perception. The data is collected by two vehicles equipped with multi modal sensors driving together through diverse scenarios. It covers a driving area of 410 km comprising 20K LiDAR frames, 40K RGB frames, 240K annotated 3D bounding boxes for 5 classes, and HDMaps that cover all the driving routes. Source: V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception Image Source: https://arxiv.org/pdf/2303.07601v1.pdf

The LM (Linemod) dataset is a valuable resource introduced by Stefan Hinterstoisser and colleagues in their research on model-based training, detection, and pose estimation of texture-less 3D objects in heavily cluttered scenes¹. Let's delve into the details: 1. Purpose and Context: - The primary goal of the LM dataset is to facilitate the development and evaluation of methods for detecting and estimating the 6 degrees-of-freedom pose of texture-less 3D objects. - It specifically targets scenarios where objects lack distinctive textures and are embedded in complex backgrounds with occlusions. 2. Methodology: - The dataset builds upon the LINEMOD approach, which combines depth and color information to create templates representing different views of an object. - LINEMOD templates are learned from 3D models and serve as a basis for object detection. - The initial LINEMOD method had limitations, including online template learning and approximate pose estimation. 3. Improvements and Contributions: - Hinterstoisser et al. enhance LINEMOD by incorporating accurate 3D models of objects. - Their approach leverages the 3D model to address the shortcomings of the original LINEMOD. - Notable improvements include better pose estimation and reduced false positives. - The proposed framework is suitable for robotics applications, such as object manipulation. 4. Dataset Details: - The LM dataset consists of 15 registered video sequences, each containing over 1100 frames. - These sequences feature 15 different texture-less household objects. - Objects in the dataset exhibit discriminative color, shape, and size characteristics. - Researchers can use this dataset to evaluate and compare their methods for object detection and pose estimation. LM Dataset Example In summary, the LM dataset provides a valuable benchmark for advancing the field of 6D object pose estimation, especially in scenarios where texture information is limited¹². Researchers can access this dataset to test and refine their algorithms, ultimately contributing to advancements in robotics and machine vision. (1) Model Based Training, Detection and Pose ... - Stefan HINTERSTOISSER. http://stefan-hinterstoisser.com/papers/hinterstoisser2012accv.pdf. (2) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/. (3) paroj/linemod_dataset: Hinterstoisser et al. ACCV12 dataset - GitHub. https://github.com/paroj/linemod_dataset.

VLCS is a dataset to test for domain generalization.

ImageNet-P consists of noise, blur, weather, and digital distortions. The dataset has validation perturbations; has difficulty levels; has CIFAR-10, Tiny ImageNet, ImageNet 64 × 64, standard, and Inception-sized editions; and has been designed for benchmarking not training networks. ImageNet-P departs from ImageNet-C by having perturbation sequences generated from each ImageNet validation image. Each sequence contains more than 30 frames, so to counteract an increase in dataset size and evaluation time only 10 common perturbations are used. Source: Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

The goal of NICO Challenge is to facilitate the OOD (Out-of-Distribution) generalization in visual recognition through promoting the research on the intrinsic learning mechanisms with native invariance and generalization ability. The training data is a mixture of several observed contexts while the test data is composed of unseen contexts. Participants are tasked with developing reliable algorithms across different contexts (domains) to improve the generalization ability of models.

We introduce ACDC, the Adverse Conditions Dataset with Correspondences for training and testing semantic segmentation methods on adverse visual conditions. It comprises a large set of 4006 images which are evenly distributed between fog, nighttime, rain, and snow. Each adverse-condition image comes with a high-quality fine pixel-level semantic annotation, a corresponding image of the same scene taken under normal conditions and a binary mask that distinguishes between intra-image regions of clear and uncertain semantic content. ACDC supports two tasks: 1. standard semantic segmentation 2. uncertainty-aware semantic segmentation

AVD focuses on simulating robotic vision tasks in everyday indoor environments using real imagery. The dataset includes 20,000+ RGB-D images and 50,000+ 2D bounding boxes of object instances densely captured in 9 unique scenes. Source: A Dataset for Developing and Benchmarking Active Vision

Comic2k is a dataset used for cross-domain object detection which contains 2k comic images with image and instance-level annotations. Image Source: https://naoto0804.github.io/cross_domain_detection/

TECHQA is a domain-adaptation question answering dataset for the technical support domain. The TECHQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size – 600 training, 310 dev, and 490 evaluation question/answer pairs – thus reflecting the cost of creating large labeled datasets with actual data. Consequently, TECHQA is meant to stimulate research in domain adaptation rather than being a resource to build QA systems from scratch. The dataset was obtained by crawling the IBM Developer and IBM DeveloperWorks forums for questions with accepted answers that appear in a published IBM Technote—a technical document that addresses a specific technical issue. Source: The TechQA Dataset

KdConv is a Chinese multi-domain Knowledge-driven Conversation dataset, grounding the topics in multi-turn conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer learning and domain adaptation. Source: KdConv

Our goal is to improve upon the status quo for designing image classification models trained in one domain that perform well on images from another domain. Complementing existing work in robustness testing, we introduce the first test dataset for this purpose which comes from an authentic use case where photographers wanted to learn about the content in their images. We built a new test set using 8,900 images taken by people who are blind for which we collected metadata to indicate the presence versus absence of 200 ImageNet object categories. We call this dataset VizWiz-Classification.

Animal-Pose Dataset is an animal pose dataset to facilitate training and evaluation. This dataset provides animal pose annotations on five categories are provided: dog, cat, cow, horse, sheep, with in total 6,000+ instances in 4,000+ images. Besides, the dataset also contains bounding box annotations for other 7 animal categories. Source: Cross-Domain Adaptation for Animal Pose Estimation Image Source: https://sites.google.com/view/animal-pose/

SynLiDAR is a large-scale synthetic LiDAR sequential point cloud dataset with point-wise annotations. 13 sequences of LiDAR point cloud with around 20k scans (over 19 billion points and 32 semantic classes) are collected from virtual urban cities, suburban towns, neighborhood, and harbor.

VIDIT is a reference evaluation benchmark and to push forward the development of illumination manipulation methods. VIDIT includes 390 different Unreal Engine scenes, each captured with 40 illumination settings, resulting in 15,600 images. The illumination settings are all the combinations of 5 color temperatures (2500K, 3500K, 4500K, 5500K and 6500K) and 8 light directions (N, NE, E, SE, S, SW, W, NW). Original image resolution is 1024x1024. Source: VIDIT: Virtual Image Dataset for Illumination Transfer Image source: https://github.com/majedelhelou/VIDIT

Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning based approaches. Natural and unstructured environments present many additional challenges for the tasks of long-term localisation but these environments are not represented in currently available datasets. To address this we introduce Wild-Places, a challenging large-scale dataset for lidar place recognition in unstructured, natural environments. Wild-Places contains eight lidar sequences collected with a handheld sensor payload over the course of fourteen months, containing a total of 63K undistorted lidar submaps along with accurate 6DoF ground truth. This dataset contains multiple revisits both within and between sequences, allowing for both intra-sequence (i.e., loop closure detection) and inter-sequence (i.e., re-localisation) tasks. We also benchmark several state-of-the-art approaches to demonstrate the challenges that this dataset introduces, particularly the case of long-term place recognition due to natural environments changing over time. Our dataset and code is available at https://csiro-robotics.github.io/Wild-Places

Japanese-English Subtitle Corpus is a large Japanese-English parallel corpus covering the underrepresented domain of conversational dialogue. It consists of more than 3.2 million examples, making it the largest freely available dataset of its kind. The corpus was assembled by crawling and aligning subtitles found on the web. Source: JESC: Japanese-English Subtitle Corpus

Jester Gesture Recognition dataset includes 148,092 labeled video clips of humans performing basic, pre-defined hand gestures in front of a laptop camera or webcam. It is designed for training machine learning models to recognize human hand gestures like sliding two fingers down, swiping left or right and drumming fingers. Source: Gesture Recognition Dataset: Jester

VehicleX is a large-scale synthetic dataset. Created in Unity, it contains 1,362 vehicles of various 3D models with fully editable attributes. Source: https://github.com/yorkeyao/VehicleX Image Source: https://arxiv.org/pdf/1912.08855.pdf

CASIA V2 is a dataset for forgery classification. It contains 4795 images, 1701 authentic and 3274 forged. Source: Copy-Move Forgery Classification via Unsupervised Domain Adaptation Image Source: https://www.mdpi.com/1099-4300/21/4/371

This is a document grounded dataset for text conversations. "Document Grounded Conversations" are conversations that are about the contents of a specified document. In this dataset the specified documents are Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation. Source: https://github.com/festvox/datasets-CMU_DoG Image Source: https://arxiv.org/pdf/1809.07358v1.pdf

CrossNER is a cross-domain NER (Named Entity Recognition) dataset, a fully-labeled collection of NER data spanning over five diverse domains (Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five domains. Source: CrossNER

The MSK dataset is a dataset for lesion recognition from the Memorial Sloan-Kettering Cancer Center. It is used as part of the ISIC lesion recognition challenges. Source: https://arxiv.org/pdf/1710.05006.pdf Image Source: https://arxiv.org/pdf/1902.03368.pdf

REFUGE Challenge provides a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. Source: REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs Image Source: Orlando et al

The Multilingual Reuters Collection dataset comprises over 11,000 articles from six classes in five languages, i.e., English (E), French (F), German (G), Italian (I), and Spanish (S). Source: Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network Image Source: https://papers.nips.cc/paper/2009/file/f79921bbae40a577928b76d2fc3edc2a-Paper.pdf

Super-CLEVR is a dataset for Visual Question Answering (VQA) where different factors in VQA domain shifts can be isolated in order that their effects can be studied independently. It contains 21 vehicle models belonging to 5 categories, with controllable attributes. Four factors are considered: visual complexity, question redundancy, concept distribution and concept compositionality. Source: Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning Image Source: https://arxiv.org/pdf/2212.00259v1.pdf

Perspectrum is a dataset of claims, perspectives and evidence, making use of online debate websites to create the initial data collection, and augmenting it using search engines in order to expand and diversify the dataset. Crowd-sourcing was used to filter out noise and ensure high-quality data. The dataset contains 1k claims, accompanied with pools of 10k and 8k perspective sentences and evidence paragraphs, respectively. Source: Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims

So2Sat LCZ42 consists of local climate zone (LCZ) labels of about half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe. This dataset was labeled by 15 domain experts following a carefully designed labeling work flow and evaluation process over a period of six months. Source: So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification Image Source: Zhu et al

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.

Office-Caltech-10 a standard benchmark for domain adaptation, which consists of Office 10 and Caltech 10 datasets. It contains the 10 overlapping categories between the Office dataset and Caltech256 dataset. SURF BoW historgram features, vector quantized to 800 dimensions are also available for this dataset. Source: Impact of ImageNet Model Selection on Domain Adaptation Image Source: https://arxiv.org/abs/1409.5241