Source-Free Domain Adaptation

PACS is an image dataset for domain generalization. It consists of four domains, namely Photo (1,670 images), Art Painting (2,048 images), Cartoon (2,344 images) and Sketch (3,929 images). Each domain contains seven categories. Source: Deep Domain-Adversarial Image Generation for Domain Generalisation Image Source: https://www.researchgate.net/figure/Sample-images-from-PACS-dataset-Each-row-represents-a-domain-and-each-column-represents_fig1_334695033

The SYNTHIA dataset is a synthetic dataset that consists of 9400 multi-viewpoint photo-realistic frames rendered from a virtual city and comes with pixel-level semantic annotations for 13 classes. Each frame has resolution of 1280 × 960. Source: Orientation-aware Semantic Segmentation on Icosahedron Spheres Image Source: https://synthia-dataset.net/

The GTA5 dataset contains 24966 synthetic images with pixel level semantic annotation. The images have been rendered using the open-world video game Grand Theft Auto 5 and are all from the car perspective in the streets of American-style virtual cities. There are 19 semantic classes which are compatible with the ones of Cityscapes dataset. Source: Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation Image Source: Richter et al

Foggy Cityscapes is a synthetic foggy dataset which simulates fog on real scenes. Each foggy image is rendered with a clear image and depth map from Cityscapes. Thus the annotations and data split in Foggy Cityscapes are inherited from Cityscapes. Source: Exploring Object Relation in Mean Teacher for Cross-Domain Detection Image Source: http://people.ee.ethz.ch/~csakarid/SFSU_synthetic/

VisDA-2017 is a simulation-to-real dataset for domain adaptation with over 280,000 images across 12 categories in the training, validation and testing domains. The training images are generated from the same object under different circumstances, while the validation images are collected from MSCOCO.. Source: Gradually Vanishing Bridge for Adversarial Domain Adaptation Image Source: http://ai.bu.edu/visda-2017/

Rationale and objectives: Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database. Materials and methods: Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. João [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospital's Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used. **Results: **The new database-INbreast-has a total of 115 cases (410 images) from which 90 cases are from women with both breasts affected (four images per case) and 25 cases are from mastectomy patients (two images per case). Several types of lesions (masses, calcifications, asymmetries, and distortions) were included. Accurate contours made by specialists are also provided in XML format. Conclusion: The strengths of the actually presented database-INbreast-relies on the fact that it was built with full-field digital mammograms (in opposition to digitized mammograms), it presents a wide variability of cases, and is made publicly available together with precise annotations. We believe that this database can be a reference for future works centered or related to breast cancer imaging.

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.

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

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.