Key Point Matching

Data set covering a set of debatable topics, where for each topic and stance, a set of triplets of the form <argument, KP, label> is provided. The data set is based on the ArgKP data set, which contains arguments contributed by the crowd on 28 debatable topics, split by their stance towards the topic, and KPs written by an expert for those topics. Crowd annotations were collected to determine whether a KP represents an argument, i.e., is a match for an argument. The arguments in ArgKP are a subset of the [IBM-ArgQ-Rank-30kArgs data set](The arguments in ArgKP are a subset of the IBM-ArgQ-Rank-30kArgs data set). For a test set, we extended ArgKP, adding three new debatable topics, that were also not part of IBM-ArgQ-Rank-30kArgs. The test set was collected specifically for KPA-2021, and was carefully designed to be similar in various aspects to the training data 2 . For each topic, crowd sourced arguments were collected, expert KPs generated, and match/no match annotations for argument/KP pairs obtained, resulting in a data set compatible with the ArgKP format. Arguments collection strictly adhered to the guidelines, quality measures, and post processing used for the collection of arguments in IBM-ArgQ-Rank-30kArgs, while the generation of expert KPs, collection of match annotations, and final data set creation strictly adhered to the manner in which ArgKP was created. Source: [Overview of the 2021 Key Point Analysis Shared Task](Overview of the 2021 Key Point Analysis Shared Task)

The Few-Shot Object Learning (FewSOL) dataset can be used for object recognition with a few images per object. It contains 336 real-world objects with 9 RGB-D images per object from different views. Object segmentation masks, object poses and object attributes are provided. In addition, synthetic images generated using 330 3D object models are used to augment the dataset. FewSOL dataset can be used to study a set of few-shot object recognition problems such as classification, detection and segmentation, shape reconstruction, pose estimation, keypoint correspondences and attribute recognition. Motivation: If robots can recognize objects from a few exemplar images, it is possible to scale up the number of objects a robot can recognize because collecting a few images per object is a much easier process compared to building a 3D model of an object. In addition, models trained in the meta-learning setting can generalize to new objects without re-training.

A new synthetic, multi-purpose dataset - called ENRICH - for testing photogrammetric and computer vision algorithms. Compared to existing datasets, ENRICH offers higher resolution images also rendered with different lighting conditions, camera orientation, scales, and field of view. Specifically, ENRICH is composed of three sub-datasets: ENRICH-Aerial, ENRICH-Square, and ENRICH-Statue, each exhibiting different characteristics. The proposed dataset is useful for several photogrammetry and computer vision-related tasks, such as the evaluation of hand-crafted and deep learning-based local features, effects of ground control points (GCPs) configuration on the 3D accuracy, and monocular depth estimation. Each zip file in the root is relative to a specific dataset: - ENRICH-Aerial, is an aerial image block of the city of Launceston, Australia. The acquisition is performed by simulating a typical oblique aerial camera with five views (nadir and four oblique views). - ENRICH-Square, is a ground-level dataset of a square captured by four cameras, each one moving on a different path with different focal length, orientation, and lighting conditions. - ENRICH-Statue, is a ground-level dataset portraying a statue (placed in the center of the ENRICH-Square scene), acquired using four cameras moving on different paths with different focal lengths, orientations, and lighting conditions. Be sure to check the README file in the dataset root for information on folder structure and file contents. Please refer to the related paper (https://doi.org/10.1016/j.isprsjprs.2023.03.002) for information about the generation method and the purpose of ENRICH.