Boundary Detection

The Kinetics dataset is a large-scale, high-quality dataset for human action recognition in videos. The dataset consists of around 500,000 video clips covering 600 human action classes with at least 600 video clips for each action class. Each video clip lasts around 10 seconds and is labeled with a single action class. The videos are collected from YouTube. Source: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey

The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. It features: * 1449 densely labeled pairs of aligned RGB and depth images * 464 new scenes taken from 3 cities * 407,024 new unlabeled frames * Each object is labeled with a class and an instance number. The dataset has several components: * Labeled: A subset of the video data accompanied by dense multi-class labels. This data has also been preprocessed to fill in missing depth labels. * Raw: The raw RGB, depth and accelerometer data as provided by the Kinect. * Toolbox: Useful functions for manipulating the data and labels. Source: https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html Image Source: https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html

The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focussed and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. Source: https://arxiv.org/abs/1705.06950 Image source: https://arxiv.org/abs/1705.06950

The PASCAL Context dataset is an extension of the PASCAL VOC 2010 detection challenge, and it contains pixel-wise labels for all training images. It contains more than 400 classes (including the original 20 classes plus backgrounds from PASCAL VOC segmentation), divided into three categories (objects, stuff, and hybrids). Many of the object categories of this dataset are too sparse and; therefore, a subset of 59 frequent classes are usually selected for use. Source: Image Segmentation Using Deep Learning:A Survey Image Source: https://cs.stanford.edu/~roozbeh/pascal-context/

A novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production. Source: SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos

CoAuthor is a dataset designed for revealing GPT-3's capabilities in assisting creative and argumentative writing. CoAuthor captures rich interactions between 63 writers and four instances of GPT-3 across 1445 writing sessions.

In order to study the interaction of several early visual cues (luminance, color, stereo, motion) during boundary detection in challenging natural scenes, we have built a multi-cue video dataset composed of short binocular video sequences of natural scenes using a consumer-grade Fujifilm stereo camera (Mély, Kim, McGill, Guo and Serre, 2016). We considered a variety of places (from university campuses to street scenes and parks) and seasons to minimize possible biases. We attempted to capture more challenging scenes for boundary detection by framing a few dominant objects in each shot under a variety of appearances. Representative sample keyframes are shown on the figure below. The dataset contains 100 scenes, each consisting of a left and right view short (10-frame) color sequence. Each sequence was sampled at a rate of 30 frames per second. Each frame has a resolution of 1280 by 720 pixels.

UruDendro is a database of wood cross section images of commercially grown Pinus taeda trees from northern Uruguay. It is form by 64 RGB wood images, their tree rings delineations and pith location.

RoFT is a dataset of 21,000 human annotations of generated text. The task is "Boundary detection" i.e. given a passage that starts off as human written, determine when the text transitions to being machine generated. The dataset also includes error annotations using the taxonomy introduced in the paper. The data can be used to train automatic detection systems, train automatic error correction, analyze visibility of model errors, and compare performance across models. Data was collected using http://roft.io. Models: GPT2, GPT2-XL, CTRL, GPT3 "Davinci" Genres: News, Stories, Recipes, Speeches

TriBERT dataset consists of 12,049 training, 2,527 validation and 2,560 test Human-Machine collaborative texts. Each text contains both human-written and LLM-generated parts, which can appear in different orders (human → AI, AI → human). Therefore, each sample has between 1 and 3 boundaries, indicating the sentences where authorship changes. The texts were created using humanwritten essays with LLM-generated sections added using ChatGPT.

The temporal variability in calving front positions of marine-terminating glaciers permits inference on the frontal ablation. Frontal ablation, the sum of the calving rate and the melt rate at the terminus, significantly contributes to the mass balance of glaciers. Therefore, the glacier area has been declared as an Essential Climate Variable product by the World Meteorological Organization. The presented dataset provides the necessary information for training deep learning techniques to automate the process of calving front delineation. The dataset includes Synthetic Aperture Radar (SAR) images of seven glaciers distributed around the globe. Five of them are located in Antarctica: Crane, Dinsmoore-Bombardier-Edgeworth, Mapple, Jorum and the Sjörgen-Inlet Glacier. The remaining glaciers are the Jakobshavn Isbrae Glacier in Greenland and the Columbia Glacier in Alaska. Several images were taken for each glacier, forming a time series. The time series lie in the time span between 1995 and 2020. The images have different spatial resolutions, as they were captured by different satellites. The satellites used are Sentinel-1, TerraSAR-X, TanDEM-X, ENVISAT, European Remote Sensing Satellite 1&2, ALOS PALSAR, and RADARSAT-1. Along with the SAR images, two types of labels are provided so that deep learning techniques can be trained in a supervised manner. One label provides the position of the calving front. The other label shows the position of different landscape regions comprising glacier, rock outcrop, ocean including ice-melange, and an area where no information is available consisting of SAR shadows, layover regions, and areas outside the swath. The two labels allow different approaches to calving front delineation, as the calving front can be extracted from landscape region predictions during post-processing. As additional information for post-processing, the dataset includes bounding boxes for the dynamic calving front for each image. This bounding box excludes nearly static calving fronts also visible in the images, which are not of interest but would still be predicted as calving fronts by deep learning techniques. Hence, all front predictions outside this bounding box can be excluded during post-processing. To ensure the generalizability of the trained deep learning techniques to new unseen glaciers, the dataset is split into a training and an out-of-sample test set. The latter shall only be used to test the performance of the trained front delineation algorithm after all hyperparameters are optimized. The test set comprises the time series of Mapple and Columbia. More information on the dataset and how to use it can be found in the related paper. - The dataset has four subfolders: bounding_boxes, fronts, sar_images, and zones. - The bounding_boxes folder includes the bounding boxes for each image as separate text files. - The fronts, sar_images, and zones folders are each divided into test and train subfolders. - The sar_images folder holds the SAR images for training and testing as png files. - The fronts and zones folders include the labels (fronts - calving front position and zones - position of landscape regions) for each of the images in the sar_images folder. - The labels are png files with the same size and location as the corresponding SAR image. - The naming scheme of all files is: Glacier_Date_Satellite_SpatialResolutionInMeter_QualityFactor_Orbit(_Modality).png - The modality gives the type of label (front or zones). - The quality factor (with 1 being the best and 6 the worst) is based on the expert's opinion, who labelled the data. - Images with a quality factor of 6 were hard to interpret for the expert. Thus, the labels for these images may contain some inaccuracies. ### Dataset Gourmelon, Nora; Seehaus, Thorsten; Braun, Matthias Holger; Maier, Andreas; Christlein, Vincent (2022): CaFFe (CAlving Fronts and where to Find thEm: a benchmark dataset and methodology for automatic glacier calving front extraction from sar imagery). PANGAEA, https://doi.org/10.1594/PANGAEA.940950 ### Paper Gourmelon, N., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery, Earth Syst. Sci. Data, 14, 4287–4313, https://doi.org/10.5194/essd-14-4287-2022, 2022.

A new dataset of contour drawings. Source: Photo-Sketching: Inferring Contour Drawings from Images

RoFT-chatgpt is a variation of RoFT dataset, where the same human prompts are continued with the gpt-3.5-turbo model. Each dataset sample consists of ten sentences, with the first part written by a human and the remainder completed by an LLM. Consequently, every sample has a boundary indicating the index of the sentence where authorship changes.

The Oxford Road Boundaries is a dataset designed for training and testing machine-learning-based road-boundary detection and inference approaches. The authors have hand-annotated two of the 10 km-long forays from the Oxford Robotcar Dataset and generated from other forays several thousand further examples with semi-annotated road-boundary masks. To boost the number of training samples in this way, the authors used a vision-based localiser to project labels from the annotated datasets to other traversals at different times and weather conditions. As a result, the dataset consists of 62,605 labelled samples, of which 47,639 samples are curated. Each of these samples contains both raw and classified masks for left and right lenses. Our data contains images from a diverse set of scenarios such as straight roads, parked cars, junctions, etc.