![]() ![]() The new SpaceNet dataset contains satellite images of building areas taken every month. The Multi-Temporal Urban Development SpaceNet AI training Dataset Dataset paper: Download link: ![]() They also provided an elegant solution to solve the video / caption retrieval problem using the adaptive average margin (AMM) method. However, they did not stop to show a great dataset. This AI training dataset builds a corpus of 500,000 short video and audio descriptions describing various events. Although this method has proven to be promising, we often forget that there are many rich summaries of our visual experience in terms of spoken language. Usually, for this type of task, we have AI training datasets like COCO, which contains images and their accompanying text captions. ![]() This is another of the most popular AI training datasets this year because it takes a slightly different approach to image captioning and video summarization. Some samples from the MIT audio captioning AI training dataset Proposed architecture for combining audiovisual information in the dataset Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions Ground truth, Trajectron++ predicted trajectory and joint B-VAE predicted trajectory (proposed in the same dataset paper) 2. In order to solve the problem of joint representation of trajectory and visual information in potential space, the same paper also proposes the generation architecture of Joint-B-VAE, which is a trained variational automatic encoder, which is used to encode the trajectory of participants and decode it into future synthetic trajectory. The Euro-PVI AI training dataset contains rich information about pedestrian-vehicle interactions, such as the visual scene, speed, and acceleration of all participants in the scene.Īll this information must be mapped to the relevant latent space by the trained model. Earlier, AI training datasets such as Stanford UAV, nuScenes, and Lyft L5 focused on the trajectories of nearby vehicles, but this is only part of the complete picture of the autonomous system.Įuro-PVI provides a comprehensive interactive diagram through information such as the visual scene during the interaction, the speed and acceleration during the interaction, and the overall coordinate trajectory during the entire interaction. Therefore, the Euro-PVI AI training dataset aims to solve this problem by training a model on a labeled dataset of pedestrian and cyclist trajectories. Predicting pedestrian trajectories in dense environments is a challenging task. One of the key parts is to make these autonomous systems understand how pedestrians respond to their existence. Predicting what trajectory pedestrians will take in response to approaching vehicles is critical to building fully automated self-driving cars.Īlthough there is a lot of discussion about fully autonomous autonomous driving systems, the fact remains that it is a very difficult problem that requires multiple problems to be solved in real time at the same time. Euro-PVI : Pedestrian vehicle interaction in dense urban centersĮxamples of real-time vehicle-pedestrian behavior. This article briefly summarizes some of the AI training dataset papers published on CVPR 2021, and reads through the papers to extract some important details. The newly released AI training datasets in the public domain can well represent new ways to understand the development of computer vision and the problems to be solved. Total training and inference times are calculated based upon an AWS p3.Some newly released AI training datasets can provide a window through which you can understand the complexity of the problem you are trying to solve. Note that the total contribution to the total NN’s ensembled is listed in parentheses in the Architectures column. The model architectures, ensemble and pre-training schemes, as well as training and inference time for each of the winning solutions. We also report model precision (ratio of false predictions) and recall (ratio of missed ground truth polygons): The overall score represents the SpaceNet Metric (x 100) for the entire scoring set. See the blog post on CosmiQ Works' blog The DownlinQ for an additional summary.Ĭompetitors’ scores in the SpaceNet 6: Multi-Sensor All Weather Mapping Challenge compared to the baseline model. Each subdirectory contains the competitors' written descriptions of their solution to the challenge. The five subdirectories in this repository comprise the code for the winning solutions of SpaceNet 6: Multi-Sensor All Weather Mapping Challenge hosted by TopCoder. SpaceNet 6: Multi-Sensor All Weather Mapping Competitor Solutions ![]()
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