AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization

• URL: http://arxiv.org/abs/2410.24116v1
• Date: Thu, 31 Oct 2024 16:46:23 GMT
• Title: AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization
• Authors: Amir Kazemi, Qurat ul ain Fatima, Volodymyr Kindratenko, Christopher Tessum,
• Abstract summary: This work introduces a novel approach that leverages outpainting to address the problem of annotated data scarcity. We apply this technique to a particularly acute challenge in autonomous driving, urban planning, and environmental monitoring. Augmentation with outpainted vehicles improves overall performance metrics by up to 8% and enhances prediction of underrepresented classes by up to 20%.
• Score: 0.0
• License:
• Abstract: Image labeling is a critical bottleneck in the development of computer vision technologies, often constraining the potential of machine learning models due to the time-intensive nature of manual annotations. This work introduces a novel approach that leverages outpainting to address the problem of annotated data scarcity by generating artificial contexts and annotations, significantly reducing manual labeling efforts. We apply this technique to a particularly acute challenge in autonomous driving, urban planning, and environmental monitoring: the lack of diverse, eye-level vehicle images in desired classes. Our dataset comprises AI-generated vehicle images obtained by detecting and cropping vehicles from manually selected seed images, which are then outpainted onto larger canvases to simulate varied real-world conditions. The outpainted images include detailed annotations, providing high-quality ground truth data. Advanced outpainting techniques and image quality assessments ensure visual fidelity and contextual relevance. Augmentation with outpainted vehicles improves overall performance metrics by up to 8\% and enhances prediction of underrepresented classes by up to 20\%. This approach, exemplifying outpainting as a self-annotating paradigm, presents a solution that enhances dataset versatility across multiple domains of machine learning. The code and links to datasets used in this study are available for further research and replication at https://github.com/amir-kazemi/aidovecl.

Related papers

• Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline [3.524869467682149]
  We propose an automatic pipeline for generating synthetic image datasets using Stable Diffusion. We leverage YOLOv8 for automatic bounding box detection and quality assessment of synthesized images.
  arXiv  Detail & Related papers  (2024-06-03T07:44:08Z)
• Exploring Generative AI for Sim2Real in Driving Data Synthesis [6.769182994217369]
  Driving simulators offer a solution by automatically generating various driving scenarios with corresponding annotations, but the simulation-to-reality (Sim2Real) domain gap remains a challenge. This paper applied three different generative AI methods to leverage semantic label maps from a driving simulator as a bridge for the creation of realistic datasets. Experiments show that although GAN-based methods are adept at generating high-quality images when provided with manually annotated labels, ControlNet produces synthetic datasets with fewer artefacts and more structural fidelity when using simulator-generated labels.
  arXiv  Detail & Related papers  (2024-04-14T01:23:19Z)
• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• Learned representation-guided diffusion models for large-image generation [58.192263311786824]
  We introduce a novel approach that trains diffusion models conditioned on embeddings from self-supervised learning (SSL) Our diffusion models successfully project these features back to high-quality histopathology and remote sensing images. Augmenting real data by generating variations of real images improves downstream accuracy for patch-level and larger, image-scale classification tasks.
  arXiv  Detail & Related papers  (2023-12-12T14:45:45Z)
• Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks [64.67735676127208]
  Text-to-image diffusion models have shown great potential for benefiting image recognition. Although promising, there has been inadequate exploration dedicated to unsupervised learning on diffusion-generated images. We introduce customized solutions by fully exploiting the aforementioned free attention masks.
  arXiv  Detail & Related papers  (2023-08-13T10:07:46Z)
• CarPatch: A Synthetic Benchmark for Radiance Field Evaluation on Vehicle Components [77.33782775860028]
  We introduce CarPatch, a novel synthetic benchmark of vehicles. In addition to a set of images annotated with their intrinsic and extrinsic camera parameters, the corresponding depth maps and semantic segmentation masks have been generated for each view. Global and part-based metrics have been defined and used to evaluate, compare, and better characterize some state-of-the-art techniques.
  arXiv  Detail & Related papers  (2023-07-24T11:59:07Z)
• Image Captions are Natural Prompts for Text-to-Image Models [70.30915140413383]
  We analyze the relationship between the training effect of synthetic data and the synthetic data distribution induced by prompts. We propose a simple yet effective method that prompts text-to-image generative models to synthesize more informative and diverse training data. Our method significantly improves the performance of models trained on synthetic training data.
  arXiv  Detail & Related papers  (2023-07-17T14:38:11Z)
• CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images [7.868449549351487]
  This article proposes to enhance our ability to recognise AI-generated images through computer vision. The two sets of data present as a binary classification problem with regard to whether the photograph is real or generated by AI. This study proposes the use of a Convolutional Neural Network (CNN) to classify the images into two categories; Real or Fake.
  arXiv  Detail & Related papers  (2023-03-24T16:33:06Z)
• Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection [1.4528189330418975]
  We propose a generative approach that generates top-down images by overlaying artificial vehicles created from 2D CAD drawings on artificial or real backgrounds. Our experiments with a modified RetinaNet object detection network show that adding these images to small real-world datasets significantly improves detection performance.
  arXiv  Detail & Related papers  (2021-04-07T11:06:15Z)
• Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings [22.889059874754242]
  Generation of stroke-based non-photorealistic imagery is an important problem in the computer vision community. Previous methods have been limited to datasets with little variation in position, scale and saliency of the foreground object. We propose a Semantic Guidance pipeline with 1) a bi-level painting procedure for learning the distinction between foreground and background brush strokes at training time.
  arXiv  Detail & Related papers  (2020-11-25T09:00:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.