mlsanityData to 13 Oct 2026

A Diffusion-based Data Generator for Training Object Recognition Models in Ultra-Range Distance

arXiv 2404.09846 ↗First posted 15 Apr 2024cs.CVcited by 0

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Abstract (arXiv)

Object recognition, commonly performed by a camera, is a fundamental requirement for robots to complete complex tasks. Some tasks require recognizing objects far from the robot's camera. A challenging example is Ultra-Range Gesture Recognition (URGR) in human-robot interaction where the user exhibits directive gestures at a distance of up to 25~m from the robot. However, training a model to recognize hardly visible objects located in ultra-range requires an exhaustive collection of a significant amount of labeled samples. The generation of synthetic training datasets is a recent solution to the lack of real-world data, while unable to properly replicate the realistic visual characteristics of distant objects in images. In this letter, we propose the Diffusion in Ultra-Range (DUR) framework based on a Diffusion model to generate labeled images of distant objects in various scenes. The DUR generator receives a desired distance and class (e.g., gesture) and outputs a corresponding synthetic image. We apply DUR to train a URGR model with directive gestures in which fine details of the gesturing hand are challenging to distinguish. DUR is compared to other types of generative models showcasing superiority both in fidelity and in recognition success rate when training a URGR model. More importantly, training a DUR model on a limited amount of real data and then using it to generate synthetic data for training a URGR model outperforms directly training the URGR model on real data. The synthetic-based URGR model is also demonstrated in gesture-based direction of a ground robot.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 27 of its 53 references that are in the map. First posted on arXiv 15 Apr 2024.

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27 of 53 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,043
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,952
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
  5. 2020Denoising Diffusion Implicit Modelscited by 9,718
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,023
  7. 2022Classifier-Free Diffusion Guidancecited by 5,785
  8. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,077
  9. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,975
  10. 2016Improved Techniques for Training GANscited by 2,946
  11. 2014Conditional Generative Adversarial Netscited by 2,157
  12. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,051
15 more
  1. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,425
  2. 2022Diffusion Models in Vision: A Surveycited by 1,012
  3. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 563
  4. 2023ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shiftingcited by 364
  5. 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
  6. 2018Pros and Cons of GAN Evaluation Measurescited by 224
  7. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  8. 2021CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detectioncited by 64
  9. 2018Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networkscited by 54
  10. 2018GestureGAN for Hand Gesture-to-Gesture Translation in the Wildcited by 27
  11. 2019It GAN DO Better: GAN-based Detection of Objects on Images with Varying Qualitycited by 12
  12. 2021The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognitioncited by 8
  13. 2023Ultra-Range Gesture Recognition using a Web-Camera in Human-Robot Interactioncited by 6
  14. 2021Leveraging Image-based Generative Adversarial Networks for Time Series Generationcited by 4
  15. 2019Graph Convolutional Networks: analysis, improvements and resultscited by 3

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