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.

对于机器人完成复杂任务来说，对象识别是基本要求。在人机交互的超远距离姿势识别方面，我们提出了基于扩散模型的超远距离扩散（DUR）框架，能够生成各种场景中远距离对象的标记图像，并且在训练超远距离姿势识别模型时表现出了优越性。

超远距离条件下训练目标识别模型的扩散式数据生成器

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

Generative models can serve as surrogates for some real data sources by
creating synthetic training datasets, but in doing so they may transfer biases
to downstream tasks. We focus on protecting quality and diversity when
generating synthetic training datasets. We propose quality-diversity generative
sampling (QDGS), a framework for sampling data uniformly across a user-defined
measure space, despite the data coming from a biased generator. QDGS is a
model-agnostic framework that uses prompt guidance to optimize a quality
objective across measures of diversity for synthetically generated data,
without fine-tuning the generative model. Using balanced synthetic datasets
generated by QDGS, we first debias classifiers trained on color-biased shape
datasets as a proof-of-concept. By applying QDGS to facial data synthesis, we
prompt for desired semantic concepts, such as skin tone and age, to create an
intersectional dataset with a combined blend of visual features. Leveraging
this balanced data for training classifiers improves fairness while maintaining
accuracy on facial recognition benchmarks. Code available at:
this https URL

通过质量 - 多样性生成抽样（QDGS）框架，我们可以生成均衡的合成数据集来训练分类器，从而提高公平性并保持面部识别基准的准确性。