Recognising human attributes from surveillance footage is widely studied for attribute-based re-identification. However, most works assume coarse, expertly-defined categories, ineffective in describing challenging images. Such brittle representations are limited in descriminitive power and hamper the efficacy of learnt estimators. We aim to discover more relevant and precise subject descriptions, improving image retrieval and closing the semantic gap. Inspired by fine-grained and relative attributes, we introduce super-fine attributes, which now describe multiple, integral concepts of a single trait as multi-dimensional perceptual coordinates. Crowd prototyping facilitates efficient crowdsourcing of super-fine labels by pre-discovering salient perceptual concepts for prototype matching. We re-annotate gender, age and ethnicity traits from PETA, a highly diverse (19K instances, 8.7K identities) amalgamation of 10 re-id datasets including VIPER, CUHK and TownCentre. Employing joint attribute regression with the ResNet-152 CNN, we demonstrate substantially improved ranked retrieval performance with super-fine attributes in comparison to conventional binary labels, reporting up to a 11.2 and 14.8 percent mAP improvement for gender and age, further surpassed by ethnicity. We also find our 3 super-fine traits to outperform 35 binary attributes by 6.5 percent mAP for subject retrieval in a challenging zero-shot identification scenario.

中文翻译：

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群体原型的超精细属性


从监控录像中识别人类属性已被广泛研究用于基于属性的重新识别。然而，大多数作品都假设粗略的、专业定义的类别，在描述具有挑战性的图像时无效。这种脆弱的表示在判别力方面受到限制，并妨碍了学习估计器的有效性。我们的目标是发现更相关和更精确的主题描述，改进图像检索并缩小语义差距。受细粒度和相对属性的启发，我们引入了超细属性，这些属性现在将单个特征的多个完整概念描述为多维感知坐标。群体原型制作通过预先发现原型匹配的显着感知概念，促进超精细标签的高效众包。我们重新注释来自 PETA 的性别、年龄和种族特征，PETA 是 10 个重新识别数据集（包括 VIPER、CUHK 和 TownCentre）的高度多样化（19K 个实例、8.7K 个身份）的合并。采用 ResNet-152 CNN 的联合属性回归，与传统的二元标签相比，我们证明了具有超精细属性的排序检索性能得到了显着提高，性别和年龄的 mAP 提高了 11.2% 和 14.8%，种族方面的 mAP 提高幅度更大。我们还发现，在具有挑战性的零样本识别场景中，我们的 3 个超精细特征比 35 个二元属性的主题检索性能高出 6.5% mAP。