Human intelligence can retrieve any person according to both visual and
language descriptions. However, the current computer vision community studies
specific person re-identification (ReID) tasks in different scenarios
separately, which limits the applications in the real world. This paper strives
to resolve this problem by proposing a novel instruct-ReID task that requires
the model to retrieve images according to the given image or language
instructions. Instruct-ReID is the first exploration of a general ReID setting,
where existing 6 ReID tasks can be viewed as special cases by assigning
different instructions. To facilitate research in this new instruct-ReID task,
we propose a large-scale OmniReID++ benchmark equipped with diverse data and
comprehensive evaluation methods e.g., task specific and task-free evaluation
settings. In the task-specific evaluation setting, gallery sets are categorized
according to specific ReID tasks. We propose a novel baseline model, IRM, with
an adaptive triplet loss to handle various retrieval tasks within a unified
framework. For task-free evaluation setting, where target person images are
retrieved from task-agnostic gallery sets, we further propose a new method
called IRM++ with novel memory bank-assisted learning. Extensive evaluations of
IRM and IRM++ on OmniReID++ benchmark demonstrate the superiority of our
proposed methods, achieving state-of-the-art performance on 10 test sets. The
datasets, the model, and the code will be available at
this https URL

该研究提出了一种新的 instruct-ReID 任务，通过给定的图像或语言指令，模型可以检索图像；提出了一个大规模 OmniReID++ 基准，配备了各种数据和综合评估方法；介绍了基于自适应三元组损失的 IRM 基线模型和基于存储器辅助学习的 IRM++ 方法，在多个测试集上实现了最先进的性能。

Instruct-ReID++: 通向通用用途的指导人物再识别

Instruct-ReID++: Towards Universal Purpose Instruction-Guided Person  Re-identification

Human intelligence can retrieve any person according to both visual and
language descriptions. However, the current computer vision community studies
specific person re-identification (ReID) tasks in different scenarios
separately, which limits the applications in the real world. This paper strives
to resolve this problem by proposing a new instruct-ReID task that requires the
model to retrieve images according to the given image or language
instructions.Our instruct-ReID is a more general ReID setting, where existing
ReID tasks can be viewed as special cases by designing different instructions.
We propose a large-scale OmniReID benchmark and an adaptive triplet loss as a
baseline method to facilitate research in this new setting. Experimental
results show that the baseline model trained on our OmniReID benchmark can
improve +0.5%, +3.3% mAP on Market1501 and CUHK03 for traditional ReID, +2.1%,
+0.2%, +15.3% mAP on PRCC, VC-Clothes, LTCC for clothes-changing ReID, +12.5%
mAP on COCAS+ real2 for clothestemplate based clothes-changing ReID when using
only RGB images, +25.5% mAP on COCAS+ real2 for our newly defined
language-instructed ReID. The dataset, model, and code will be available at
this https URL

本文提出了一种新的人物 ReID 任务，即 instruct-ReID，该任务要求根据给定的图像或语言指令检索图像，提出了一个基于大规模 OmniReID 基准数据集和自适应三元组损失的基线方法来促进在这个新场景下的研究，实验结果表明，基于该 benchmark 训练的基线模型可以提高市场 1501 和 CUHK03 的传统 ReID 上的 mAP，各种衣服变化 ReID 上的 mAP，并在语言指导的 ReID 上进一步提高。