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

We address the problem of distance metric learning in visual similarity
search, defined as learning an image embedding model which projects images into
Euclidean space where semantically and visually similar images are closer and
dissimilar images are further from one another. We present a weakly supervised
adaptive triplet loss (ATL) capable of capturing fine-grained semantic
similarity that encourages the learned image embedding models to generalize
well on cross-domain data. The method uses weakly labeled product description
data to implicitly determine fine grained semantic classes, avoiding the need
to annotate large amounts of training data. We evaluate on the Amazon fashion
retrieval benchmark and DeepFashion in-shop retrieval data. The method boosts
the performance of triplet loss baseline by 10.6% on cross-domain data and
out-performs the state-of-art model on all evaluation metrics.

该研究提出一种弱监督自适应三元组损失方法来解决视觉相似性搜索中的距离度量学习问题，通过使用弱标记的产品描述数据来隐式地确定细粒度语义类别，从而提高了学习图像嵌入模型的泛化能力，并在 Amazon 时尚检索基准和 DeepFashion 店内检索数据集上实现了优于当前最先进模型的性能。