Lifelong Person Re-Identification (LReID) aims to continuously learn from
successive data streams, matching individuals across multiple cameras. The key
challenge for LReID is how to effectively preserve old knowledge while learning
new information incrementally. Task-level domain gaps and limited old task
datasets are key factors leading to catastrophic forgetting in ReLD, which are
overlooked in existing methods. To alleviate this problem, we propose a novel
Diverse Representation Embedding (DRE) framework for LReID. The proposed DRE
preserves old knowledge while adapting to new information based on
instance-level and task-level layout. Concretely, an Adaptive Constraint Module
(ACM) is proposed to implement integration and push away operations between
multiple representations, obtaining dense embedding subspace for each instance
to improve matching ability on limited old task datasets. Based on the
processed diverse representation, we interact knowledge between the adjustment
model and the learner model through Knowledge Update (KU) and Knowledge
Preservation (KP) strategies at the task-level layout, which reduce the
task-wise domain gap on both old and new tasks, and exploit diverse
representation of each instance in limited datasets from old tasks, improving
model performance for extended periods. Extensive experiments were conducted on
eleven Re-ID datasets, including five seen datasets for training in order-1 and
order-2 orders and six unseen datasets for inference. Compared to
state-of-the-art methods, our method achieves significantly improved
performance in holistic, large-scale, and occluded datasets.

通过 Diverse Representation Embedding （DRE）框架，提出了一种新颖的 Lifelong Person Re-Identification（LReID）方法，该方法在实例级别和任务级别布局上保留老知识并适应新信息，减少旧任务和新任务之间的领域差距，从而提高模型的性能。

多样化的表示嵌入在持续人员再识别中的应用

Diverse Representation Embedding for Lifelong Person Re-Identification

In order for large language model (LLM)-based assistants to effectively adapt
to evolving information needs, it must be possible to update their factual
knowledge through continued training on new data. The standard recipe for doing
so involves continued pre-training on new documents followed by
instruction-tuning on question-answer (QA) pairs. However, we find that LLMs
trained with this recipe struggle to answer questions, even though the
perplexity of documents is minimized. We found that QA pairs are generally
straightforward, while documents are more complex, weaving many factual
statements together in an intricate manner. Therefore, we hypothesize that it
is beneficial to expose LLMs to QA pairs before continued pre-training on
documents so that the process of encoding knowledge from complex documents
takes into account how this knowledge is accessed through questions. Based on
this, we propose pre-instruction-tuning (PIT), a method that instruction-tunes
on questions prior to training on documents. This contrasts with standard
instruction-tuning, which learns how to extract knowledge after training on
documents. Extensive experiments and ablation studies demonstrate that PIT
significantly enhances the ability of LLMs to absorb knowledge from new
documents, outperforming standard instruction-tuning by 17.8%.

通过在文档训练之前进行问答配对，提出了一种称为 PIT（pre-instruction-tuning）的方法，能够显著提高大型语言模型从新文档吸收知识的能力，比传统的指令调整方法的性能提高了 17.8%。