Data quality is critical for multimedia tasks, while various types of
systematic flaws are found in image benchmark datasets, as discussed in recent
work. In particular, the existence of the semantic gap problem leads to a
many-to-many mapping between the information extracted from an image and its
linguistic description. This unavoidable bias further leads to poor performance
on current computer vision tasks. To address this issue, we introduce a
Knowledge Representation (KR)-based methodology to provide guidelines driving
the labeling process, thereby indirectly introducing intended semantics in ML
models. Specifically, an iterative refinement-based annotation method is
proposed to optimize data labeling by organizing objects in a classification
hierarchy according to their visual properties, ensuring that they are aligned
with their linguistic descriptions. Preliminary results verify the
effectiveness of the proposed method.

提出了一种基于知识表示（KR）的方法来指导数据标注的过程，并通过迭代优化技术，将对象组织在分类层次结构中，以保证它们与它们的语言描述保持一致，从而间接地引入了 ML 模型的预期语义。初步结果验证了所提出方法的有效性。

通过迭代优化实现递增式图像标记

Incremental Image Labeling via Iterative Refinement

Person re-identification (ReID) aims to re-identify a person from
non-overlapping camera views. Since person ReID data contains sensitive
personal information, researchers have adopted federated learning, an emerging
distributed training method, to mitigate the privacy leakage risks. However,
existing studies rely on data labels that are laborious and time-consuming to
obtain. We present FedUReID, a federated unsupervised person ReID system to
learn person ReID models without any labels while preserving privacy. FedUReID
enables in-situ model training on edges with unlabeled data. A cloud server
aggregates models from edges instead of centralizing raw data to preserve data
privacy. Moreover, to tackle the problem that edges vary in data volumes and
distributions, we personalize training in edges with joint optimization of
cloud and edge. Specifically, we propose personalized epoch to reassign
computation throughout training, personalized clustering to iteratively predict
suitable labels for unlabeled data, and personalized update to adapt the server
aggregated model to each edge. Extensive experiments on eight person ReID
datasets demonstrate that FedUReID not only achieves higher accuracy but also
reduces computation cost by 29%. Our FedUReID system with the joint
optimization will shed light on implementing federated learning to more
multimedia tasks without data labels.

FedUReID 是一种基于联邦无监督学习的人物重识别系统，可在维护数据隐私的同时，在边缘计算上进行现场模型训练，通过联合优化云和边缘的个性化训练解决无监督场景下人物重识别问题。实验表明，与现有方法相比，FedUReID 不仅在精度上更高，而且计算成本降低了 29％，可用于解决无标签的多媒体任务。