Person Re-identification (ReID) plays a more and more crucial role in recent
years with a wide range of applications. Existing ReID methods are suffering
from the challenges of misalignment and occlusions, which degrade the
performance dramatically. Most methods tackle such challenges by utilizing
external tools to locate body parts or exploiting matching strategies.
Nevertheless, the inevitable domain gap between the datasets utilized for
external tools and the ReID datasets and the complicated matching process make
these methods unreliable and sensitive to noises. In this paper, we propose a
Region Generation and Assessment Network (RGANet) to effectively and
efficiently detect the human body regions and highlight the important regions.
In the proposed RGANet, we first devise a Region Generation Module (RGM) which
utilizes the pre-trained CLIP to locate the human body regions using semantic
prototypes extracted from text descriptions. Learnable prompt is designed to
eliminate domain gap between CLIP datasets and ReID datasets. Then, to measure
the importance of each generated region, we introduce a Region Assessment
Module (RAM) that assigns confidence scores to different regions and reduces
the negative impact of the occlusion regions by lower scores. The RAM consists
of a discrimination-aware indicator and an invariance-aware indicator, where
the former indicates the capability to distinguish from different identities
and the latter represents consistency among the images of the same class of
human body regions. Extensive experimental results for six widely-used
benchmarks including three tasks (occluded, partial, and holistic) demonstrate
the superiority of RGANet against state-of-the-art methods.

利用区域生成和评估网络（RGANet）有效而高效地检测人体区域并突出重要区域，通过区域生成模块（RGM）和区域评估模块（RAM）解决了现有人物再识别方法中存在的挑战，如失配和遮挡以及外部工具和比对策略带来的不可靠性和噪声敏感性。