  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.
