The core problem of text-based person retrieval is how to bridge the
heterogeneous gap between multi-modal data. Many previous approaches contrive
to learning a latent common manifold mapping paradigm following a
\textbf{cross-modal distribution consensus prediction (CDCP)} manner. When
mapping features from distribution of one certain modality into the common
manifold, feature distribution of the opposite modality is completely
invisible. That is to say, how to achieve a cross-modal distribution consensus
so as to embed and align the multi-modal features in a constructed cross-modal
common manifold all depends on the experience of the model itself, instead of
the actual situation. With such methods, it is inevitable that the multi-modal
data can not be well aligned in the common manifold, which finally leads to a
sub-optimal retrieval performance. To overcome this \textbf{CDCP dilemma}, we
propose a novel algorithm termed LBUL to learn a Consistent Cross-modal Common
Manifold (C$^{3}$M) for text-based person retrieval. The core idea of our
method, just as a Chinese saying goes, is to `\textit{san si er hou xing}',
namely, to \textbf{Look Before yoU Leap (LBUL)}. The common manifold mapping
mechanism of LBUL contains a looking step and a leaping step. Compared to
CDCP-based methods, LBUL considers distribution characteristics of both the
visual and textual modalities before embedding data from one certain modality
into C$^{3}$M to achieve a more solid cross-modal distribution consensus, and
hence achieve a superior retrieval accuracy. We evaluate our proposed method on
two text-based person retrieval datasets CUHK-PEDES and RSTPReid. Experimental
results demonstrate that the proposed LBUL outperforms previous methods and
achieves the state-of-the-art performance.

以 LBUL 算法为例，该文研究如何解决文本 - 图像跨模态数据不一致的问题，通过考虑视觉和文本模态的分布特征，实现更加可靠的跨模态分布一致性，从而实现更优秀的文本 - 图像跨模态检索准确率。