Existing person re-identification methods utilizing CLIP (Contrastive Language-Image Pre-training) mostly suffer from coarse-grained alignment issues. This is primarily due to the original design intention of the CLIP model, which aims at broad and global alignment between images and texts to support a wide range of image–text matching tasks. However, in the specific domain of person re-identification, local features and fine-grained information are equally important in addition to global features. This paper proposes an innovative modal fusion approach, aiming to precisely locate the most prominent pedestrian information in images by combining visual features extracted by the ResNet-50 model with text representations generated by a text encoder. This method leverages the cross-attention mechanism of the Transformer Decoder to enable text features to dynamically guide visual features, enhancing the ability to identify and locate the target pedestrian. Experiments conducted on four public datasets, namely MSMT17, Market1501, DukeMTMC, and Occluded-Duke, demonstrate that our method outperforms the baseline network by 5.4%, 2.7%, 2.6%, and 9.2% in mAP, and by 4.3%, 1.7%, 2.7%, and 11.8% in Rank-1, respectively. This method exhibits excellent performance and provides new research insights for the task of person re-identification.

Light scattering and attenuation in water degrade underwater images with low visibility and color distortion, which often interfere with the high-level visual tasks of underwater autonomous robots. Most existing deep learning methods for underwater image enhancement only supervise the final output of network and ignore the promotion effect of the intermediate results on the final feature representation. These supervision methods affect the feature representation ability, network efficiency, and ability. In this paper, we present a novel idea of multiple-stage supervision to guide the network to learn useful features correctly and progressively. With this idea, we propose a pixel-wise Progressive Guided Network (PGN) for underwater image enhancement to take advantage of the network’s intermediate results and promote the final enhancement effect. The Pixel-Wise Attention Module is designed by introducing supervision in each stage to progressively promote the representation ability of the features and the recovered image quality. The experimental results on several datasets demonstrate that our method outperforms recent state-of-the-art underwater image enhancement methods.