Wencheng Qin

Reducing the inter-modality gap has been the core of visible-infrared person re-identification (VI-ReID). Most of the existing methods directly constrain the features to reduce the gap between the different modalities. However, the consistency of channel semantic information across different modalities is ignored. In this letter, channel alignment along with severe semantic constraints is proposed to alleviate the significant distribution differences between modalities. To obtain the optimum channel matching mode, a novel channel alignment mechanism termed Channel Instance Level Alignment (CILA) is proposed at the shallow layer, and matched channel features are constrained by the proposed Channel Instance Alignment (CIA) Loss. In the middle layer, we divide the features into multiple hierarchically-aware channel clusters and align the channel clusters by Channel Cluster Alignment (CCA) Loss. The proposed method is validated on SYSU-MM01 and RegDB, and extensive experiments show that the proposed method achieves competitive performance compared with the state of the arts.

Adenomatous polyps are usually the most common tumor in clinical screening for colorectal cancer. Early detection and removal of these precursor lesions have been proved to be effective in preventing many cancers and reducing the mortality rate. Therefore, intelligent polyp detection is an urgent matter, which could assist clinicians to quickly identify detected polyps at an early stage. This paper proposes a polyp segmentation method based on sequential self-attentive networks with the features such as the color and texture of images and the relationship features between them. The color and texture features of the region image are extracted and fused with the original image. The resulting feature maps are serially input to the Transformer model to learn high-level spatial and attentional features. The loss of spatial information is reduced by multiscale local aggregation. By aggregating the models from a global perspective, the transformer model can further reduce the loss of context information caused by deep convolution. Controlled experiments between the PAFormer network and the mainstream polyp segmentation networks are conducted on five datasets, including ColonDB, ETIS, CVC-ColonDB, CVC-612 and Kvasir. The results show that the segmentation accuracy of PAFormer has great improvement on the datasets and PAFormer is superior to existing mainstream baseline networks.