Transformer has been very successful in various computer vision tasks and understanding the working mechanism of transformer is important. As touchstones, weakly-supervised semantic segmentation (WSSS) and class activation map (CAM) are useful tasks for analyzing vision transformers (ViT). Based on the plain ViT pre-trained with ImageNet classification, we find that multi-layer, multi-head self-attention maps can provide rich and diverse information for weakly-supervised semantic segmentation and CAM generation, e.g., different attention heads of ViT focus on different image areas and object categories. Thus we propose a novel method to end-to-end estimate the importance of attention heads, where the self-attention maps are adaptively fused for high-quality CAM results that tend to have more complete objects. Besides, we propose a ViT-based gradient clipping decoder for online retraining with the CAM results efficiently and effectively. Furthermore, the gradient clipping decoder can make good use of the knowledge in large-scale pre-trained ViT and has a scalable ability. The proposed plain Transformer-based Weakly-supervised learning method (WeakTr) obtains the superior WSSS performance on standard benchmarks, i.e., 78.5% mIoU on the $val$ set of PASCAL VOC 2012 and 51.1% mIoU on the $val$ set of COCO 2014. Source code and checkpoints are available at https://github.com/hustvl/WeakTr.

New design approach to improve the turbomachinery performance is the research hot in various areas. In order to make up for the shortcomings about the uncertain design variables ranges and the simple 2D blade profile design method in previous studies, a modified inverse design approach established on the newly derived constrained zone and the PSO algorithm has been proposed in this study, and a developed design program with the Matlab platform was developed. With the adoption of the scaled 1:2.5 hydraulic model of the CAP1400 impeller as the target, the proposed joint design method was applied and verified.