Although convolutional networks (ConvNets) have enjoyed great success in computer vision (CV), it suffers from capturing global information crucial to dense prediction tasks such as object detection and segmentation. In this work, we innovatively propose ConTNet (Convolution-Transformer Network), combining transformer with Con-vNet architectures to provide large receptive fields. Unlike the recently-proposed transformer-based models (e.g., ViT, DeiT) that are sensitive to hyper-parameters and extremely dependent on a pile of data augmentations when trained from scratch on a midsize dataset (e.g., ImageNet1k), Con-TNet can be optimized like normal ConvNets (e.g., ResNet) and preserve an outstanding robustness. It is also worth pointing that, given identical strong data augmentations, the performance improvement of ConTNet is more remarkable than that of ResNet. We present its superiority and effectiveness on image classification and downstream tasks. For example, our ConTNet achieves 81.8% top-1 accuracy on ImageNet which is the same as DeiT-B with less than 40% computational complexity. ConTNet-M also outperforms ResNet50 as the backbone of both Faster-RCNN (by 2.6%) and Mask-RCNN (by 3.2%) on COCO2017 dataset. We hope that ConTNet could serve as a useful backbone for CV tasks and bring new ideas for model design. The code will be released at https://github.com/yanhao-tian/ConTNet.
A systematic method for dynamic analysis of compliant mechanisms is presented including basic formulations for natural frequencies, modes, dynamic response, and frequency characteristics. Methods for design sensitivity analysis are developed to investigate the effect of various design parameters on the dynamic performance of compliant mechanisms. A micro compliant stroke amplifier mechanism, for MEMS actuator application, is presented as a design example to demonstrate significant differences between static and dynamic performance of compliant mechanisms.
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