SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of semantic segmentation due to the efficiency of self-attention in encoding spatial information. In this paper, we show that convolutional attention is a more efficient and effective way to encode contextual information than the self-attention mechanism in transformers. By re-examining the characteristics owned by successful segmentation models, we discover several key components leading to the performance improvement of segmentation models. This motivates us to design a novel convolutional attention network that uses cheap convolutional operations. Without bells and whistles, our SegNeXt significantly improves the performance of previous state-of-the-art methods on popular benchmarks, including ADE20K, Cityscapes, COCO-Stuff, Pascal VOC, Pascal Context, and iSAID. Notably, SegNeXt outperforms EfficientNet-L2 w/ NAS-FPN and achieves 90.6% mIoU on the Pascal VOC 2012 test leaderboard using only 1/10 parameters of it. On average, SegNeXt achieves about 2.0% mIoU improvements compared to the state-of-the-art methods on the ADE20K datasets with the same or fewer computations. Code is available at https://github.com/uyzhang/JSeg (Jittor) and https://github.com/Visual-Attention-Network/SegNeXt (Pytorch).Comment: SegNeXt, a simple CNN for semantic segmentation. Code is availabl

Self-Attention Convolutional Neural Network for Improved MR Image Reconstruction.

MRI is an advanced imaging modality with the unfortunate disadvantage of long data acquisition time. To accelerate MR image acquisition while maintaining high image quality, extensive investigations have been conducted on image reconstruction of sparsely sampled MRI. Recently, deep convolutional neural networks have achieved promising results, yet the local receptive field in convolution neural network raises concerns regarding signal synthesis and artifact compensation. In this study, we proposed a deep learning-based reconstruction framework to provide improved image fidelity for accelerated MRI. We integrated the self-attention mechanism, which captured long-range dependencies across image regions, into a volumetric hierarchical deep residual convolutional neural network. Basically, a self-attention module was integrated to every convolutional layer, where signal at a position was calculated as a weighted sum of the features at all positions. Furthermore, relatively dense shortcut connections were employed, and data consistency was enforced. The proposed network, referred to as SAT-Net, was applied on cartilage MRI acquired using an ultrashort TE sequence and retrospectively undersampled in a pseudo-random Cartesian pattern. The network was trained using 336 three dimensional images (each containing 32 slices) and tested with 24 images that yielded improved outcome. The framework is generic and can be extended to various applications