Ling Shao

TL;DR: Huang et al. as discussed by the authors proposed Pyramid Vision Transformer (PVT), which is a simple backbone network useful for many dense prediction tasks without convolutions, and achieved state-of-the-art performance on the COCO dataset.

TL;DR: MPRNet as discussed by the authors proposes a multi-stage architecture that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps, and introduces a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.

TL;DR: Li et al. as discussed by the authors proposed a COVID-19 Lung Infection Segmentation Deep Network ( Inf-Net) to automatically identify infected regions from chest CT slices, where a parallel partial decoder is used to aggregate the high-level features and generate a global map.

TL;DR: MIRNet as mentioned in this paper proposes a multi-scale residual block containing several key elements: (a) parallel multi-resolution convolution streams for extracting mult-scale features, (b) information exchange across the multiresolution streams, (c) spatial and channel attention mechanisms for capturing contextual information, and (d) attention-based multiscale feature aggregation.

TL;DR: A novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT scans and outperforms most cutting-edge segmentation models and advances the state-of-the-art technology.