Guozhong Luo

TL;DR: The first Mobile AI challenge as discussed by the authors was introduced to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms, where participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters.

TL;DR: Shuffle Transformer as discussed by the authors revisited the spatial shuffle as an efficient way to build connections among windows, which is highly efficient and easy to implement by modifying two lines of code.

TL;DR: The first Mobile AI challenge as discussed by the authors was introduced to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms.

TL;DR: Li et al. as mentioned in this paper proposed a simple but effective encoder-decoder based network for fast and accurate depth estimation on mobile devices, where the encoder with a fast downsampling strategy is employed to obtain sufficient receptive field and contexts at a faster rate.

TL;DR: Wang et al. as discussed by the authors introduced a strong backbone which is cross-window based Shuffle Transformer for presenting accurate face parsing representation, and further obtained the finer segmentation results, especially on the edges, by introducing a Feature Alignment Aggregation (FAA) module.