125 citations · 155 across the 6 of their papers we have counts for
7 papers
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Andrey Ignatov, Grigory Malivenko, Radu Timofte +36
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus…
TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation
Wenqiang Zhang, Zilong Huang, Guozhong Luo +5
Although vision transformers (ViTs) have achieved great success in computer vision, the heavy computational cost hampers their applications to dense prediction tasks such as semant…
Sketch Me A Video
Haichao Zhang, Gang Yu, Tao Chen +1
Video creation has been an attractive yet challenging task for artists to explore. With the advancement of deep learning, recent works try to utilize deep convolutional neural netw…
Shuffle Transformer with Feature Alignment for Video Face Parsing
Rui Zhang, Yang Han, Zilong Huang +4
This is a short technical report introducing the solution of the Team TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR…
Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer
Zilong Huang, Youcheng Ben, Guozhong Luo +3
Very recently, Window-based Transformers, which computed self-attention within non-overlapping local windows, demonstrated promising results on image classification, semantic segme…
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Grigory Malivenko, David Plowman +35
Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually…