27 citations · 59 across the 5 of their papers we have counts for
5 papers
FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures
Hao Lu, Wenze Liu, Hongtao Fu +1
The goal of this work is to develop a task-agnostic feature upsampling operator for dense prediction where the operator is required to facilitate not only region-sensitive tasks li…
Masked Image Residual Learning for Scaling Deeper Vision Transformers
Guoxi Huang, Hongtao Fu, Adrian G. Bors
Deeper Vision Transformers (ViTs) are more challenging to train. We expose a degradation problem in deeper layers of ViT when using masked image modeling (MIM) for pre-training. To…
Learning to Upsample by Learning to Sample
Wenze Liu, Hao Lu, Hongtao Fu +1
We present DySample, an ultra-lightweight and effective dynamic upsampler. While impressive performance gains have been witnessed from recent kernel-based dynamic upsamplers such a…
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
Hao Lu, Wenze Liu, Zixuan Ye +3
We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points…
FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling
Hao Lu, Wenze Liu, Hongtao Fu +1
We consider the problem of task-agnostic feature upsampling in dense prediction where an upsampling operator is required to facilitate both region-sensitive tasks like semantic seg…