activity
20162026
most citedLocalViT: Analyzing Locality in Vision Transformers

285 citations · 838 across the 56 of their papers we have counts for

collaborators
Showing 2020Show all

7 papers · 1 filter

cs.CV2020

Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation

Rui Gong, Yuhua Chen, Danda Pani Paudel +5

Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advant…

eess.IV2020

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

Kai Zhang, Martin Danelljan, Yawei Li +75

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…

eess.IV2020

Plug-and-Play Image Restoration with Deep Denoiser Prior

Kai Zhang, Yawei Li, Wangmeng Zuo +3

Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems. Such a pr…

cs.CV2020★ 1 cited

The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures

Yawei Li, Wen Li, Martin Danelljan +4

In this paper, we tackle the problem of convolutional neural network design. Instead of focusing on the design of the overall architecture, we investigate a design space that is us…

cs.CV2020★ 17 cited

Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training

Yunxuan Wei, Shuhang Gu, Yawei Li +1

These days, unsupervised super-resolution (SR) has been soaring due to its practical and promising potential in real scenarios. The philosophy of off-the-shelf approaches lies in t…

cs.CV2020★ 18 cited

Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression

Yawei Li, Shuhang Gu, Christoph Mayer +2

In this paper, we analyze two popular network compression techniques, i.e. filter pruning and low-rank decomposition, in a unified sense. By simply changing the way the sparsity re…