activity
20192022
most citedAutomated Progressive Learning for Efficient Training of Vision Transformers

4 citations · 14 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CV20222 cited

Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism

Binbin Yang, Xinchi Deng, Han Shi +6

Continual learning is a challenging real-world problem for constructing a mature AI system when data are provided in a streaming fashion. Despite recent progress in continual class…

cs.CV20223 cited

Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling

Takashi Isobe, Xu Jia, Xin Tao +6

Temporal modeling is crucial for video super-resolution. Most of the video super-resolution methods adopt the optical flow or deformable convolution for explicitly motion compensat…

cs.CV20221 cited

Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search

Minbin Huang, Zhijian Huang, Changlin Li +4

Neural Architecture Search (NAS) aims to find efficient models for multiple tasks. Beyond seeking solutions for a single task, there are surging interests in transferring network d…

cs.CV2022

Beyond Fixation: Dynamic Window Visual Transformer

Pengzhen Ren, Changlin Li, Guangrun Wang +4

Recently, a surge of interest in visual transformers is to reduce the computational cost by limiting the calculation of self-attention to a local window. Most current work uses a f…

cs.CV20224 cited

Automated Progressive Learning for Efficient Training of Vision Transformers

Changlin Li, Bohan Zhuang, Guangrun Wang +3

Recent advances in vision Transformers (ViTs) have come with a voracious appetite for computing power, high-lighting the urgent need to develop efficient training methods for ViTs.…

cs.CV20211 cited

Dynamic Slimmable Denoising Network

Zutao Jiang, Changlin Li, Xiaojun Chang +2

Recently, tremendous human-designed and automatically searched neural networks have been applied to image denoising. However, previous works intend to handle all noisy images in a…