activity
20152022
most citedHierarchical Neural Architecture Search for Deep Stereo Matching

230 citations · 513 across the 30 of their papers we have counts for

collaborators
Showing cs.CVShow all

29 papers · 1 filter

cs.CV20221 cited

Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers

Tao Tang, Changlin Li, Guangrun Wang +3

Automatic data augmentation (AutoAugment) strategies are indispensable in supervised data-efficient training protocols of vision transformers, and have led to state-of-the-art resu…

cs.CV20222 cited

Prompt-driven efficient Open-set Semi-supervised Learning

Haoran Li, Chun-Mei Feng, Tao Zhou +2

Open-set semi-supervised learning (OSSL) has attracted growing interest, which investigates a more practical scenario where out-of-distribution (OOD) samples are only contained in…

cs.CV202217 cited

PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search

Yameng Peng, Andy Song, Vic Ciesielski +2

Neural architecture search (NAS) aims to automate architecture engineering in neural networks. This often requires a high computational overhead to evaluate a number of candidate n…

cs.CV20222 cited

Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition

Mingfei Han, David Junhao Zhang, Yali Wang +4

Learning spatial-temporal relation among multiple actors is crucial for group activity recognition. Different group activities often show the diversified interactions between actor…

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.CV20221 cited

Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillation

Li Liu, Qingle Huang, Sihao Lin +4

Knowledge Distillation has shown very promising abil-ity in transferring learned representation from the largermodel (teacher) to the smaller one (student).Despitemany efforts, pri…