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FastMIM: Expediting Masked Image Modeling Pre-training for Vision
Jianyuan Guo, Kai Han, Han Wu +3
The combination of transformers and masked image modeling (MIM) pre-training framework has shown great potential in various vision tasks. However, the pre-training computational bu…
GhostNetV2: Enhance Cheap Operation with Long-Range Attention
Yehui Tang, Kai Han, Jianyuan Guo +3
Light-weight convolutional neural networks (CNNs) are specially designed for applications on mobile devices with faster inference speed. The convolutional operation can only captur…
Vision GNN: An Image is Worth Graph of Nodes
Kai Han, Yunhe Wang, Jianyuan Guo +2
Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or s…
Source-Free Domain Adaptation via Distribution Estimation
Ning Ding, Yixing Xu, Yehui Tang +3
Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. However, the training da…
From Quantum Graph Computing to Quantum Graph Learning: A Survey
Yehui Tang, Junchi Yan, Hancock Edwin
Quantum computing (QC) is a new computational paradigm whose foundations relate to quantum physics. Notable progress has been made, driving the birth of a series of quantum-based a…
PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture
Kai Han, Jianyuan Guo, Yehui Tang +1
Transformer networks have achieved great progress for computer vision tasks. Transformer-in-Transformer (TNT) architecture utilizes inner transformer and outer transformer to extra…