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BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons
Yixing Xu, Xinghao Chen, Yunhe Wang
This paper studies the problem of designing compact binary architectures for vision multi-layer perceptrons (MLPs). We provide extensive analysis on the difficulty of binarizing vi…
Redistribution of Weights and Activations for AdderNet Quantization
Ying Nie, Kai Han, Haikang Diao +3
Adder Neural Network (AdderNet) provides a new way for developing energy-efficient neural networks by replacing the expensive multiplications in convolution with cheaper additions…
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…
AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets
Zhijun Tu, Xinghao Chen, Pengju Ren +1
This paper studies the Binary Neural Networks (BNNs) in which weights and activations are both binarized into 1-bit values, thus greatly reducing the memory usage and computational…
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…
Multimodal Token Fusion for Vision Transformers
Yikai Wang, Xinghao Chen, Lele Cao +3
Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images. Intuitively, f…
Brain-inspired Multilayer Perceptron with Spiking Neurons
Wenshuo Li, Hanting Chen, Jianyuan Guo +2
Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field of computer vision tasks. Without inductive bias, MLPs perform well on feature extraction and achieve amazing…
MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation
Min Zhong, Xinghao Chen, Xiaokang Chen +2
This paper studies the 3D instance segmentation problem, which has a variety of real-world applications such as robotics and augmented reality. Since the surroundings of 3D objects…
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
Weijun Hong, Guilin Li, Weinan Zhang +4
Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation t…
GhostNets on Heterogeneous Devices via Cheap Operations
Kai Han, Yunhe Wang, Chang Xu +4
Deploying convolutional neural networks (CNNs) on mobile devices is difficult due to the limited memory and computation resources. We aim to design efficient neural networks for he…
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…