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06/01/2023
Can Large Pre-trained Models Help Vision Models on Perception Tasks?
The recent upsurge in pre-trained large models (e.g. GPT-4) has swept ac...
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03/09/2023
Masked Image Modeling with Local Multi-Scale Reconstruction
Masked Image Modeling (MIM) achieves outstanding success in self-supervi...
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12/13/2022
FastMIM: Expediting Masked Image Modeling Pre-training for Vision
The combination of transformers and masked image modeling (MIM) pre-trai...
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11/23/2022
GhostNetV2: Enhance Cheap Operation with Long-Range Attention
Light-weight convolutional neural networks (CNNs) are specially designed...
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06/01/2022
Vision GNN: An Image is Worth Graph of Nodes
Network architecture plays a key role in the deep learning-based compute...
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04/24/2022
Source-Free Domain Adaptation via Distribution Estimation
Domain Adaptation aims to transfer the knowledge learned from a labeled ...
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01/04/2022
PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture
Transformer networks have achieved great progress for computer vision ta...
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11/24/2021
An Image Patch is a Wave: Phase-Aware Vision MLP
Different from traditional convolutional neural network (CNN) and vision...
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08/30/2021
Hire-MLP: Vision MLP via Hierarchical Rearrangement
This paper presents Hire-MLP, a simple yet competitive vision MLP archit...
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07/28/2021
Homogeneous Architecture Augmentation for Neural Predictor
Neural Architecture Search (NAS) can automatically design well-performed...
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07/13/2021
CMT: Convolutional Neural Networks Meet Vision Transformers
Vision transformers have been successfully applied to image recognition ...
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07/03/2021
Efficient Vision Transformers via Fine-Grained Manifold Distillation
This paper studies the model compression problem of vision transformers....
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06/30/2021
Augmented Shortcuts for Vision Transformers
Transformer models have achieved great progress on computer vision tasks...
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06/05/2021
Patch Slimming for Efficient Vision Transformers
This paper studies the efficiency problem for visual transformers by exc...
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04/17/2021
Visual Transformer Pruning
Visual transformer has achieved competitive performance on a variety of ...
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03/10/2021
Manifold Regularized Dynamic Network Pruning
Neural network pruning is an essential approach for reducing the computa...
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03/01/2021
Learning Frequency Domain Approximation for Binary Neural Networks
Binary neural networks (BNNs) represent original full-precision weights ...
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12/23/2020
A Survey on Visual Transformer
Transformer is a type of deep neural network mainly based on self-attent...
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10/21/2020
SCOP: Scientific Control for Reliable Neural Network Pruning
This paper proposes a reliable neural network pruning algorithm by setti...
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05/14/2020
A Semi-Supervised Assessor of Neural Architectures
Neural architecture search (NAS) aims to automatically design deep neura...
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02/23/2020
Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks
Deep neural networks often consist of a great number of trainable parame...
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09/30/2019
RNAS: Architecture Ranking for Powerful Networks
Neural Architecture Search (NAS) is attractive for automatically produci...
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07/13/2019
