research
∙
06/26/2023
ParameterNet: Parameters Are All You Need for Large-scale Visual Pretraining of Mobile Networks
The large-scale visual pretraining has significantly improve the perform...
share
research
∙
05/25/2023
VanillaKD: Revisit the Power of Vanilla Knowledge Distillation from Small Scale to Large Scale
The tremendous success of large models trained on extensive datasets dem...
research
∙
05/22/2023
VanillaNet: the Power of Minimalism in Deep Learning
At the heart of foundation models is the philosophy of "more is differen...
research
∙
03/09/2023
Masked Image Modeling with Local Multi-Scale Reconstruction
Masked Image Modeling (MIM) achieves outstanding success in self-supervi...
research
∙
12/13/2022
FastMIM: Expediting Masked Image Modeling Pre-training for Vision
The combination of transformers and masked image modeling (MIM) pre-trai...
research
∙
11/23/2022
GhostNetV2: Enhance Cheap Operation with Long-Range Attention
Light-weight convolutional neural networks (CNNs) are specially designed...
research
∙
09/02/2022
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs) are known for their large scale and knowledge inf...
research
∙
06/01/2022
Vision GNN: An Image is Worth Graph of Nodes
Network architecture plays a key role in the deep learning-based compute...
research
∙
03/28/2022
Brain-inspired Multilayer Perceptron with Spiking Neurons
Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field o...
research
∙
01/10/2022
GhostNets on Heterogeneous Devices via Cheap Operations
Deploying convolutional neural networks (CNNs) on mobile devices is diff...
research
∙
01/04/2022
PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture
Transformer networks have achieved great progress for computer vision ta...
research
∙
11/24/2021
An Image Patch is a Wave: Phase-Aware Vision MLP
Different from traditional convolutional neural network (CNN) and vision...
research
∙
08/30/2021
Hire-MLP: Vision MLP via Hierarchical Rearrangement
This paper presents Hire-MLP, a simple yet competitive vision MLP archit...
research
∙
07/13/2021
CMT: Convolutional Neural Networks Meet Vision Transformers
Vision transformers have been successfully applied to image recognition ...
research
∙
07/03/2021
Efficient Vision Transformers via Fine-Grained Manifold Distillation
This paper studies the model compression problem of vision transformers....
research
∙
06/05/2021
Patch Slimming for Efficient Vision Transformers
This paper studies the efficiency problem for visual transformers by exc...
research
∙
03/26/2021
Distilling Object Detectors via Decoupled Features
Knowledge distillation is a widely used paradigm for inheriting informat...
research
∙
02/27/2021
Transformer in Transformer
Transformer is a type of self-attention-based neural networks originally...
research
∙
12/23/2020
A Survey on Visual Transformer
Transformer is a type of deep neural network mainly based on self-attent...
research
∙
05/29/2020
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
Neural Architecture Search (NAS) refers to automatically design the arch...
research
∙
03/26/2020
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
Neural Architecture Search (NAS) has achieved great success in image cla...
research
∙
11/27/2019
GhostNet: More Features from Cheap Operations
Deploying convolutional neural networks (CNNs) on embedded devices is di...
research
∙
10/22/2019
Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification
Person re-identification is a challenging task due to various complex fa...
research
∙
07/29/2019
Interlaced Sparse Self-Attention for Semantic Segmentation
In this paper, we present a so-called interlaced sparse self-attention a...
research
∙
01/02/2019