b'Yunhe Wang'

research

∙ 07/24/2023

Less is More: Focus Attention for Efficient DETR

DETR-like models have significantly boosted the performance of detectors...

0 Dehua Zheng, et al. ∙

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...

0 Kai Han, et al. ∙

research

∙ 06/14/2023

GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image

The extraordinary ability of generative models to generate photographic ...

0 Mingjian Zhu, et al. ∙

research

∙ 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...

0 Ning Ding, et al. ∙

research

∙ 05/29/2023

Multiscale Positive-Unlabeled Detection of AI-Generated Texts

Recent releases of Large Language Models (LLMs), e.g. ChatGPT, are aston...

0 Yuchuan Tian, et al. ∙

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...

0 Zhiwei Hao, et al. ∙

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...

0 Hanting Chen, et al. ∙

research

∙ 03/09/2023

Masked Image Modeling with Local Multi-Scale Reconstruction

Masked Image Modeling (MIM) achieves outstanding success in self-supervi...

0 Haoqing Wang, et al. ∙

research

∙ 12/29/2022

BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons

This paper studies the problem of designing compact binary architectures...

0 Yixing Xu, et al. ∙

research

∙ 12/20/2022

Redistribution of Weights and Activations for AdderNet Quantization

Adder Neural Network (AdderNet) provides a new way for developing energy...

0 Ying Nie, et al. ∙

research

∙ 12/13/2022

FastMIM: Expediting Masked Image Modeling Pre-training for Vision

The combination of transformers and masked image modeling (MIM) pre-trai...

0 Jianyuan Guo, et al. ∙

research

∙ 11/23/2022

GhostNetV2: Enhance Cheap Operation with Long-Range Attention

Light-weight convolutional neural networks (CNNs) are specially designed...

0 Yehui Tang, et al. ∙

research

∙ 08/17/2022

AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets

This paper studies the Binary Neural Networks (BNNs) in which weights an...

0 Zhijun Tu, et al. ∙

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...

0 Kai Han, et al. ∙

research

∙ 04/24/2022

Source-Free Domain Adaptation via Distribution Estimation

Domain Adaptation aims to transfer the knowledge learned from a labeled ...

0 Ning Ding, et al. ∙

research

∙ 04/19/2022

Multimodal Token Fusion for Vision Transformers

Many adaptations of transformers have emerged to address the single-moda...

0 Yikai Wang, et al. ∙

research

∙ 03/28/2022

Brain-inspired Multilayer Perceptron with Spiking Neurons

Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field o...

0 Wenshuo Li, et al. ∙

research

∙ 03/28/2022

MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation

This paper studies the 3D instance segmentation problem, which has a var...

0 Min Zhong, et al. ∙

research

∙ 01/27/2022

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

Neural architecture search (NAS) has shown encouraging results in automa...

0 Weijun Hong, et al. ∙

research

∙ 01/10/2022

GhostNets on Heterogeneous Devices via Cheap Operations

Deploying convolutional neural networks (CNNs) on mobile devices is diff...

2 Kai Han, et al. ∙

research

∙ 01/04/2022

PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture

Transformer networks have achieved great progress for computer vision ta...

0 Kai Han, et al. ∙

research

∙ 12/27/2021

An Empirical Study of Adder Neural Networks for Object Detection

Adder neural networks (AdderNets) have shown impressive performance on i...

10 Xinghao Chen, et al. ∙

research

∙ 11/24/2021

An Image Patch is a Wave: Phase-Aware Vision MLP

Different from traditional convolutional neural network (CNN) and vision...

0 Yehui Tang, et al. ∙

research

∙ 10/27/2021

A Self-adaptive Weighted Differential Evolution Approach for Large-scale Feature Selection

Recently, many evolutionary computation methods have been developed to s...

34 Xubin Wang, et al. ∙

research

∙ 09/20/2021

Learning Versatile Convolution Filters for Efficient Visual Recognition

This paper introduces versatile filters to construct efficient convoluti...

0 Kai Han, et al. ∙

research

∙ 08/30/2021

Hire-MLP: Vision MLP via Hierarchical Rearrangement

This paper presents Hire-MLP, a simple yet competitive vision MLP archit...

0 Jianyuan Guo, et al. ∙

research

∙ 08/16/2021

Neural Architecture Dilation for Adversarial Robustness

With the tremendous advances in the architecture and scale of convolutio...

0 Yanxi Li, et al. ∙

research

∙ 07/31/2021

Greedy Network Enlarging

Recent studies on deep convolutional neural networks present a simple pa...

10 Chuanjian Liu, et al. ∙

research

∙ 07/13/2021

CMT: Convolutional Neural Networks Meet Vision Transformers

Vision transformers have been successfully applied to image recognition ...

0 Jianyuan Guo, et al. ∙

research

∙ 07/03/2021

Efficient Vision Transformers via Fine-Grained Manifold Distillation

This paper studies the model compression problem of vision transformers....

0 Ding Jia, et al. ∙

research

∙ 06/30/2021

Augmented Shortcuts for Vision Transformers

Transformer models have achieved great progress on computer vision tasks...

0 Yehui Tang, et al. ∙

research

∙ 06/27/2021

Post-Training Quantization for Vision Transformer

Recently, transformer has achieved remarkable performance on a variety o...

0 Zhenhua Liu, et al. ∙

research

∙ 06/21/2021

Federated Learning with Positive and Unlabeled Data

We study the problem of learning from positive and unlabeled (PU) data i...

0 Xinyang Lin, et al. ∙

research

∙ 06/05/2021

Dynamic Resolution Network

Deep convolutional neural networks (CNNs) are often of sophisticated des...

0 Mingjian Zhu, et al. ∙

research

∙ 06/05/2021

Patch Slimming for Efficient Vision Transformers

This paper studies the efficiency problem for visual transformers by exc...

0 Yehui Tang, et al. ∙

research

∙ 05/12/2021

Winograd Algorithm for AdderNet

Adder neural network (AdderNet) is a new kind of deep model that replace...

0 Wenshuo Li, et al. ∙

research

∙ 04/19/2021

Coarse-to-Fine Searching for Efficient Generative Adversarial Networks

This paper studies the neural architecture search (NAS) problem for deve...

0 Jiahao Wang, et al. ∙

research

∙ 04/17/2021

Visual Transformer Pruning

Visual transformer has achieved competitive performance on a variety of ...

0 Mingjian Zhu, et al. ∙

research

∙ 03/26/2021

Distilling Object Detectors via Decoupled Features

Knowledge distillation is a widely used paradigm for inheriting informat...

0 Jianyuan Guo, et al. ∙

research

∙ 03/15/2021

Learning Frequency-aware Dynamic Network for Efficient Super-Resolution

Deep learning based methods, especially convolutional neural networks (C...

48 Wenbin Xie, et al. ∙

research

∙ 03/10/2021

Manifold Regularized Dynamic Network Pruning

Neural network pruning is an essential approach for reducing the computa...

0 Yehui Tang, et al. ∙

research

∙ 03/01/2021

Learning Frequency Domain Approximation for Binary Neural Networks

Binary neural networks (BNNs) represent original full-precision weights ...

0 Yixing Xu, et al. ∙

research

∙ 02/27/2021

Transformer in Transformer

Transformer is a type of self-attention-based neural networks originally...

0 Kai Han, et al. ∙

research

∙ 01/25/2021

AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence

Convolutional neural networks (CNN) have been widely used for boosting t...

35 Yunhe Wang, et al. ∙

research

∙ 01/21/2021

GhostSR: Learning Ghost Features for Efficient Image Super-Resolution

Modern single image super-resolution (SISR) system based on convolutiona...

0 Ying Nie, et al. ∙

research

∙ 12/23/2020

A Survey on Visual Transformer

Transformer is a type of deep neural network mainly based on self-attent...

0 Kai Han, et al. ∙

research

∙ 12/01/2020

Dynamic Feature Pyramid Networks for Object Detection

This paper studies feature pyramid network (FPN), which is a widely used...

0 Mingjian Zhu, et al. ∙

research

∙ 12/01/2020

Pre-Trained Image Processing Transformer

As the computing power of modern hardware is increasing strongly, pre-tr...

52 Hanting Chen, et al. ∙

research

∙ 10/21/2020

SCOP: Scientific Control for Reliable Neural Network Pruning

This paper proposes a reliable neural network pruning algorithm by setti...

0 Yehui Tang, et al. ∙

research

∙ 10/10/2020

Training Binary Neural Networks through Learning with Noisy Supervision

This paper formalizes the binarization operations over neural networks f...

7 Kai Han, et al. ∙

Yunhe Wang

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