b'Bohan Zhuang'

research

∙ 05/18/2023

PTQD: Accurate Post-Training Quantization for Diffusion Models

Diffusion models have recently dominated image synthesis and other relat...

0 Yefei He, et al. ∙

research

∙ 02/13/2023

Stitchable Neural Networks

The public model zoo containing enormous powerful pretrained model famil...

0 Zizheng Pan, et al. ∙

research

∙ 02/02/2023

A Survey on Efficient Training of Transformers

Recent advances in Transformers have come with a huge requirement on com...

0 Bohan Zhuang, et al. ∙

research

∙ 11/14/2022

BiViT: Extremely Compressed Binary Vision Transformer

Model binarization can significantly compress model size, reduce energy ...

0 Yefei He, et al. ∙

research

∙ 09/19/2022

EcoFormer: Energy-Saving Attention with Linear Complexity

Transformer is a transformative framework that models sequential data an...

0 Jing Liu, et al. ∙

research

∙ 08/23/2022

FocusFormer: Focusing on What We Need via Architecture Sampler

Vision Transformers (ViTs) have underpinned the recent breakthroughs in ...

0 Jing Liu, et al. ∙

research

∙ 07/21/2022

An Efficient Spatio-Temporal Pyramid Transformer for Action Detection

The task of action detection aims at deducing both the action category a...

0 Yuetian Weng, et al. ∙

research

∙ 05/26/2022

Fast Vision Transformers with HiLo Attention

Vision Transformers (ViTs) have triggered the most recent and significan...

0 Zizheng Pan, et al. ∙

research

∙ 03/28/2022

Automated Progressive Learning for Efficient Training of Vision Transformers

Recent advances in vision Transformers (ViTs) have come with a voracious...

10 Changlin Li, et al. ∙

research

∙ 11/24/2021

Sharpness-aware Quantization for Deep Neural Networks

Network quantization is an effective compression method to reduce the mo...

0 Jing Liu, et al. ∙

research

∙ 11/22/2021

Mesa: A Memory-saving Training Framework for Transformers

There has been an explosion of interest in designing high-performance Tr...

15 Zizheng Pan, et al. ∙

research

∙ 08/03/2021

Elastic Architecture Search for Diverse Tasks with Different Resources

We study a new challenging problem of efficient deployment for diverse t...

0 Jing Liu, et al. ∙

research

∙ 05/29/2021

Less is More: Pay Less Attention in Vision Transformers

Transformers have become one of the dominant architectures in deep learn...

0 Zizheng Pan, et al. ∙

research

∙ 05/04/2021

End-to-end One-shot Human Parsing

Previous human parsing models are limited to parsing humans into pre-def...

0 Haoyu He, et al. ∙

research

∙ 03/19/2021

Scalable Visual Transformers with Hierarchical Pooling

The recently proposed Visual image Transformers (ViT) with pure attentio...

0 Zizheng Pan, et al. ∙

research

∙ 01/13/2021

ABS: Automatic Bit Sharing for Model Compression

We present Automatic Bit Sharing (ABS) to automatically search for optim...

0 Jing Liu, et al. ∙

research

∙ 11/29/2020

Fully Quantized Image Super-Resolution Networks

With the rising popularity of intelligent mobile devices, it is of great...

0 Hu Wang, et al. ∙

research

∙ 08/12/2020

FATNN: Fast and Accurate Ternary Neural Networks

Ternary Neural Networks (TNNs) have received much attention due to being...

10 Peng Chen, et al. ∙

research

∙ 07/14/2020

AQD: Towards Accurate Quantized Object Detection

Network quantization aims to lower the bitwidth of weights and activatio...

7 Jing Liu, et al. ∙

research

∙ 04/19/2020

Role-Wise Data Augmentation for Knowledge Distillation

Knowledge Distillation (KD) is a common method for transferring the “kno...

4 Jie Fu, et al. ∙

research

∙ 03/07/2020

Generative Low-bitwidth Data Free Quantization

Neural network quantization is an effective way to compress deep models ...

2 Shoukai Xu, et al. ∙

research

∙ 02/07/2020

Switchable Precision Neural Networks

Instantaneous and on demand accuracy-efficiency trade-off has been recen...

3 Luis Guerra, et al. ∙

research

∙ 02/03/2020

Automatic Pruning for Quantized Neural Networks

Neural network quantization and pruning are two techniques commonly used...

4 Luis Guerra, et al. ∙

research

∙ 01/04/2020

Discrimination-aware Network Pruning for Deep Model Compression

We study network pruning which aims to remove redundant channels/kernels...

21 Jing Liu, et al. ∙

research

∙ 09/22/2019

Structured Binary Neural Networks for Image Recognition

We propose methods to train convolutional neural networks (CNNs) with bo...

0 Bohan Zhuang, et al. ∙

research

∙ 09/05/2019

Training Compact Neural Networks via Auxiliary Overparameterization

It is observed that overparameterization (i.e., designing neural network...

0 Yifan Liu, et al. ∙

research

∙ 08/21/2019

RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs

Binarized convolutional neural networks (BCNNs) are widely used to impro...

0 Chunlei Liu, et al. ∙

research

∙ 08/10/2019

Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations

This paper tackles the problem of training a deep convolutional neural n...

0 Bohan Zhuang, et al. ∙

research

∙ 03/27/2019

Training Quantized Network with Auxiliary Gradient Module

In this paper, we seek to tackle two challenges in training low-precisio...

0 Bohan Zhuang, et al. ∙

research

∙ 11/22/2018

Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation

In this paper, we propose to train convolutional neural networks (CNNs) ...

0 Bohan Zhuang, et al. ∙

research

∙ 11/22/2018

Rethinking Binary Neural Network for Accurate Image Classification and Semantic Segmentation

In this paper, we propose to train a network with both binary weights an...

0 Bohan Zhuang, et al. ∙

research

∙ 10/28/2018

Discrimination-aware Channel Pruning for Deep Neural Networks

Channel pruning is one of the predominant approaches for deep model comp...

0 Zhuangwei Zhuang, et al. ∙

research

∙ 08/08/2018

Training Compact Neural Networks with Binary Weights and Low Precision Activations

In this paper, we propose to train a network with binary weights and low...

0 Bohan Zhuang, et al. ∙

research

∙ 11/17/2017

Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries

Recognising objects according to a pre-defined fixed set of class labels...

0 Bohan Zhuang, et al. ∙

research

∙ 11/01/2017

Towards Effective Low-bitwidth Convolutional Neural Networks

This paper tackles the problem of training a deep convolutional neural n...

0 Bohan Zhuang, et al. ∙

research

∙ 07/07/2017

TasselNet: Counting maize tassels in the wild via local counts regression network

Accurately counting maize tassels is important for monitoring the growth...

0 Hao Lu, et al. ∙

research

∙ 05/28/2017

Care about you: towards large-scale human-centric visual relationship detection

Visual relationship detection aims to capture interactions between pairs...

0 Bohan Zhuang, et al. ∙

research

∙ 03/18/2017

Towards Context-aware Interaction Recognition

Recognizing how objects interact with each other is a crucial task in vi...

0 Bohan Zhuang, et al. ∙

research

∙ 11/30/2016

Sequential Person Recognition in Photo Albums with a Recurrent Network

Recognizing the identities of people in everyday photos is still a very ...

0 Yao Li, et al. ∙

research

∙ 11/30/2016

Attend in groups: a weakly-supervised deep learning framework for learning from web data

Large-scale datasets have driven the rapid development of deep neural ne...

0 Bohan Zhuang, et al. ∙

research

∙ 07/27/2016

Visual Tracking via Shallow and Deep Collaborative Model

In this paper, we propose a robust tracking method based on the collabor...

0 Bohan Zhuang, et al. ∙

research

∙ 03/09/2016

Fast Training of Triplet-based Deep Binary Embedding Networks

In this paper, we aim to learn a mapping (or embedding) from images to a...

0 Bohan Zhuang, et al. ∙

Bohan Zhuang

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