Search Results for author: Mengshu Sun

Found 22 papers, 6 papers with code

Continual Few-shot Event Detection via Hierarchical Augmentation Networks

1 code implementation26 Mar 2024 Chenlong Zhang, Pengfei Cao, Yubo Chen, Kang Liu, Zhiqiang Zhang, Mengshu Sun, Jun Zhao

The CFED task is challenging as it involves memorizing previous event types and learning new event types with few-shot samples.

Event Detection

ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models

no code implementations8 Mar 2024 Jun Xu, Mengshu Sun, Zhiqiang Zhang, Jun Zhou

This motivated us to explore domain-specific modeling in chat-based language models as a solution for extracting structured information from natural language.

IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus

1 code implementation22 Feb 2024 Honghao Gui, Lin Yuan, Hongbin Ye, Ningyu Zhang, Mengshu Sun, Lei Liang, Huajun Chen

Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE).

Zero-shot Generalization

Pursing the Sparse Limitation of Spiking Deep Learning Structures

no code implementations18 Nov 2023 Hao Cheng, Jiahang Cao, Erjia Xiao, Mengshu Sun, Le Yang, Jize Zhang, Xue Lin, Bhavya Kailkhura, Kaidi Xu, Renjing Xu

It posits that within dense neural networks, there exist winning tickets or subnetworks that are sparser but do not compromise performance.

Gaining the Sparse Rewards by Exploring Lottery Tickets in Spiking Neural Network

no code implementations23 Sep 2023 Hao Cheng, Jiahang Cao, Erjia Xiao, Mengshu Sun, Renjing Xu

Deploying energy-efficient deep learning algorithms on computational-limited devices, such as robots, is still a pressing issue for real-world applications.


InstructIE: A Bilingual Instruction-based Information Extraction Dataset

3 code implementations19 May 2023 Honghao Gui, Shuofei Qiao, Jintian Zhang, Hongbin Ye, Mengshu Sun, Lei Liang, Huajun Chen, Ningyu Zhang

Traditional information extraction (IE) methodologies, constrained by pre-defined classes and static training paradigms, often falter in adaptability, especially in the dynamic world.

Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training

1 code implementation19 Nov 2022 Zhenglun Kong, Haoyu Ma, Geng Yuan, Mengshu Sun, Yanyue Xie, Peiyan Dong, Xin Meng, Xuan Shen, Hao Tang, Minghai Qin, Tianlong Chen, Xiaolong Ma, Xiaohui Xie, Zhangyang Wang, Yanzhi Wang

Vision transformers (ViTs) have recently obtained success in many applications, but their intensive computation and heavy memory usage at both training and inference time limit their generalization.

HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers

no code implementations15 Nov 2022 Peiyan Dong, Mengshu Sun, Alec Lu, Yanyue Xie, Kenneth Liu, Zhenglun Kong, Xin Meng, Zhengang Li, Xue Lin, Zhenman Fang, Yanzhi Wang

While vision transformers (ViTs) have continuously achieved new milestones in the field of computer vision, their sophisticated network architectures with high computation and memory costs have impeded their deployment on resource-limited edge devices.


Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization

no code implementations10 Aug 2022 Zhengang Li, Mengshu Sun, Alec Lu, Haoyu Ma, Geng Yuan, Yanyue Xie, Hao Tang, Yanyu Li, Miriam Leeser, Zhangyang Wang, Xue Lin, Zhenman Fang

Compared with state-of-the-art ViT quantization work (algorithmic approach only without hardware acceleration), our quantization achieves 0. 47% to 1. 36% higher Top-1 accuracy under the same bit-width.


VAQF: Fully Automatic Software-Hardware Co-Design Framework for Low-Bit Vision Transformer

no code implementations17 Jan 2022 Mengshu Sun, Haoyu Ma, Guoliang Kang, Yifan Jiang, Tianlong Chen, Xiaolong Ma, Zhangyang Wang, Yanzhi Wang

To the best of our knowledge, this is the first time quantization has been incorporated into ViT acceleration on FPGAs with the help of a fully automatic framework to guide the quantization strategy on the software side and the accelerator implementations on the hardware side given the target frame rate.


SPViT: Enabling Faster Vision Transformers via Soft Token Pruning

1 code implementation27 Dec 2021 Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Mengshu Sun, Wei Niu, Xuan Shen, Geng Yuan, Bin Ren, Minghai Qin, Hao Tang, Yanzhi Wang

Moreover, our framework can guarantee the identified model to meet resource specifications of mobile devices and FPGA, and even achieve the real-time execution of DeiT-T on mobile platforms.

Efficient ViTs Model Compression

RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions

no code implementations ICCV 2021 Sung-En Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Sijia Liu, Yanzhi Wang, Xue Lin

Specifically, this is the first effort to assign mixed quantization schemes and multiple precisions within layers -- among rows of the DNN weight matrix, for simplified operations in hardware inference, while preserving accuracy.

Image Classification Quantization

HFSP: A Hardware-friendly Soft Pruning Framework for Vision Transformers

no code implementations29 Sep 2021 Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Mengshu Sun, Wei Niu, Bin Ren, Minghai Qin, Hao Tang, Yanzhi Wang

Recently, Vision Transformer (ViT) has continuously established new milestones in the computer vision field, while the high computation and memory cost makes its propagation in industrial production difficult.

Image Classification Model Compression

Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework

no code implementations8 Dec 2020 Sung-En Chang, Yanyu Li, Mengshu Sun, Runbin Shi, Hayden K. -H. So, Xuehai Qian, Yanzhi Wang, Xue Lin

Unlike existing methods that use the same quantization scheme for all weights, we propose the first solution that applies different quantization schemes for different rows of the weight matrix.

Edge-computing Model Compression +1

MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework

no code implementations16 Sep 2020 Sung-En Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Runbin Shi, Xue Lin, Yanzhi Wang

To tackle the limited computing and storage resources in edge devices, model compression techniques have been widely used to trim deep neural network (DNN) models for on-device inference execution.

Edge-computing Image Denoising +2

RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices

no code implementations20 Jul 2020 Wei Niu, Mengshu Sun, Zhengang Li, Jou-An Chen, Jiexiong Guan, Xipeng Shen, Yanzhi Wang, Sijia Liu, Xue Lin, Bin Ren

The vanilla sparsity removes whole kernel groups, while KGS sparsity is a more fine-grained structured sparsity that enjoys higher flexibility while exploiting full on-device parallelism.

Code Generation Model Compression

Towards an Efficient and General Framework of Robust Training for Graph Neural Networks

no code implementations25 Feb 2020 Kaidi Xu, Sijia Liu, Pin-Yu Chen, Mengshu Sun, Caiwen Ding, Bhavya Kailkhura, Xue Lin

To overcome these limitations, we propose a general framework which leverages the greedy search algorithms and zeroth-order methods to obtain robust GNNs in a generic and an efficient manner.

SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency

no code implementations23 Jan 2020 Zhengang Li, Yifan Gong, Xiaolong Ma, Sijia Liu, Mengshu Sun, Zheng Zhan, Zhenglun Kong, Geng Yuan, Yanzhi Wang

Structured weight pruning is a representative model compression technique of DNNs for hardware efficiency and inference accelerations.

Model Compression

Adversarial T-shirt! Evading Person Detectors in A Physical World

1 code implementation ECCV 2020 Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, Xue Lin

To the best of our knowledge, this is the first work that models the effect of deformation for designing physical adversarial examples with respect to-rigid objects such as T-shirts.

Interpreting Adversarial Examples by Activation Promotion and Suppression

no code implementations3 Apr 2019 Kaidi Xu, Sijia Liu, Gaoyuan Zhang, Mengshu Sun, Pu Zhao, Quanfu Fan, Chuang Gan, Xue Lin

It is widely known that convolutional neural networks (CNNs) are vulnerable to adversarial examples: images with imperceptible perturbations crafted to fool classifiers.

Adversarial Robustness

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