Search Results for author: Peng Qiao

Found 12 papers, 2 papers with code

TFDMNet: A Novel Network Structure Combines the Time Domain and Frequency Domain Features

1 code implementation29 Jan 2024 Hengyue Pan, Yixin Chen, Zhiliang Tian, Peng Qiao, Linbo Qiao, Dongsheng Li

To get the balance between the computation complexity and memory usage, we propose a new network structure, namely Time-Frequency Domain Mixture Network (TFDMNet), which combines the advantages of both convolution layers and EMLs.

Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz

no code implementations23 Oct 2023 Tao Sun, Congliang Chen, Peng Qiao, Li Shen, Xinwang Liu, Dongsheng Li

Sign-based stochastic methods have gained attention due to their ability to achieve robust performance despite using only the sign information for parameter updates.

PVP: Pre-trained Visual Parameter-Efficient Tuning

no code implementations26 Apr 2023 Zhao Song, Ke Yang, Naiyang Guan, Junjie Zhu, Peng Qiao, Qingyong Hu

Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks.

Ranked #4 on Image Classification on VTAB-1k (using extra training data)

Fine-Grained Image Classification

Multi-Outputs Is All You Need For Deblur

1 code implementation27 Aug 2022 Sidun Liu, Peng Qiao, Yong Dou

Therefore, we propose to make the network learn the distribution of feasible solutions, and design based on this consideration a novel multi-head output architecture and corresponding loss function for distribution learning.

Deblurring Image Deblurring

Visual Tree Convolutional Neural Network in Image Classification

no code implementations4 Jun 2019 Yuntao Liu, Yong Dou, Ruochun Jin, Peng Qiao

In image classification, Convolutional Neural Network(CNN) models have achieved high performance with the rapid development in deep learning.

Classification General Classification +1

IF-TTN: Information Fused Temporal Transformation Network for Video Action Recognition

no code implementations26 Feb 2019 Ke Yang, Peng Qiao, Dongsheng Li, Yong Dou

Focusing on discriminate spatiotemporal feature learning, we propose Information Fused Temporal Transformation Network (IF-TTN) for action recognition on top of popular Temporal Segment Network (TSN) framework.

Action Recognition Optical Flow Estimation +1

Exploring Frame Segmentation Networks for Temporal Action Localization

no code implementations14 Feb 2019 Ke Yang, Xiaolong Shen, Peng Qiao, Shijie Li, Dongsheng Li, Yong Dou

The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information.

Open-Ended Question Answering Temporal Action Localization

Learning Generic Diffusion Processes for Image Restoration

no code implementations17 Jul 2018 Peng Qiao, Yong Dou, Yunjin Chen, Wensen Feng

On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained.

Denoising Image Restoration

Exploring Temporal Preservation Networks for Precise Temporal Action Localization

no code implementations10 Aug 2017 Ke Yang, Peng Qiao, Dongsheng Li, Shaohe Lv, Yong Dou

A newly proposed work exploits Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the predictions of 3D ConvNets, making it possible to perform per-frame action predictions and achieving promising performance in terms of temporal action localization.

Open-Ended Question Answering Temporal Action Localization +1

Learning Non-local Image Diffusion for Image Denoising

no code implementations24 Feb 2017 Peng Qiao, Yong Dou, Wensen Feng, Yunjin Chen

In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising.

Image Denoising SSIM

Image Denoising via Multi-scale Nonlinear Diffusion Models

no code implementations21 Sep 2016 Wensen Feng, Peng Qiao, Xuanyang Xi, Yunjin Chen

However, in recent two years, discriminatively trained local approaches have started to outperform previous non-local models and have been attracting increasing attentions due to the additional advantage of computational efficiency.

Computational Efficiency Image Denoising

Cannot find the paper you are looking for? You can Submit a new open access paper.