no code implementations • 7 May 2024 • Sidun Liu, Peng Qiao, Zongxin Ye, Wenyu Li, Yong Dou
The scalability of our method on reconstruction quality is further evaluated qualitatively and quantitatively.
no code implementations • 16 Apr 2024 • Zongxin Ye, Wenyu Li, Sidun Liu, Peng Qiao, Yong Dou
In this work, we present a comprehensive analysis of the cause of aforementioned artifacts, namely gradient collision, which prevents large Gaussians in over-reconstructed regions from splitting.
1 code implementation • 29 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.
no code implementations • 23 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.
no code implementations • 26 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)
no code implementations • 29 Jan 2023 • Peng Qiao, Sidun Liu, Tao Sun, Ke Yang, Yong Dou
It provides a promising way to introduce the Transformer in low-level vision tasks.
1 code implementation • 27 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.
Ranked #6 on Image Deblurring on GoPro
no code implementations • 4 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.
no code implementations • 26 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.
no code implementations • 14 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.
no code implementations • 17 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.
no code implementations • 10 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
no code implementations • 24 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.
no code implementations • 21 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.