Search Results for author: Dongliang He

Found 25 papers, 17 papers with code

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction

2 code implementations ICCV 2021 Songhua Liu, Tianwei Lin, Dongliang He, Fu Li, Ruifeng Deng, Xin Li, Errui Ding, Hao Wang

Neural painting refers to the procedure of producing a series of strokes for a given image and non-photo-realistically recreating it using neural networks.

Object Detection Style Transfer

AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer

2 code implementations ICCV 2021 Songhua Liu, Tianwei Lin, Dongliang He, Fu Li, Meiling Wang, Xin Li, Zhengxing Sun, Qian Li, Errui Ding

Finally, the content feature is normalized so that they demonstrate the same local feature statistics as the calculated per-point weighted style feature statistics.

Style Transfer Video Style Transfer

Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings

2 code implementations15 Jun 2021 Hengyuan Zhao, Wenhao Wu, Yihao Liu, Dongliang He

In this paper, we present a fast exemplar-based image colorization approach using color embeddings named Color2Embed.

Colorization Semantic correspondence

Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness

1 code implementation28 Apr 2021 Manyu Zhu, Dongliang He, Xin Li, Chao Li, Fu Li, Xiao Liu, Errui Ding, Zhaoxiang Zhang

Inpainting arbitrary missing regions is challenging because learning valid features for various masked regions is nontrivial.

Image Inpainting

Learning Semantic Person Image Generation by Region-Adaptive Normalization

1 code implementation CVPR 2021 Zhengyao Lv, Xiaoming Li, Xin Li, Fu Li, Tianwei Lin, Dongliang He, WangMeng Zuo

In the first stage, we predict the target semantic parsing maps to eliminate the difficulties of pose transfer and further benefit the latter translation of per-region appearance style.

Pose Transfer Semantic Parsing +1

Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer

2 code implementations CVPR 2021 Tianwei Lin, Zhuoqi Ma, Fu Li, Dongliang He, Xin Li, Errui Ding, Nannan Wang, Jie Li, Xinbo Gao

Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle).

Style Transfer

MVFNet: Multi-View Fusion Network for Efficient Video Recognition

2 code implementations13 Dec 2020 Wenhao Wu, Dongliang He, Tianwei Lin, Fu Li, Chuang Gan, Errui Ding

Existing state-of-the-art methods have achieved excellent accuracy regardless of the complexity meanwhile efficient spatiotemporal modeling solutions are slightly inferior in performance.

Action Classification Action Recognition +1

RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning

1 code implementation27 Oct 2020 Peihao Chen, Deng Huang, Dongliang He, Xiang Long, Runhao Zeng, Shilei Wen, Mingkui Tan, Chuang Gan

We study unsupervised video representation learning that seeks to learn both motion and appearance features from unlabeled video only, which can be reused for downstream tasks such as action recognition.

Representation Learning Self-Supervised Action Recognition +1

HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network

1 code implementation15 Oct 2020 Pengcheng Yuan, Shufei Lin, Cheng Cui, Yuning Du, Ruoyu Guo, Dongliang He, Errui Ding, Shumin Han

Moreover, Hierarchical-Split block is very flexible and efficient, which provides a large space of potential network architectures for different applications.

Image Classification Instance Segmentation +2

Dynamic Inference: A New Approach Toward Efficient Video Action Recognition

no code implementations9 Feb 2020 Wenhao Wu, Dongliang He, Xiao Tan, Shifeng Chen, Yi Yang, Shilei Wen

In a nutshell, we treat input frames and network depth of the computational graph as a 2-dimensional grid, and several checkpoints are placed on this grid in advance with a prediction module.

Action Recognition Action Recognition In Videos +1

Multi-Label Classification with Label Graph Superimposing

2 code implementations21 Nov 2019 Ya Wang, Dongliang He, Fu Li, Xiang Long, Zhichao Zhou, Jinwen Ma, Shilei Wen

In this paper, we propose a label graph superimposing framework to improve the conventional GCN+CNN framework developed for multi-label recognition in the following two aspects.

Classification General Classification +2

TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation

no code implementations14 Oct 2019 Fan Yang, Xiao Liu, Dongliang He, Chuang Gan, Jian Wang, Chao Li, Fu Li, Shilei Wen

In this work, we introduce a new problem, named as {\em story-preserving long video truncation}, that requires an algorithm to automatically truncate a long-duration video into multiple short and attractive sub-videos with each one containing an unbroken story.

Video Summarization

Deep Concept-wise Temporal Convolutional Networks for Action Localization

2 code implementations26 Aug 2019 Xin Li, Tianwei Lin, Xiao Liu, Chuang Gan, WangMeng Zuo, Chao Li, Xiang Long, Dongliang He, Fu Li, Shilei Wen

In this paper, we empirically find that stacking more conventional temporal convolution layers actually deteriorates action classification performance, possibly ascribing to that all channels of 1D feature map, which generally are highly abstract and can be regarded as latent concepts, are excessively recombined in temporal convolution.

Action Classification Action Localization

Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution

no code implementations7 May 2019 Chao Li, Dongliang He, Xiao Liu, Yukang Ding, Shilei Wen

Recently, image super-resolution has been widely studied and achieved significant progress by leveraging the power of deep convolutional neural networks.

Image Super-Resolution Video Super-Resolution

Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos

1 code implementation21 Jan 2019 Dongliang He, Xiang Zhao, Jizhou Huang, Fu Li, Xiao Liu, Shilei Wen

The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos.

Decision Making Multi-Task Learning

StNet: Local and Global Spatial-Temporal Modeling for Action Recognition

3 code implementations5 Nov 2018 Dongliang He, Zhichao Zhou, Chuang Gan, Fu Li, Xiao Liu, Yandong Li, Li-Min Wang, Shilei Wen

In this paper, in contrast to the existing CNN+RNN or pure 3D convolution based approaches, we explore a novel spatial temporal network (StNet) architecture for both local and global spatial-temporal modeling in videos.

Action Recognition

Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition

no code implementations27 Jun 2018 Dongliang He, Fu Li, Qijie Zhao, Xiang Long, Yi Fu, Shilei Wen

In this challenge, we propose spatial-temporal network (StNet) for better joint spatial-temporal modelling and comprehensively video understanding.

Action Recognition Video Understanding

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