Search Results for author: Wenhan Luo

Found 29 papers, 10 papers with code

Beyond Monocular Deraining: Stereo Image Deraining via Semantic Understanding

no code implementations ECCV 2020 Kaihao Zhang, Wenhan Luo, Wenqi Ren, Jingwen Wang Fang Zhao, Lin Ma , Hongdong Li

Moreover, even for single image based monocular deraining, many current methods fail to complete the task satisfactorily because they mostly rely on per pixel loss functions and ignoring semantic information.

Rain Removal Semantic Segmentation

Towards Distraction-Robust Active Visual Tracking

no code implementations18 Jun 2021 Fangwei Zhong, Peng Sun, Wenhan Luo, Tingyun Yan, Yizhou Wang

In active visual tracking, it is notoriously difficult when distracting objects appear, as distractors often mislead the tracker by occluding the target or bringing a confusing appearance.

Visual Tracking

T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing

no code implementations5 Jun 2021 Lirong Zheng, Yanshan Li, Kaihao Zhang, Wenhan Luo

In order to reduce network parameters, the intra-stage recursive computation of ResNet is adopted in our Stack T-Net.

Image Dehazing

Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior

no code implementations9 May 2021 Kaihao Zhang, Wenhan Luo, Yanjiang Yu, Wenqi Ren, Fang Zhao, Changsheng Li, Lin Ma, Wei Liu, Hongdong Li

We first use a coarse deraining network to reduce the rain streaks on the input images, and then adopt a pre-trained semantic segmentation network to extract semantic features from the coarse derained image.

Rain Removal Semantic Segmentation

Enhanced Spatio-Temporal Interaction Learning for Video Deraining: A Faster and Better Framework

no code implementations23 Mar 2021 Kaihao Zhang, Dongxu Li, Wenhan Luo, Wen-Yan Lin, Fang Zhao, Wenqi Ren, Wei Liu, Hongdong Li

Video deraining is an important task in computer vision as the unwanted rain hampers the visibility of videos and deteriorates the robustness of most outdoor vision systems.

Rain Removal

Deep Dense Multi-scale Network for Snow Removal Using Semantic and Geometric Priors

no code implementations21 Mar 2021 Kaihao Zhang, Rongqing Li, Yanjiang Yu, Wenhan Luo, Changsheng Li, Hongdong Li

Images captured in snowy days suffer from noticeable degradation of scene visibility, which degenerates the performance of current vision-based intelligent systems.

Image Restoration

Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal

no code implementations12 Mar 2021 Kaihao Zhang, Dongxu Li, Wenhan Luo, Wenqi Ren

In addition, to further refine the result, a Differential-driven Dual Attention-in-Attention Model (D-DAiAM) is proposed with a "heavy-to-light" scheme to remove rain via addressing the unsatisfying deraining regions.

Rain Removal

Benchmarking Ultra-High-Definition Image Super-Resolution

no code implementations ICCV 2021 Kaihao Zhang, Dongxu Li, Wenhan Luo, Wenqi Ren, Bjorn Stenger, Wei Liu, Hongdong Li, Ming-Hsuan Yang

Increasingly, modern mobile devices allow capturing images at Ultra-High-Definition (UHD) resolution, which includes 4K and 8K images.

Image Super-Resolution

Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis

3 code implementations18 Nov 2020 Wen Liu, Zhixin Piao, Zhi Tu, Wenhan Luo, Lin Ma, Shenghua Gao

Also, we build a new dataset, namely iPER dataset, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis.

Denoising Image Generation +1

Deblurring by Realistic Blurring

1 code implementation CVPR 2020 Kaihao Zhang, Wenhan Luo, Yiran Zhong, Lin Ma, Bjorn Stenger, Wei Liu, Hongdong Li

To address this problem, we propose a new method which combines two GAN models, i. e., a learning-to-Blur GAN (BGAN) and learning-to-DeBlur GAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images.

Deblurring

Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video

no code implementations25 Jan 2020 Zhenfang Chen, Lin Ma, Wenhan Luo, Peng Tang, Kwan-Yee K. Wong

In this paper, we study the problem of weakly-supervised temporal grounding of sentence in video.

Fine-grained Image-to-Image Transformation towards Visual Recognition

no code implementations CVPR 2020 Wei Xiong, Yutong He, Yixuan Zhang, Wenhan Luo, Lin Ma, Jiebo Luo

In this paper, we aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image, which can thereby benefit the subsequent fine-grained image recognition and few-shot learning tasks.

Few-Shot Learning Fine-Grained Image Recognition

Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling

no code implementations18 Dec 2019 Tianrui Liu, Wenhan Luo, Lin Ma, Jun-Jie Huang, Tania Stathaki, Tianhong Dai

Ablation studies have validated the effectiveness of both the proposed gated multi-layer feature extraction sub-network and the deformable occlusion handling sub-network.

Occlusion Handling Pedestrian Detection

Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis

2 code implementations ICCV 2019 Wen Liu, Zhixin Piao, Jie Min, Wenhan Luo, Lin Ma, Shenghua Gao

In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape.

Denoising Novel View Synthesis

Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video

1 code implementation ACL 2019 Zhenfang Chen, Lin Ma, Wenhan Luo, Kwan-Yee K. Wong

In this paper, we address a novel task, namely weakly-supervised spatio-temporally grounding natural sentence in video.

Video Object Detection

AD-VAT: An Asymmetric Dueling mechanism for learning Visual Active Tracking

no code implementations ICLR 2019 Fangwei Zhong, Peng Sun, Wenhan Luo, Tingyun Yan, Yizhou Wang

In AD-VAT, both the tracker and the target are approximated by end-to-end neural networks, and are trained via RL in a dueling/competitive manner: i. e., the tracker intends to lockup the target, while the target tries to escape from the tracker.

Learning to Compose Dynamic Tree Structures for Visual Contexts

5 code implementations CVPR 2019 Kaihua Tang, Hanwang Zhang, Baoyuan Wu, Wenhan Luo, Wei Liu

We propose to compose dynamic tree structures that place the objects in an image into a visual context, helping visual reasoning tasks such as scene graph generation and visual Q&A.

Graph Generation Scene Graph Generation +2

Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance

1 code implementation4 Nov 2018 Zechun Liu, Wenhan Luo, Baoyuan Wu, Xin Yang, Wei Liu, Kwang-Ting Cheng

To address the training difficulty, we propose a training algorithm using a tighter approximation to the derivative of the sign function, a magnitude-aware gradient for weight updating, a better initialization method, and a two-step scheme for training a deep network.

Depth Estimation

End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning

no code implementations10 Aug 2018 Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang

We further propose an environment augmentation technique and a customized reward function, which are crucial for successful training.

Object Tracking

Adversarial Spatio-Temporal Learning for Video Deblurring

1 code implementation28 Mar 2018 Kaihao Zhang, Wenhan Luo, Yiran Zhong, Lin Ma, Wei Liu, Hongdong Li

To tackle the second challenge, we leverage the developed DBLRNet as a generator in the GAN (generative adversarial network) architecture, and employ a content loss in addition to an adversarial loss for efficient adversarial training.

Deblurring

Real-Time Neural Style Transfer for Videos

no code implementations CVPR 2017 Hao-Zhi Huang, Hao Wang, Wenhan Luo, Lin Ma, Wenhao Jiang, Xiaolong Zhu, Zhifeng Li, Wei Liu

More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames.

Style Transfer Video Style Transfer

End-to-end Active Object Tracking via Reinforcement Learning

no code implementations ICML 2018 Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang

We study active object tracking, where a tracker takes as input the visual observation (i. e., frame sequence) and produces the camera control signal (e. g., move forward, turn left, etc.).

Object Tracking

Multiple Object Tracking: A Literature Review

no code implementations26 Sep 2014 Wenhan Luo, Junliang Xing, Anton Milan, Xiaoqin Zhang, Wei Liu, Xiaowei Zhao, Tae-Kyun Kim

We inspect the recent advances in various aspects and propose some interesting directions for future research.

Multiple Object Tracking

Bi-label Propagation for Generic Multiple Object Tracking

no code implementations CVPR 2014 Wenhan Luo, Tae-Kyun Kim, Bjorn Stenger, Xiaowei Zhao, Roberto Cipolla

In this paper, we propose a label propagation framework to handle the multiple object tracking (MOT) problem for a generic object type (cf.

Multiple Object Tracking

Unified Face Analysis by Iterative Multi-Output Random Forests

no code implementations CVPR 2014 Xiaowei Zhao, Tae-Kyun Kim, Wenhan Luo

In this paper, we present a unified method for joint face image analysis, i. e., simultaneously estimating head pose, facial expression and landmark positions in real-world face images.

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