Search Results for author: Haoqiang Fan

Found 18 papers, 10 papers with code

iShape: A First Step Towards Irregular Shape Instance Segmentation

no code implementations30 Sep 2021 Lei Yang, Yan Zi Wei, Yisheng He, Wei Sun, Zhenhang Huang, Haibin Huang, Haoqiang Fan

In this paper, we introduce a brand new dataset to promote the study of instance segmentation for objects with irregular shapes.

Instance Segmentation Semantic Segmentation

ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

2 code implementations22 May 2021 Zhen Liu, Wenjie Lin, Xinpeng Li, Qing Rao, Ting Jiang, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu

In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet.

ASFlow: Unsupervised Optical Flow Learning with Adaptive Pyramid Sampling

no code implementations8 Apr 2021 Kunming Luo, Ao Luo, Chuan Wang, Haoqiang Fan, Shuaicheng Liu

Equipped with these two modules, our method achieves the best performance for unsupervised optical flow estimation on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.

Optical Flow Estimation

Motion Basis Learning for Unsupervised Deep Homography Estimation with Subspace Projection

no code implementations ICCV 2021 Nianjin Ye, Chuan Wang, Haoqiang Fan, Shuaicheng Liu

Last, we propose a Feature Identity Loss (FIL) to enforce the learned image feature warp-equivariant, meaning that the result should be identical if the order of warp operation and feature extraction is swapped.

Homography Estimation

D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution

no code implementations26 Mar 2021 Youwei Li, Haibin Huang, Lanpeng Jia, Haoqiang Fan, Shuaicheng Liu

Rethinking both, we learn the distribution of underlying high-frequency details in a discrete form and propose a two-stage pipeline: divergence stage to convergence stage.

Image Super-Resolution SSIM

FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation

3 code implementations CVPR 2021 Yisheng He, Haibin Huang, Haoqiang Fan, Qifeng Chen, Jian Sun

Moreover, at the output representation stage, we designed a simple but effective 3D keypoints selection algorithm considering the texture and geometry information of objects, which simplifies keypoint localization for precise pose estimation.

6D Pose Estimation Representation Learning

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

3 code implementations CVPR 2021 Shen Cheng, Yuzhi Wang, Haibin Huang, Donghao Liu, Haoqiang Fan, Shuaicheng Liu

Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space.

Image Denoising SSIM

UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning

1 code implementation CVPR 2021 Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, Jian Sun

By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.

Optical Flow Estimation

Disentangled Image Matting

no code implementations ICCV 2019 Shaofan Cai, Xiaoshuai Zhang, Haoqiang Fan, Haibin Huang, Jiangyu Liu, Jiaming Liu, Jiaying Liu, Jue Wang, Jian Sun

Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap.

Image Matting

Deep Fusion Network for Image Completion

3 code implementations17 Apr 2019 Xin Hong, Pengfei Xiong, Renhe Ji, Haoqiang Fan

The fusion block not only provides a smooth fusion between restored and existing content, but also provides an attention map to make network focus more on the unknown pixels.

Image Inpainting

DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation

2 code implementations CVPR 2019 Hanchao Li, Pengfei Xiong, Haoqiang Fan, Jian Sun

This paper introduces an extremely efficient CNN architecture named DFANet for semantic segmentation under resource constraints.

Real-Time Semantic Segmentation

A Point Set Generation Network for 3D Object Reconstruction from a Single Image

4 code implementations CVPR 2017 Haoqiang Fan, Hao Su, Leonidas Guibas

Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image.

Ranked #2 on 3D Reconstruction on Data3D−R2N2 (using extra training data)

3D Object Reconstruction From A Single Image 3D Reconstruction

Learning Deep Face Representation

no code implementations12 Mar 2014 Haoqiang Fan, Zhimin Cao, Yuning Jiang, Qi Yin, Chinchilla Doudou

Our basic network is capable of achieving high recognition accuracy ($85. 8\%$ on LFW benchmark) with only 8 dimension representation.

Face Recognition

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