Search Results for author: Youliang Yan

Found 16 papers, 9 papers with code

SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers

no code implementations17 Dec 2021 Lin Liu, Shanxin Yuan, Jianzhuang Liu, Xin Guo, Youliang Yan, Qi Tian

For zero-shot image restoration, we design a novel model, termed SiamTrans, which is constructed by Siamese transformers, encoders, and decoders.

Denoising Image Restoration +1

Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables

no code implementations ICCV 2021 Tao Wang, Yong Li, Jingyang Peng, Yipeng Ma, Xian Wang, Fenglong Song, Youliang Yan

One is a 1D weight vector used for image-level scenario adaptation, the other is a 3D weight map aimed for pixel-wise category fusion.

Image Enhancement

Wavelet-Based Network For High Dynamic Range Imaging

no code implementations3 Aug 2021 Tianhong Dai, Wei Li, Xilei Cao, Jianzhuang Liu, Xu Jia, Ales Leonardis, Youliang Yan, Shanxin Yuan

The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details.

Optical Flow Estimation

BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation

9 code implementations CVPR 2020 Hao Chen, Kunyang Sun, Zhi Tian, Chunhua Shen, Yongming Huang, Youliang Yan

The proposed BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer, thus being fast in inference.

Real-time Instance Segmentation Semantic Segmentation

Knowledge Adaptation for Efficient Semantic Segmentation

1 code implementation CVPR 2019 Tong He, Chunhua Shen, Zhi Tian, Dong Gong, Changming Sun, Youliang Yan

To tackle this dilemma, we propose a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride.

Knowledge Distillation Semantic Segmentation

Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation

no code implementations CVPR 2019 Zhi Tian, Tong He, Chunhua Shen, Youliang Yan

In this work, we propose a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs.

Semantic Segmentation

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