Search Results for author: Pengxu Wei

Found 10 papers, 6 papers with code

Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution

1 code implementation7 May 2022 Xiaoqian Xu, Pengxu Wei, Weikai Chen, Mingzhi Mao, Liang Lin, Guanbin Li

To address this issue, we propose an unsupervised domain adaptation mechanism for real-world SR, named Dual ADversarial Adaptation (DADA), which only requires LR images in the target domain with available real paired data from a source camera.

Image Super-Resolution Unsupervised Domain Adaptation

Open Set Domain Adaptation By Novel Class Discovery

no code implementations7 Mar 2022 Jingyu Zhuang, Ziliang Chen, Pengxu Wei, Guanbin Li, Liang Lin

In Open Set Domain Adaptation (OSDA), large amounts of target samples are drawn from the implicit categories that never appear in the source domain.

Domain Adaptation

Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning

no code implementations ICCV 2021 Junkai Huang, Chaowei Fang, Weikai Chen, Zhenhua Chai, Xiaolin Wei, Pengxu Wei, Liang Lin, Guanbin Li

Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data.

CDNet: Centripetal Direction Network for Nuclear Instance Segmentation

2 code implementations ICCV 2021 Hongliang He, Zhongyi Huang, Yao Ding, Guoli Song, Lin Wang, Qian Ren, Pengxu Wei, Zhiqiang Gao, Jie Chen

Specifically, we define the centripetal direction feature as a class of adjacent directions pointing to the nuclear center to represent the spatial relationship between pixels within the nucleus.

Instance Segmentation Semantic Segmentation

Component Divide-and-Conquer for Real-World Image Super-Resolution

1 code implementation ECCV 2020 Pengxu Wei, Ziwei Xie, Hannan Lu, Zongyuan Zhan, Qixiang Ye, WangMeng Zuo, Liang Lin

Learning an SR model with conventional pixel-wise loss usually is easily dominated by flat regions and edges, and fails to infer realistic details of complex textures.

Image Super-Resolution

Generalizing Energy-based Generative ConvNets from Particle Evolution Perspective

no code implementations31 Oct 2019 Yang Wu, Xu Cai, Pengxu Wei, Guanbin Li, Liang Lin

Compared with Generative Adversarial Networks (GAN), Energy-Based generative Models (EBMs) possess two appealing properties: i) they can be directly optimized without requiring an auxiliary network during the learning and synthesizing; ii) they can better approximate underlying distribution of the observed data by learning explicitly potential functions.

Min-Entropy Latent Model for Weakly Supervised Object Detection

1 code implementation CVPR 2018 Fang Wan, Pengxu Wei, Zhenjun Han, Jianbin Jiao, Qixiang Ye

Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors.

Image Classification Weakly Supervised Object Detection +1

3D Human Pose Machines with Self-supervised Learning

2 code implementations 2019 Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, Pengxu Wei

Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests.

3D Human Pose Estimation Self-Supervised Learning

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