Search Results for author: Gaofeng Meng

Found 23 papers, 8 papers with code

Differentiable Convolution Search for Point Cloud Processing

no code implementations ICCV 2021 Xing Nie, Yongcheng Liu, Shaohong Chen, Jianlong Chang, Chunlei Huo, Gaofeng Meng, Qi Tian, Weiming Hu, Chunhong Pan

It can work in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling.

Alleviating Mode Collapse in GAN via Diversity Penalty Module

no code implementations5 Aug 2021 Sen Pei, Richard Yi Da Xu, Shiming Xiang, Gaofeng Meng

We compare the proposed method with Unrolled GAN (Metz et al. 2016), BourGAN (Xiao, Zhong, and Zheng 2018), PacGAN (Lin et al. 2018), VEEGAN (Srivastava et al. 2017) and ALI (Dumoulin et al. 2016) on 2D synthetic dataset, and results show that the diversity penalty module can help GAN capture much more modes of the data distribution.

Data Augmentation

Density-aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement

no code implementations11 Mar 2021 Chi Zhang, Zihang Lin, Liheng Xu, Zongliang Li, Wei Tang, Yuehu Liu, Gaofeng Meng, Le Wang, Li Li

The key procedure of haze image translation through adversarial training lies in the disentanglement between the feature only involved in haze synthesis, i. e. style feature, and the feature representing the invariant semantic content, i. e. content feature.

Image Generation Translation

Spatio-Temporal Graph Structure Learning for Traffic Forecasting

no code implementations AAAI 2020 Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, Chunhong Pan

To address these issues, we propose a novel framework named Structure Learning Convolution (SLC) that enables to extend the traditional convolutional neural network (CNN) to graph domains and learn the graph structure for traffic forecasting.

Graph structure learning Time Series +1

FontGAN: A Unified Generative Framework for Chinese Character Stylization and De-stylization

no code implementations28 Oct 2019 Xiyan Liu, Gaofeng Meng, Shiming Xiang, Chunhong Pan

In our model, we decouple character images into style representation and content representation, which facilitates more precise control of these two types of variables, thereby improving the quality of the generated results.

Joint haze image synthesis and dehazing with mmd-vae losses

no code implementations15 May 2019 Zongliang Li, Chi Zhang, Gaofeng Meng, Yuehu Liu

Fog and haze are weathers with low visibility which are adversarial to the driving safety of intelligent vehicles equipped with optical sensors like cameras and LiDARs.

Autonomous Driving Image Dehazing +2

Differentiable Architecture Search with Ensemble Gumbel-Softmax

no code implementations6 May 2019 Jianlong Chang, Xinbang Zhang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, Chunhong Pan

For network architecture search (NAS), it is crucial but challenging to simultaneously guarantee both effectiveness and efficiency.

Neural Architecture Search

Deep Discriminative Clustering Analysis

no code implementations5 May 2019 Jianlong Chang, Yiwen Guo, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan

Traditional clustering methods often perform clustering with low-level indiscriminative representations and ignore relationships between patterns, resulting in slight achievements in the era of deep learning.

DetNAS: Backbone Search for Object Detection

1 code implementation NeurIPS 2019 Yukang Chen, Tong Yang, Xiangyu Zhang, Gaofeng Meng, Xinyu Xiao, Jian Sun

In this work, we present DetNAS to use Neural Architecture Search (NAS) for the design of better backbones for object detection.

Classification Fine-tuning +4

Structure-Aware Convolutional Neural Networks

1 code implementation NeurIPS 2018 Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan

Convolutional neural networks (CNNs) are inherently subject to invariable filters that can only aggregate local inputs with the same topological structures.

Action Recognition Activity Detection +3

Joint Neural Architecture Search and Quantization

no code implementations23 Nov 2018 Yukang Chen, Gaofeng Meng, Qian Zhang, Xinbang Zhang, Liangchen Song, Shiming Xiang, Chunhong Pan

Here our goal is to automatically find a compact neural network model with high performance that is suitable for mobile devices.

Model Compression Neural Architecture Search +1

Exploiting Vector Fields for Geometric Rectification of Distorted Document Images

no code implementations ECCV 2018 Gaofeng MENG, Yuanqi SU, Ying Wu, Shiming Xiang, Chunhong Pan

This paper proposes a segment-free method for geometric rectification of a distorted document image captured by a hand-held camera.

Rectification

Reinforced Evolutionary Neural Architecture Search

1 code implementation1 Aug 2018 Yukang Chen, Gaofeng Meng, Qian Zhang, Shiming Xiang, Chang Huang, Lisen Mu, Xinggang Wang

To address this issue, we propose the Reinforced Evolutionary Neural Architecture Search (RE- NAS), which is an evolutionary method with the reinforced mutation for NAS.

Neural Architecture Search Semantic Segmentation

AMVH: Asymmetric Multi-Valued Hashing

no code implementations CVPR 2017 Cheng Da, Shibiao Xu, Kun Ding, Gaofeng Meng, Shiming Xiang, Chunhong Pan

(2) A multi-integer-embedding is employed for compressing the whole database, which is modeled by binary sparse representation with fixed sparsity.

Extraction of Virtual Baselines From Distorted Document Images Using Curvilinear Projection

no code implementations ICCV 2015 Gaofeng Meng, Zuming Huang, Yonghong Song, Shiming Xiang, Chunhong Pan

In this paper, we propose an efficient method for accurate extraction of these virtual visual cues from a curved document image.

Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity

no code implementations2 Sep 2014 Feiyun Zhu, Ying Wang, Bin Fan, Gaofeng Meng, Chunhong Pan

Based on this observation, we exploit a learning-based sparsity method to simultaneously learn the HU results and a sparse guidance map.

Hyperspectral Unmixing

Spectral Unmixing via Data-guided Sparsity

no code implementations13 Mar 2014 Feiyun Zhu, Ying Wang, Bin Fan, Gaofeng Meng, Shiming Xiang, Chunhong Pan

Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral analysis, visualization and understanding.

Hyperspectral Unmixing

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