Search Results for author: Fangneng Zhan

Found 31 papers, 10 papers with code

Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution

no code implementations4 Aug 2022 Jiahui Zhang, Fangneng Zhan, Yingchen Yu, Rongliang Wu, Xiaoqin Zhang, Shijian Lu

In addition, stochastic noises fed to the generator are employed for unconditional detail generation, which tends to produce unfaithful details that compromise the fidelity of the generated SR image.

Code Generation Disentanglement +2

Auto-regressive Image Synthesis with Integrated Quantization

no code implementations21 Jul 2022 Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Kaiwen Cui, Changgong Zhang, Shijian Lu

Extensive experiments over multiple conditional image generation tasks show that our method achieves superior diverse image generation performance qualitatively and quantitatively as compared with the state-of-the-art.

Conditional Image Generation Inductive Bias +1

Towards Counterfactual Image Manipulation via CLIP

1 code implementation6 Jul 2022 Yingchen Yu, Fangneng Zhan, Rongliang Wu, Jiahui Zhang, Shijian Lu, Miaomiao Cui, Xuansong Xie, Xian-Sheng Hua, Chunyan Miao

In addition, we design a simple yet effective scheme that explicitly maps CLIP embeddings (of target text) to the latent space and fuses them with latent codes for effective latent code optimization and accurate editing.

Image Manipulation

VMRF: View Matching Neural Radiance Fields

no code implementations6 Jul 2022 Jiahui Zhang, Fangneng Zhan, Rongliang Wu, Yingchen Yu, Wenqing Zhang, Bai Song, Xiaoqin Zhang, Shijian Lu

With the feature transport plan as the guidance, a novel pose calibration technique is designed which rectifies the initially randomized camera poses by predicting relative pose transformations between the pair of rendered and real images.

Novel View Synthesis

Marginal Contrastive Correspondence for Guided Image Generation

no code implementations CVPR 2022 Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Changgong Zhang

We design a Marginal Contrastive Learning Network (MCL-Net) that explores contrastive learning to learn domain-invariant features for realistic exemplar-based image translation.

Contrastive Learning Image Generation +2

Fourier Document Restoration for Robust Document Dewarping and Recognition

1 code implementation CVPR 2022 Chuhui Xue, Zichen Tian, Fangneng Zhan, Shijian Lu, Song Bai

State-of-the-art document dewarping techniques learn to predict 3-dimensional information of documents which are prone to errors while dealing with documents with irregular distortions or large variations in depth.

Modulated Contrast for Versatile Image Synthesis

1 code implementation CVPR 2022 Fangneng Zhan, Jiahui Zhang, Yingchen Yu, Rongliang Wu, Shijian Lu

Perceiving the similarity between images has been a long-standing and fundamental problem underlying various visual generation tasks.

Contrastive Learning Image Generation

Multimodal Image Synthesis and Editing: A Survey

1 code implementation27 Dec 2021 Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Lingjie Liu, Adam Kortylewski, Christian Theobalt, Eric Xing

As information exists in various modalities in real world, effective interaction and fusion among multimodal information plays a key role for the creation and perception of multimodal data in computer vision and deep learning research.

Image Generation

GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data

1 code implementation4 Oct 2021 Kaiwen Cui, Jiaxing Huang, Zhipeng Luo, Gongjie Zhang, Fangneng Zhan, Shijian Lu

Specifically, we design GenCo, a Generative Co-training network that mitigates the discriminator over-fitting issue by introducing multiple complementary discriminators that provide diverse supervision from multiple distinctive views in training.

Data Augmentation Image Generation

WaveFill: A Wavelet-based Generation Network for Image Inpainting

1 code implementation ICCV 2021 Yingchen Yu, Fangneng Zhan, Shijian Lu, Jianxiong Pan, Feiying Ma, Xuansong Xie, Chunyan Miao

This paper presents WaveFill, a wavelet-based inpainting network that decomposes images into multiple frequency bands and fills the missing regions in each frequency band separately and explicitly.

Image Inpainting

Conditional Directed Graph Convolution for 3D Human Pose Estimation

1 code implementation16 Jul 2021 WenBo Hu, Changgong Zhang, Fangneng Zhan, Lei Zhang, Tien-Tsin Wong

Based on this representation, we further propose a spatial-temporal conditional directed graph convolution to leverage varying non-local dependence for different poses by conditioning the graph topology on input poses.

3D Human Pose Estimation

Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation

1 code implementation12 Jul 2021 Aoran Xiao, Jiaxing Huang, Dayan Guan, Fangneng Zhan, Shijian Lu

Extensive experiments show that SynLiDAR provides a high-quality data source for studying 3D transfer and the proposed PCT achieves superior point cloud translation consistently across the three setups.

3D Unsupervised Domain Adaptation Data Augmentation +3

Bi-level Feature Alignment for Versatile Image Translation and Manipulation

2 code implementations7 Jul 2021 Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Kaiwen Cui, Aoran Xiao, Shijian Lu, Chunyan Miao

This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance in image generation by explicitly building a correspondence.

Image Generation Translation

Blind Image Super-Resolution via Contrastive Representation Learning

no code implementations1 Jul 2021 Jiahui Zhang, Shijian Lu, Fangneng Zhan, Yingchen Yu

Extensive experiments on synthetic datasets and real images show that the proposed CRL-SR can handle multi-modal and spatially variant degradation effectively under blind settings and it also outperforms state-of-the-art SR methods qualitatively and quantitatively.

Contrastive Learning Image Super-Resolution +1

Sparse Needlets for Lighting Estimation with Spherical Transport Loss

no code implementations ICCV 2021 Fangneng Zhan, Changgong Zhang, WenBo Hu, Shijian Lu, Feiying Ma, Xuansong Xie, Ling Shao

Accurate lighting estimation is challenging yet critical to many computer vision and computer graphics tasks such as high-dynamic-range (HDR) relighting.

Lighting Estimation

Diverse Image Inpainting with Bidirectional and Autoregressive Transformers

no code implementations26 Apr 2021 Yingchen Yu, Fangneng Zhan, Rongliang Wu, Jianxiong Pan, Kaiwen Cui, Shijian Lu, Feiying Ma, Xuansong Xie, Chunyan Miao

With image-level attention, transformers enable to model long-range dependencies and generate diverse contents with autoregressive modeling of pixel-sequence distributions.

Image Inpainting Language Modelling

GMLight: Lighting Estimation via Geometric Distribution Approximation

1 code implementation20 Feb 2021 Fangneng Zhan, Yingchen Yu, Changgong Zhang, Rongliang Wu, WenBo Hu, Shijian Lu, Feiying Ma, Xuansong Xie, Ling Shao

This paper presents Geometric Mover's Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation.

Lighting Estimation regression

EMLight: Lighting Estimation via Spherical Distribution Approximation

no code implementations21 Dec 2020 Fangneng Zhan, Changgong Zhang, Yingchen Yu, Yuan Chang, Shijian Lu, Feiying Ma, Xuansong Xie

Motivated by the Earth Mover distance, we design a novel spherical mover's loss that guides to regress light distribution parameters accurately by taking advantage of the subtleties of spherical distribution.

Lighting Estimation regression

Adversarial Image Composition with Auxiliary Illumination

no code implementations17 Sep 2020 Fangneng Zhan, Shijian Lu, Changgong Zhang, Feiying Ma, Xuansong Xie

State-of-the-art methods strive to harmonize the composed image by adapting the style of foreground objects to be compatible with the background image, whereas the potential shadow of foreground objects within the composed image which is critical to the composition realism is largely neglected.

Towards Realistic 3D Embedding via View Alignment

no code implementations14 Jul 2020 Changgong Zhang, Fangneng Zhan, Shijian Lu, Feiying Ma, Xuansong Xie

Recent advances in generative adversarial networks (GANs) have achieved great success in automated image composition that generates new images by embedding interested foreground objects into background images automatically.

Spatial-Aware GAN for Unsupervised Person Re-identification

no code implementations26 Nov 2019 Changgong Zhang, Fangneng Zhan

The recent person re-identification research has achieved great success by learning from a large number of labeled person images.

Unsupervised Domain Adaptation Unsupervised Person Re-Identification

GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition

no code implementations ICCV 2019 Fangneng Zhan, Chuhui Xue, Shijian Lu

Recent adversarial learning research has achieved very impressive progress for modelling cross-domain data shifts in appearance space but its counterpart in modelling cross-domain shifts in geometry space lags far behind.

Domain Adaptation Scene Text Detection

Hierarchy Composition GAN for High-fidelity Image Synthesis

no code implementations12 May 2019 Fangneng Zhan, Jiaxing Huang, Shijian Lu

Despite the rapid progress of generative adversarial networks (GANs) in image synthesis in recent years, the existing image synthesis approaches work in either geometry domain or appearance domain alone which often introduces various synthesis artifacts.

Image Generation

Scene Text Synthesis for Efficient and Effective Deep Network Training

no code implementations26 Jan 2019 Changgong Zhang, Fangneng Zhan, Hongyuan Zhu, Shijian Lu

Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique - the use of our synthesized images in deep network training is capable of achieving similar or even better scene text detection and scene text recognition performance as compared with using real images.

Image Generation Scene Text Detection +1

ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification

no code implementations CVPR 2019 Fangneng Zhan, Shijian Lu

Automated recognition of texts in scenes has been a research challenge for years, largely due to the arbitrary variation of text appearances in perspective distortion, text line curvature, text styles and different types of imaging artifacts.

Scene Text Recognition

Spatial Fusion GAN for Image Synthesis

no code implementations CVPR 2019 Fangneng Zhan, Hongyuan Zhu, Shijian Lu

Recent advances in generative adversarial networks (GANs) have shown great potentials in realistic image synthesis whereas most existing works address synthesis realism in either appearance space or geometry space but few in both.

Image Generation

Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping

no code implementations ECCV 2018 Chuhui Xue, Shijian Lu, Fangneng Zhan

This paper presents a scene text detection technique that exploits bootstrapping and text border semantics for accurate localization of texts in scenes.

Scene Text Detection

Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes

no code implementations ECCV 2018 Fangneng Zhan, Shijian Lu, Chuhui Xue

This paper presents a novel image synthesis technique that aims to generate a large amount of annotated scene text images for training accurate and robust scene text detection and recognition models.

Image Generation Scene Text Detection +1

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