Search Results for author: Yingchen Yu

Found 17 papers, 8 papers with code

Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors

no code implementations24 Aug 2022 Gongjie Zhang, Zhipeng Luo, Yingchen Yu, Zichen Tian, Jingyi Zhang, Shijian Lu

Multi-scale features have been proven highly effective for object detection, and most ConvNet-based object detectors adopt Feature Pyramid Network (FPN) as a basic component for exploiting multi-scale features.

object-detection Object Detection

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

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

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

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

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

Accelerating DETR Convergence via Semantic-Aligned Matching

1 code implementation CVPR 2022 Gongjie Zhang, Zhipeng Luo, Yingchen Yu, Kaiwen Cui, Shijian Lu

First, it projects object queries into the same embedding space as encoded image features, where the matching can be accomplished efficiently with aligned semantics.

object-detection Object Detection

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

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

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

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

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