Search Results for author: Yukai Shi

Found 25 papers, 8 papers with code

DreamTime: An Improved Optimization Strategy for Text-to-3D Content Creation

no code implementations21 Jun 2023 Yukun Huang, Jianan Wang, Yukai Shi, Xianbiao Qi, Zheng-Jun Zha, Lei Zhang

Text-to-image diffusion models pre-trained on billions of image-text pairs have recently enabled text-to-3D content creation by optimizing a randomly initialized Neural Radiance Fields (NeRF) with score distillation.

Image Generation Text to 3D

NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution

no code implementations24 May 2023 Yexing Song, Meilin Wang, Xiaoyu Xian, Zhijing Yang, Yuming Fan, Yukai Shi

On the contrary, simple combinations of classical degradation are used for real-world noise modeling, which led to the VSR model often being violated by out-of-distribution noise.

Video Super-Resolution

DreamWaltz: Make a Scene with Complex 3D Animatable Avatars

no code implementations21 May 2023 Yukun Huang, Jianan Wang, Ailing Zeng, He Cao, Xianbiao Qi, Yukai Shi, Zheng-Jun Zha, Lei Zhang

We present DreamWaltz, a novel framework for generating and animating complex 3D avatars given text guidance and parametric human body prior.

Text to 3D

ReGeneration Learning of Diffusion Models with Rich Prompts for Zero-Shot Image Translation

no code implementations8 May 2023 Yupei Lin, Sen Zhang, Xiaojun Yang, Xiao Wang, Yukai Shi

To ensure consistent preservation of the shape during image editing, we propose cross-attention guidance based on regeneration learning.

LipsFormer: Introducing Lipschitz Continuity to Vision Transformers

1 code implementation19 Apr 2023 Xianbiao Qi, Jianan Wang, Yihao Chen, Yukai Shi, Lei Zhang

In contrast to previous practical tricks that address training instability by learning rate warmup, layer normalization, attention formulation, and weight initialization, we show that Lipschitz continuity is a more essential property to ensure training stability.

Open-World Pose Transfer via Sequential Test-Time Adaption

no code implementations20 Mar 2023 Junyang Chen, Xiaoyu Xian, Zhijing Yang, Tianshui Chen, Yongyi Lu, Yukai Shi, Jinshan Pan, Liang Lin

In open-world conditions, the pose transfer task raises various independent signals: OOD appearance and skeleton, which need to be extracted and distributed in speciality.

Motion Synthesis Person Re-Identification +1

OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup

2 code implementations3 Jan 2023 Zhijing Yang, Junyang Chen, Yukai Shi, Hao Li, Tianshui Chen, Liang Lin

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities.

Semantic Parsing Virtual Try-on

DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer

no code implementations28 Jul 2022 Hao Li, Zhijing Yang, Xiaobin Hong, Ziying Zhao, Junyang Chen, Yukai Shi, Jinshan Pan

Real-world image denoising is a practical image restoration problem that aims to obtain clean images from in-the-wild noisy inputs.

Image Denoising Image Restoration

Criteria Comparative Learning for Real-scene Image Super-Resolution

2 code implementations26 Jul 2022 Yukai Shi, Hao Li, Sen Zhang, Zhijing Yang, Xiao Wang

Inspired by the observation that the contrastive relationship could also exist between the criteria, in this work, we propose a novel training paradigm for RealSR, named Criteria Comparative Learning (Cria-CL), by developing contrastive losses defined on criteria instead of image patches.

Contrastive Learning Image Super-Resolution

Real-World Image Super-Resolution by Exclusionary Dual-Learning

1 code implementation6 Jun 2022 Hao Li, Jinghui Qin, Zhijing Yang, Pengxu Wei, Jinshan Pan, Liang Lin, Yukai Shi

Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attention with regard to its tremendous application potentials.

Image Restoration Image Super-Resolution

Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels

1 code implementation26 May 2022 Tao Pu, Tianshui Chen, Hefeng Wu, Yukai Shi, Zhijing Yang, Liang Lin

Specifically, an instance-perspective representation blending (IPRB) module is designed to blend the representations of the known labels in an image with the representations of the corresponding unknown labels in another image to complement these unknown labels.

Image Classification Multi-label Image Recognition with Partial Labels

Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels

1 code implementation23 May 2022 Tianshui Chen, Tao Pu, Lingbo Liu, Yukai Shi, Zhijing Yang, Liang Lin

Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and thus facilitate large-scale MLR.

Multi-label Image Recognition with Partial Labels

Exploring Negatives in Contrastive Learning for Unpaired Image-to-Image Translation

no code implementations23 Apr 2022 Yupei Lin, Sen Zhang, Tianshui Chen, Yongyi Lu, Guangping Li, Yukai Shi

Recently, contrastive learning (CL) has been used to further investigate the image correspondence in unpaired image translation by using patch-based positive/negative learning.

Contrastive Learning Image-to-Image Translation +1

Content-adaptive Representation Learning for Fast Image Super-resolution

no code implementations20 May 2021 Yukai Shi, Jinghui Qin

In contrast to existing studies that ignore difficulty diversity, we adopt different stage of a neural network to perform image restoration.

Image Restoration Image Super-Resolution +1

Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from Cross View and Each View

no code implementations23 Aug 2020 Junpeng Tan, Yukai Shi, Zhijing Yang, Caizhen Wen, Liang Lin

To ensure that we achieve effective sparse representation and clustering performance on the original data matrix, adaptive graph regularization and unsupervised clustering constraints are also incorporated in the proposed model to preserve the internal structural features of the data.


DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-Resolution

1 code implementation25 Feb 2020 Yukai Shi, Haoyu Zhong, Zhijing Yang, Xiaojun Yang, Liang Lin

Previous image SR methods fail to exhibit similar performance on Real-SR as the image data is not aligned inherently.

Image Super-Resolution

Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning

no code implementations4 May 2019 Yukai Shi, Guanbin Li, Qingxing Cao, Keze Wang, Liang Lin

Face hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution~(LR) input.

Face Hallucination reinforcement-learning +2

Difficulty-aware Image Super Resolution via Deep Adaptive Dual-Network

1 code implementation11 Apr 2019 Jinghui Qin, Ziwei Xie, Yukai Shi, Wushao Wen

To identify whether a region is easy or hard, we propose a novel image difficulty recognition network based on PSNR prior.

Image Super-Resolution

Attention-Aware Face Hallucination via Deep Reinforcement Learning

no code implementations CVPR 2017 Qingxing Cao, Liang Lin, Yukai Shi, Xiaodan Liang, Guanbin Li

Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images.

Face Hallucination reinforcement-learning +2

Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning

no code implementations26 Jul 2017 Yukai Shi, Keze Wang, Chongyu Chen, Li Xu, Liang Lin

Single image super resolution (SR), which refers to reconstruct a higher-resolution (HR) image from the observed low-resolution (LR) image, has received substantial attention due to its tremendous application potentials.

Image Restoration Image Super-Resolution +1

Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning

no code implementations25 Jul 2016 Yukai Shi, Keze Wang, Li Xu, Liang Lin

Recently, machine learning based single image super resolution (SR) approaches focus on jointly learning representations for high-resolution (HR) and low-resolution (LR) image patch pairs to improve the quality of the super-resolved images.

Image Super-Resolution Representation Learning

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