Search Results for author: Longguang Wang

Found 47 papers, 31 papers with code

AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers

no code implementations7 Feb 2025 Runqing Jiang, Ye Zhang, Longguang Wang, Pengpeng Yu, Yulan Guo

Post-training quantization (PTQ) has emerged as a promising solution for reducing the storage and computational cost of vision transformers (ViTs).

Image Classification Instance Segmentation +5

Layout2Scene: 3D Semantic Layout Guided Scene Generation via Geometry and Appearance Diffusion Priors

no code implementations5 Jan 2025 Minglin Chen, Longguang Wang, Sheng Ao, Ye Zhang, Kai Xu, Yulan Guo

To fully leverage 2D diffusion priors in geometry and appearance generation, we introduce a semantic-guided geometry diffusion model and a semantic-geometry guided diffusion model which are finetuned on a scene dataset.

Scene Generation Text to 3D

Pluggable Style Representation Learning for Multi-Style Transfer

2 code implementations Asian Conference on Computer Vision 2024 Hongda Liu, Longguang Wang, Weijun Guan, Ye Zhang, Yulan Guo

Specifically, for style modeling, we propose a style representation learning scheme to encode the style information into a compact representation.

Representation Learning Style Transfer

VideoDirector: Precise Video Editing via Text-to-Video Models

no code implementations26 Nov 2024 Yukun Wang, Longguang Wang, Zhiyuan Ma, Qibin Hu, Kai Xu, Yulan Guo

Despite the typical inversion-then-editing paradigm using text-to-image (T2I) models has demonstrated promising results, directly extending it to text-to-video (T2V) models still suffers severe artifacts such as color flickering and content distortion.

Attribute Video Editing

Preserving Full Degradation Details for Blind Image Super-Resolution

1 code implementation1 Jul 2024 Hongda Liu, Longguang Wang, Ye Zhang, Kaiwen Xue, Shunbo Zhou, Yulan Guo

In addition, we develop an energy distance loss to facilitate the learning of the degradation representations by introducing a bounded constraint.

Image Super-Resolution

SpecDETR: A Transformer-based Hyperspectral Point Object Detection Network

1 code implementation16 May 2024 Zhaoxu Li, Wei An, Gaowei Guo, Longguang Wang, Yingqian Wang, Zaiping Lin

Hyperspectral target detection (HTD) aims to identify specific materials based on spectral information in hyperspectral imagery and can detect extremely small objects, some of which occupy a smaller than one-pixel area.

Binary Classification Decoder +3

Mask-ControlNet: Higher-Quality Image Generation with An Additional Mask Prompt

no code implementations8 Apr 2024 Zhiqi Huang, Huixin Xiong, Haoyu Wang, Longguang Wang, Zhiheng Li

Then, the object images are employed as additional prompts to facilitate the diffusion model to better understand the relationship between foreground and background regions during image generation.

Text-to-Image Generation

Lightweight Pixel Difference Networks for Efficient Visual Representation Learning

1 code implementation1 Feb 2024 Zhuo Su, Jiehua Zhang, Longguang Wang, Hua Zhang, Zhen Liu, Matti Pietikäinen, Li Liu

With PDC and Bi-PDC, we further present two lightweight deep networks named \emph{Pixel Difference Networks (PiDiNet)} and \emph{Binary PiDiNet (Bi-PiDiNet)} respectively to learn highly efficient yet more accurate representations for visual tasks including edge detection and object recognition.

Edge Detection Object Recognition +1

Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution

no code implementations CVPR 2024 Longguang Wang, Juncheng Li, Yingqian Wang, Qingyong Hu, Yulan Guo

The difficulty of acquiring high-resolution (HR) and low-resolution (LR) image pairs in real scenarios limits the performance of existing learning-based image super-resolution (SR) methods in the real world.

Diversity Image Generation +1

Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos

no code implementations ICCV 2023 Xiaoxiao Sheng, Zhiqiang Shen, Gang Xiao, Longguang Wang, Yulan Guo, Hehe Fan

Instead of contrasting the representations of clips or frames, in this paper, we propose a unified self-supervised framework by conducting contrastive learning at the point level.

Contrastive Learning Representation Learning +1

Deep Semantic Graph Matching for Large-scale Outdoor Point Clouds Registration

no code implementations10 Aug 2023 Shaocong Liu, Tao Wang, Yan Zhang, Ruqin Zhou, Li Li, Chenguang Dai, Yongsheng Zhang, Longguang Wang, Hanyun Wang

The adjacent points with the same category labels are then clustered together using the Euclidean clustering algorithm to obtain the semantic instances, which are represented by three kinds of attributes including spatial location information, semantic categorical information, and global geometric shape information.

Graph Matching Point Cloud Registration +1

PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos

1 code implementation CVPR 2023 Zhiqiang Shen, Xiaoxiao Sheng, Longguang Wang, Yulan Guo, Qiong Liu, Xi Zhou

Self-supervised learning can extract representations of good quality from solely unlabeled data, which is appealing for point cloud videos due to their high labelling cost.

Self-Supervised Learning Transfer Learning

NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

1 code implementation20 Apr 2023 Yingqian Wang, Longguang Wang, Zhengyu Liang, Jungang Yang, Radu Timofte, Yulan Guo

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4.

Image Super-Resolution

Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection

1 code implementation ICCV 2023 Boyang Li, Yingqian Wang, Longguang Wang, Fei Zhang, Ting Liu, Zaiping Lin, Wei An, Yulan Guo

The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters.

Clustering

Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution

1 code implementation ICCV 2023 Zhengyu Liang, Yingqian Wang, Longguang Wang, Jungang Yang, Shilin Zhou, Yulan Guo

Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities among LF images.

Image Super-Resolution

VAPCNet: Viewpoint-Aware 3D Point Cloud Completion

no code implementations ICCV 2023 Zhiheng Fu, Longguang Wang, Lian Xu, Zhiyong Wang, Hamid Laga, Yulan Guo, Farid Boussaid, Mohammed Bennamoun

In this paper, we thus propose an unsupervised viewpoint representation learning scheme for 3D point cloud completion without explicit viewpoint estimation.

Point Cloud Completion Representation Learning +1

Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution

1 code implementation19 Jul 2022 Xiaoyu Dong, Naoto Yokoya, Longguang Wang, Tatsumi Uezato

Self-supervised cross-modal super-resolution (SR) can overcome the difficulty of acquiring paired training data, but is challenging because only low-resolution (LR) source and high-resolution (HR) guide images from different modalities are available.

Super-Resolution

Real-World Light Field Image Super-Resolution via Degradation Modulation

3 code implementations13 Jun 2022 Yingqian Wang, Zhengyu Liang, Longguang Wang, Jungang Yang, Wei An, Yulan Guo

In our method, a practical LF degradation model is developed to formulate the degradation process of real LF images.

Image Super-Resolution

NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

no code implementations20 Apr 2022 Longguang Wang, Yulan Guo, Yingqian Wang, Juncheng Li, Shuhang Gu, Radu Timofte

In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results.

Stereo Image Super-Resolution

Decoupling Makes Weakly Supervised Local Feature Better

1 code implementation CVPR 2022 Kunhong Li, Longguang Wang, Li Liu, Qing Ran, Kai Xu, Yulan Guo

Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences.

Camera Localization Image Matching +1

Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution

1 code implementation4 Jan 2022 Xinyi Ying, Yingqian Wang, Longguang Wang, Weidong Sheng, Li Liu, Zaiping Lin, Shilin Zhou

Specifically, motivated by the local motion prior in the spatio-temporal dimension, we propose a local spatio-temporal attention module to perform implicit frame alignment and incorporate the local spatio-temporal information to enhance the local features (especially for small targets).

Super-Resolution

Learnable Lookup Table for Neural Network Quantization

1 code implementation CVPR 2022 Longguang Wang, Xiaoyu Dong, Yingqian Wang, Li Liu, Wei An, Yulan Guo

Since a linear quantizer (i. e., round(*) function) cannot well fit the bell-shaped distributions of weights and activations, many existing methods use pre-defined functions (e. g., exponential function) with learnable parameters to build the quantizer for joint optimization.

Computational Efficiency Image Classification +3

A Systematic Survey of Deep Learning-based Single-Image Super-Resolution

1 code implementation29 Sep 2021 Juncheng Li, Zehua Pei, Wenjie Li, Guangwei Gao, Longguang Wang, Yingqian Wang, Tieyong Zeng

This is an exhaustive survey of SISR, which can help researchers better understand SISR and inspire more exciting research in this field.

Deep Learning Image Quality Assessment +2

Light Field Image Super-Resolution with Transformers

1 code implementation17 Aug 2021 Zhengyu Liang, Yingqian Wang, Longguang Wang, Jungang Yang, Shilin Zhou

With the proposed angular and spatial Transformers, the beneficial information in an LF can be fully exploited and the SR performance is boosted.

Image Super-Resolution

Symmetric Parallax Attention for Stereo Image Super-Resolution

1 code implementation7 Nov 2020 Yingqian Wang, Xinyi Ying, Longguang Wang, Jungang Yang, Wei An, Yulan Guo

Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used.

Occlusion Handling Stereo Image Super-Resolution

Parallax Attention for Unsupervised Stereo Correspondence Learning

2 code implementations16 Sep 2020 Longguang Wang, Yulan Guo, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An

Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks.

Stereo Image Super-Resolution Stereo Matching

Light Field Image Super-Resolution Using Deformable Convolution

1 code implementation7 Jul 2020 Yingqian Wang, Jungang Yang, Longguang Wang, Xinyi Ying, Tianhao Wu, Wei An, Yulan Guo

In this paper, we propose a deformable convolution network (i. e., LF-DFnet) to handle the disparity problem for LF image SR.

Image Super-Resolution

Deformable 3D Convolution for Video Super-Resolution

1 code implementation6 Apr 2020 Xinyi Ying, Longguang Wang, Yingqian Wang, Weidong Sheng, Wei An, Yulan Guo

In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR.

Motion Compensation Video Super-Resolution

Deep Video Super-Resolution using HR Optical Flow Estimation

2 code implementations6 Jan 2020 Longguang Wang, Yulan Guo, Li Liu, Zaiping Lin, Xinpu Deng, Wei An

The key challenge for video SR lies in the effective exploitation of temporal dependency between consecutive frames.

Motion Compensation Optical Flow Estimation +1

Spatial-Angular Interaction for Light Field Image Super-Resolution

1 code implementation17 Dec 2019 Yingqian Wang, Longguang Wang, Jungang Yang, Wei An, Jingyi Yu, Yulan Guo

Specifically, spatial and angular features are first separately extracted from input LFs, and then repetitively interacted to progressively incorporate spatial and angular information.

Image Super-Resolution SSIM

DeOccNet: Learning to See Through Foreground Occlusions in Light Fields

1 code implementation10 Dec 2019 Yingqian Wang, Tianhao Wu, Jungang Yang, Longguang Wang, Wei An, Yulan Guo

In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet).

Decoder

Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution

no code implementations15 Mar 2019 Yingqian Wang, Longguang Wang, Jungang Yang, Wei An, Yulan Guo

With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo image pairs.

Stereo Image Super-Resolution

Learning Parallax Attention for Stereo Image Super-Resolution

1 code implementation CVPR 2019 Longguang Wang, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo

Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint.

Stereo Image Super-Resolution

Learning for Video Super-Resolution through HR Optical Flow Estimation

2 code implementations23 Sep 2018 Longguang Wang, Yulan Guo, Zaiping Lin, Xinpu Deng, Wei An

Extensive experiments demonstrate that HR optical flows provide more accurate correspondences than their LR counterparts and improve both accuracy and consistency performance.

Motion Compensation Optical Flow Estimation +1

Fast single image super-resolution based on sigmoid transformation

no code implementations23 Aug 2017 Longguang Wang, Zaiping Lin, Jinyan Gao, Xinpu Deng, Wei An

Single image super-resolution aims to generate a high-resolution image from a single low-resolution image, which is of great significance in extensive applications.

Image Super-Resolution

Multi-frame image super-resolution with fast upscaling technique

no code implementations20 Jun 2017 Longguang Wang, Zaiping Lin, Xinpu Deng, Wei An

In this paper, we propose an end-to-end fast upscaling technique to replace the interpolation operator, design upscaling filters in LR space for periodic sub-locations respectively and shuffle the filter results to derive the final reconstruction errors in HR space.

Image Super-Resolution

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