Search Results for author: Xianming Liu

Found 56 papers, 30 papers with code

SDGE: Stereo Guided Depth Estimation for 360° Camera Sets

no code implementations19 Feb 2024 Jialei Xu, Xianming Liu, Junjun Jiang, Xiangyang Ji

We suggest building virtual pinhole cameras to resolve the distortion problem of fisheye cameras and unify the processing for the two types of 360{\deg} cameras.

3D Object Detection Autonomous Driving +2

A Comprehensive Survey on 3D Content Generation

1 code implementation2 Feb 2024 Jian Liu, Xiaoshui Huang, Tianyu Huang, Lu Chen, Yuenan Hou, Shixiang Tang, Ziwei Liu, Wanli Ouyang, WangMeng Zuo, Junjun Jiang, Xianming Liu

Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e. g., text, image, video, audio and 3D.

Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning

no code implementations12 Jan 2024 Chenyang Wang, Junjun Jiang, Xingyu Hu, Xianming Liu, Xiangyang Ji

Using the measurement, we analyze existing techniques for inverting samples and get some insightful information that inspires a novel loss function to reduce the inconsistency.

Class Incremental Learning Incremental Learning

HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models

no code implementations11 Jan 2024 Hanzhang Wang, Haoran Wang, Jinze Yang, Zhongrui Yu, Zeke Xie, Lei Tian, Xinyan Xiao, Junjun Jiang, Xianming Liu, Mingming Sun

In the specific, our model is constructed based on Latent Diffusion Model (LDM) and elaborately designed to absorb content and style instance as conditions of LDM.

Style Transfer

Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach

1 code implementation11 Jan 2024 Gang Wu, Junjun Jiang, Junpeng Jiang, Xianming Liu

Recent progress in single-image super-resolution (SISR) has achieved remarkable performance, yet the computational costs of these methods remain a challenge for deployment on resource-constrained devices.

Image Super-Resolution

On the Dynamics Under the Unhinged Loss and Beyond

no code implementations13 Dec 2023 Xiong Zhou, Xianming Liu, Hanzhang Wang, Deming Zhai, Junjun Jiang, Xiangyang Ji

In this paper, we introduce the unhinged loss, a concise loss function, that offers more mathematical opportunities to analyze the closed-form dynamics while requiring as few simplifications or assumptions as possible.

Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration

2 code implementations12 Sep 2023 Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu

Our approach, named Model Contrastive Learning for Image Restoration (MCLIR), rejuvenates latency models as negative models, making it compatible with diverse image restoration tasks.

Contrastive Learning Image Dehazing +3

Fully $1\times1$ Convolutional Network for Lightweight Image Super-Resolution

1 code implementation30 Jul 2023 Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu

By incorporating a parameter-free spatial-shift operation, it equips the fully $1\times1$ convolutional network with powerful representation capability while impressive computational efficiency.

Computational Efficiency Image Super-Resolution

Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study

no code implementations28 May 2023 Yiqi Zhong, Xianming Liu, Deming Zhai, Junjun Jiang, Xiangyang Ji

Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series.

Adversarial Robustness Backdoor Attack +3

Super-Resolving Face Image by Facial Parsing Information

1 code implementation6 Apr 2023 Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Deming Zhai, Xianming Liu

In this paper, we build a novel parsing map guided face super-resolution network which extracts the face prior (i. e., parsing map) directly from low-resolution face image for the following utilization.


Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution

1 code implementation25 Mar 2023 Gang Wu, Junjun Jiang, Yuanchao Bai, Xianming Liu

Building upon the NA module, we propose a lightweight single image super-resolution (SISR) network named TCSR.

Image Super-Resolution

Image Deblurring by Exploring In-depth Properties of Transformer

1 code implementation24 Mar 2023 Pengwei Liang, Junjun Jiang, Xianming Liu, Jiayi Ma

We demonstrate the effectiveness of transformer properties in improving the perceptual quality while not sacrificing the quantitative scores (PSNR) over the most competitive models, such as Uformer, Restormer, and NAFNet, on defocus deblurring and motion deblurring tasks.

Deblurring Image Deblurring

Guided Depth Map Super-resolution: A Survey

1 code implementation19 Feb 2023 Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Xiangyang Ji

Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image, is a longstanding and fundamental problem, it has attracted considerable attention from computer vision and image processing communities.

Depth Image Upsampling Depth Map Super-Resolution +1

Spatial-Frequency Mutual Learning for Face Super-Resolution

1 code implementation CVPR 2023 Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Xianming Liu

To circumvent this problem, Fourier transform is introduced, which can capture global facial structure information and achieve image-size receptive field.


Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation

no code implementations5 Oct 2022 Jialei Xu, Xianming Liu, Yuanchao Bai, Junjun Jiang, Kaixuan Wang, Xiaozhi Chen, Xiangyang Ji

During the iterative update, the results of depth estimation are compared across cameras and the information of overlapping areas is propagated to the whole depth maps with the help of basis formulation.

Depth Prediction Monocular Depth Estimation

Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression

1 code implementation11 Sep 2022 Yuanchao Bai, Xianming Liu, Kai Wang, Xiangyang Ji, Xiaolin Wu, Wen Gao

In the lossless mode, the DLPR coding system first performs lossy compression and then lossless coding of residuals.

Image Compression

LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices

1 code implementation2 Sep 2022 Zhenyu Li, Zehui Chen, Jialei Xu, Xianming Liu, Junjun Jiang

Notably, our solution named LiteDepth ranks 2nd in the MAI&AIM2022 Monocular Depth Estimation Challenge}, with a si-RMSE of 0. 311, an RMSE of 3. 79, and the inference time is 37$ms$ tested on the Raspberry Pi 4.

Data Augmentation Monocular Depth Estimation

Prototype-Anchored Learning for Learning with Imperfect Annotations

no code implementations23 Jun 2022 Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji

We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.

Learning Towards the Largest Margins

no code implementations ICLR 2022 Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji

One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power.

Face Verification imbalanced classification +1

Towards Model Generalization for Monocular 3D Object Detection

no code implementations23 May 2022 Zhenyu Li, Zehui Chen, Ang Li, Liangji Fang, Qinhong Jiang, Xianming Liu, Junjun Jiang

However, caused by severe domain gaps (e. g., the field of view (FOV), pixel size, and object size among datasets), Mono3D detectors have difficulty in generalization, leading to drastic performance degradation on unseen domains.

Autonomous Driving Monocular 3D Object Detection +3

GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting

no code implementations15 May 2022 Zeyu Lu, Junjun Jiang, Junqin Huang, Gang Wu, Xianming Liu

Our proposed GLaMa can better capture different types of missing information by using more types of masks.

Image Inpainting SSIM

Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation

1 code implementation CVPR 2022 Wenbo Zhao, Xianming Liu, Zhiwei Zhong, Junjun Jiang, Wei Gao, Ge Li, Xiangyang Ji

Most existing methods either take the end-to-end supervised learning based manner, where large amounts of pairs of sparse input and dense ground-truth are exploited as supervision information; or treat up-scaling of different scale factors as independent tasks, and have to build multiple networks to handle upsampling with varying factors.

Self-Supervised Learning

BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

1 code implementation3 Apr 2022 Zhenyu Li, Xuyang Wang, Xianming Liu, Junjun Jiang

Recently, some methods reformulate it as a classification-regression task to boost the model performance, where continuous depth is estimated via a linear combination of predicted probability distributions and discrete bins.

Ranked #15 on Monocular Depth Estimation on KITTI Eigen split (using extra training data)

Monocular Depth Estimation regression +1

Exploiting the Potential of Datasets: A Data-Centric Approach for Model Robustness

1 code implementation10 Mar 2022 Yiqi Zhong, Lei Wu, Xianming Liu, Junjun Jiang

Robustness of deep neural networks (DNNs) to malicious perturbations is a hot topic in trustworthy AI.

Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon

1 code implementation CVPR 2022 Yiqi Zhong, Xianming Liu, Deming Zhai, Junjun Jiang, Xiangyang Ji

A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector.

Adversarial Attack Traffic Sign Recognition +1

Towards End-to-End Image Compression and Analysis with Transformers

1 code implementation17 Dec 2021 Yuanchao Bai, Xu Yang, Xianming Liu, Junjun Jiang, YaoWei Wang, Xiangyang Ji, Wen Gao

Meanwhile, we propose a feature aggregation module to fuse the compressed features with the selected intermediate features of the Transformer, and feed the aggregated features to a deconvolutional neural network for image reconstruction.

Classification Image Classification +3

Deep Attentional Guided Image Filtering

1 code implementation13 Dec 2021 Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Xiangyang Ji

Specifically, we propose an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target, respectively, and then adaptively combine them by modeling the pixel-wise dependency between the two images.

Collaborative Filtering Depth Image Upsampling +1

SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations

1 code implementation9 Dec 2021 Zhenyu Li, Zehui Chen, Ang Li, Liangji Fang, Qinhong Jiang, Xianming Liu, Junjun Jiang, Bolei Zhou, Hang Zhao

To bridge this gap, we aim to learn a spatial-aware visual representation that can describe the three-dimensional space and is more suitable and effective for these tasks.

Contrastive Learning Unsupervised Pre-training

A Practical Contrastive Learning Framework for Single-Image Super-Resolution

1 code implementation27 Nov 2021 Gang Wu, Junjun Jiang, Xianming Liu

Contrastive learning has achieved remarkable success on various high-level tasks, but there are fewer contrastive learning-based methods proposed for low-level tasks.

Contrastive Learning Image Restoration +1

Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data

no code implementations23 Sep 2021 Jialei Xu, Yuanchao Bai, Xianming Liu, Junjun Jiang, Xiangyang Ji

In this paper, we propose a novel weakly-supervised framework to train a monocular depth estimation network to generate HR depth maps with resolution-mismatched supervision, i. e., the inputs are HR color images and the ground-truth are low-resolution (LR) depth maps.

Monocular Depth Estimation

From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution

no code implementations31 Aug 2021 Junjun Jiang, Chenyang Wang, Xianming Liu, Kui Jiang, Jiayi Ma

By this spectral splitting and aggregation strategy (SSAS), we can divide the original hyperspectral image into multiple samples (\emph{from less to more}) to support the efficient training of the network and effectively exploit the spectral correlations among spectrum.

Image Super-Resolution

Learning with Noisy Labels via Sparse Regularization

1 code implementation ICCV 2021 Xiong Zhou, Xianming Liu, Chenyang Wang, Deming Zhai, Junjun Jiang, Xiangyang Ji

In this paper, we theoretically prove that \textbf{any loss can be made robust to noisy labels} by restricting the network output to the set of permutations over a fixed vector.

Learning with noisy labels

Physics-Based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging

no code implementations CVPR 2021 Feilong Zhang, Xianming Liu, Cheng Guo, Shiyi Lin, Junjun Jiang, Xiangyang Ji

Specifically, we unfold the iterative process of the alternative projection phase retrieval into a feed-forward neural network, whose layers mimic the processing flow.


Learning Scalable lY=-Constrained Near-Lossless Image Compression via Joint Lossy Image and Residual Compression

no code implementations CVPR 2021 Yuanchao Bai, Xianming Liu, WangMeng Zuo, YaoWei Wang, Xiangyang Ji

To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.

Image Compression

BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring

1 code implementation31 May 2021 Pengwei Liang, Junjun Jiang, Xianming Liu, Jiayi Ma

In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the DP data, which measures the defocus disparity between the left and right views.

Deblurring Image Defocus Deblurring +1

High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion

1 code implementation4 Apr 2021 Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Zhiwen Chen, Xiangyang Ji

Specifically, to effectively extract and combine relevant information from LR depth and HR guidance, we propose a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhance block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics.

Depth Map Super-Resolution

Learning Scalable $\ell_\infty$-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression

no code implementations31 Mar 2021 Yuanchao Bai, Xianming Liu, WangMeng Zuo, YaoWei Wang, Xiangyang Ji

To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.

Image Compression

When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

2 code implementations14 Jun 2017 Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, Thomas S. Huang

Conventionally, image denoising and high-level vision tasks are handled separately in computer vision.

Image Denoising

Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation

no code implementations8 Dec 2016 Xianming Liu, Amy Zhang, Tobias Tiecke, Andreas Gros, Thomas S. Huang

Learning from weakly-supervised data is one of the main challenges in machine learning and computer vision, especially for tasks such as image semantic segmentation where labeling is extremely expensive and subjective.

Semantic Segmentation Weakly-supervised Learning

Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images

no code implementations7 Jul 2016 Xianming Liu, Gene Cheung, Xiaolin Wu, Debin Zhao

In this paper, we combine three image priors---Laplacian prior for DCT coefficients, sparsity prior and graph-signal smoothness prior for image patches---to construct an efficient JPEG soft decoding algorithm.

Clustering Image Reconstruction +1

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