Search Results for author: Jiaming Liu

Found 49 papers, 18 papers with code

Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-world

no code implementations2 Dec 2022 Yulu Gan, Mingjie Pan, Rongyu Zhang, Zijian Ling, Lingran Zhao, Jiaming Liu, Shanghang Zhang

To enable the device model to deal with changing environments, we propose a new learning paradigm of Cloud-Device Collaborative Continual Adaptation, which encourages collaboration between cloud and device and improves the generalization of the device model.

object-detection Object Detection

BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks

no code implementations2 Dec 2022 Xiaowei Chi, Jiaming Liu, Ming Lu, Rongyu Zhang, Zhaoqing Wang, Yandong Guo, Shanghang Zhang

In order to find them, we further propose a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDAR to determine the heights of local slices.

3D Object Detection Autonomous Driving +1

DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

no code implementations22 Nov 2022 Jiaming Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Stewart He, K. Aditya Mohan, Ulugbek S. Kamilov, Hyojin Kim

Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine.

DOLPH: Diffusion Models for Phase Retrieval

no code implementations1 Nov 2022 Shirin Shoushtari, Jiaming Liu, Ulugbek S. Kamilov

Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements.


Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model

no code implementations1 Nov 2022 Junhao Hu, Shirin Shoushtari, Zihao Zou, Jiaming Liu, Zhixin Sun, Ulugbek S. Kamilov

Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors.

Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees

no code implementations7 Oct 2022 Weijie Gan, Chunwei Ying, Parna Eshraghi, Tongyao Wang, Cihat Eldeniz, Yuyang Hu, Jiaming Liu, Yasheng Chen, Hongyu An, Ulugbek S. Kamilov

Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data.

Image Reconstruction

StyleSwap: Style-Based Generator Empowers Robust Face Swapping

no code implementations27 Sep 2022 Zhiliang Xu, Hang Zhou, Zhibin Hong, Ziwei Liu, Jiaming Liu, Zhizhi Guo, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang

Our core idea is to leverage a style-based generator to empower high-fidelity and robust face swapping, thus the generator's advantage can be adopted for optimizing identity similarity.

Face Swapping

Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN

no code implementations23 Sep 2022 Tomas Kerepecky, Jiaming Liu, Xue Wen Ng, David W. Piston, Ulugbek S. Kamilov

Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane.

Image Reconstruction

Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer

1 code implementation26 Aug 2022 Jiaming Liu, Qizhe Zhang, Jianing Li, Ming Lu, Tiejun Huang, Shanghang Zhang

Neuromorphic spike data, an upcoming modality with high temporal resolution, has shown promising potential in real-world applications due to its inherent advantage to overcome high-velocity motion blur.

Autonomous Driving Depth Estimation +2

Uncertainty Guided Depth Fusion for Spike Camera

no code implementations26 Aug 2022 Jianing Li, Jiaming Liu, Xiaobao Wei, Jiyuan Zhang, Ming Lu, Lei Ma, Li Du, Tiejun Huang, Shanghang Zhang

In this paper, we propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera.

Autonomous Driving Stereo Depth Estimation

Deep Model-Based Architectures for Inverse Problems under Mismatched Priors

no code implementations26 Jul 2022 Shirin Shoushtari, Jiaming Liu, Yuyang Hu, Ulugbek S. Kamilov

While the empirical performance and theoretical properties of DMBAs have been widely investigated, the existing work in the area has primarily focused on their performance when the desired image prior is known exactly.

Efficient Meta-Tuning for Content-aware Neural Video Delivery

1 code implementation20 Jul 2022 Xiaoqi Li, Jiaming Liu, Shizun Wang, Cheng Lyu, Ming Lu, Yurong Chen, Anbang Yao, Yandong Guo, Shanghang Zhang

Our method significantly reduces the computational cost and achieves even better performance, paving the way for applying neural video delivery techniques to practical applications.


Online Deep Equilibrium Learning for Regularization by Denoising

1 code implementation25 May 2022 Jiaming Liu, Xiaojian Xu, Weijie Gan, Shirin Shoushtari, Ulugbek S. Kamilov

However, the dependence of the computational/memory complexity of the measurement models in PnP/RED on the total number of measurements leaves DEQ impractical for many imaging applications.


Few-Shot Font Generation by Learning Fine-Grained Local Styles

2 code implementations CVPR 2022 Licheng Tang, Yiyang Cai, Jiaming Liu, Zhibin Hong, Mingming Gong, Minhu Fan, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang

Instead of explicitly disentangling global or component-wise modeling, the cross-attention mechanism can attend to the right local styles in the reference glyphs and aggregate the reference styles into a fine-grained style representation for the given content glyphs.

Font Generation

MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer

1 code implementation3 May 2022 Jinze Yu, Jiaming Liu, Xiaobao Wei, Haoyi Zhou, Yohei Nakata, Denis Gudovskiy, Tomoyuki Okuno, JianXin Li, Kurt Keutzer, Shanghang Zhang

To solve this problem, we propose an end-to-end cross-domain detection Transformer based on the mean teacher framework, MTTrans, which can fully exploit unlabeled target domain data in object detection training and transfer knowledge between domains via pseudo labels.

Domain Adaptation object-detection +1

Few-Shot Head Swapping in the Wild

no code implementations CVPR 2022 Changyong Shu, Hemao Wu, Hang Zhou, Jiaming Liu, Zhibin Hong, Changxing Ding, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang

Particularly, seamless blending is achieved with the help of a Semantic-Guided Color Reference Creation procedure and a Blending UNet.

Face Swapping

Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth

no code implementations10 Apr 2022 Weijie Gan, Cihat Eldeniz, Jiaming Liu, Sihao Chen, Hongyu An, Ulugbek S. Kamilov

We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors.

Image Reconstruction

Monotonically Convergent Regularization by Denoising

no code implementations10 Feb 2022 Yuyang Hu, Jiaming Liu, Xiaojian Xu, Ulugbek S. Kamilov

Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image denoisers as image priors.

Compressive Sensing Deblurring +2

SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution

1 code implementation30 Nov 2021 Shizun Wang, Ming Lu, Kaixin Chen, Jiaming Liu, Xiaoqi Li, Chuang Zhang, Ming Wu

However, existing methods mostly train the DNNs on uniformly sampled LR-HR patch pairs, which makes them fail to fully exploit informative patches within the image.

Data Augmentation Image Super-Resolution +1

Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative $R_2^\ast$ Mapping

1 code implementation3 Sep 2021 Xiaojian Xu, Satya V. V. N. Kothapalli, Jiaming Liu, Sayan Kahali, Weijie Gan, Dmitriy A. Yablonskiy, Ulugbek S. Kamilov

LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of $R_2^\ast$ maps, while LEARN-BIO directly performs motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ estimation.

Deformation-Compensated Learning for Image Reconstruction without Ground Truth

1 code implementation12 Jul 2021 Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, Ulugbek S. Kamilov

Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets.

Image Reconstruction

Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition

1 code implementation NeurIPS 2021 Jiaming Liu, M. Salman Asif, Brendt Wohlberg, Ulugbek S. Kamilov

The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors.

Compressive Sensing Denoising

CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems

1 code implementation9 Feb 2021 Yu Sun, Jiaming Liu, Mingyang Xie, Brendt Wohlberg, Ulugbek S. Kamilov

We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements.

Image Reconstruction

SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees

1 code implementation22 Jan 2021 Jiaming Liu, Yu Sun, Weijie Gan, Xiaojian Xu, Brendt Wohlberg, Ulugbek S. Kamilov

Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems.

Contextual Graph Reasoning Networks

no code implementations1 Jan 2021 Zhaoqing Wang, Jiaming Liu, Yangyuxuan Kang, Mingming Gong, Chuang Zhang, Ming Lu, Ming Wu

Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks.

Instance Segmentation Pose Estimation +1

Joint Reconstruction and Calibration using Regularization by Denoising

no code implementations26 Nov 2020 Mingyang Xie, Yu Sun, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov

Cal-RED extends the traditional RED methodology to imaging problems that require the calibration of the measurement operator.

Denoising Image Reconstruction

Practical Deep Raw Image Denoising on Mobile Devices

1 code implementation ECCV 2020 Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, Jue Wang

Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets.

Image Denoising

Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors

no code implementations ICLR 2021 Yu Sun, Jiaming Liu, Yiran Sun, Brendt Wohlberg, Ulugbek S. Kamilov

Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors.


Deep Image Reconstruction using Unregistered Measurements without Groundtruth

no code implementations29 Sep 2020 Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, Ulugbek S. Kamilov

One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images.

Image Reconstruction

Provable Convergence of Plug-and-Play Priors with MMSE denoisers

no code implementations15 May 2020 Xiaojian Xu, Yu Sun, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov

Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser.

Compressive Sensing Image Reconstruction

FGSD: A Dataset for Fine-Grained Ship Detection in High Resolution Satellite Images

no code implementations15 Mar 2020 Kaiyan Chen, Ming Wu, Jiaming Liu, Chuang Zhang

To further promote the research of ship detection, we introduced a new fine-grained ship detection datasets, which is named as FGSD.

Infusing Learned Priors into Model-Based Multispectral Imaging

no code implementations20 Sep 2019 Jiaming Liu, Yu Sun, Ulugbek S. Kamilov

We introduce a new algorithm for regularized reconstruction of multispectral (MS) images from noisy linear measurements.

Denoising Image Reconstruction

EATEN: Entity-aware Attention for Single Shot Visual Text Extraction

1 code implementation20 Sep 2019 He guo, Xiameng Qin, Jiaming Liu, Junyu Han, Jingtuo Liu, Errui Ding

Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts.

Entity Extraction using GAN Optical Character Recognition

Chinese Street View Text: Large-scale Chinese Text Reading with Partially Supervised Learning

no code implementations ICCV 2019 Yipeng Sun, Jiaming Liu, Wei Liu, Junyu Han, Errui Ding, Jingtuo Liu

Most existing text reading benchmarks make it difficult to evaluate the performance of more advanced deep learning models in large vocabularies due to the limited amount of training data.

Disentangled Image Matting

no code implementations ICCV 2019 Shaofan Cai, Xiaoshuai Zhang, Haoqiang Fan, Haibin Huang, Jiangyu Liu, Jiaming Liu, Jiaying Liu, Jue Wang, Jian Sun

Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap.

Image Matting

Online Regularization by Denoising with Applications to Phase Retrieval

no code implementations4 Sep 2019 Zihui Wu, Yu Sun, Jiaming Liu, Ulugbek S. Kamilov

Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems.

Denoising Retrieval

Editing Text in the Wild

2 code implementations8 Aug 2019 Liang Wu, Chengquan Zhang, Jiaming Liu, Junyu Han, Jingtuo Liu, Errui Ding, Xiang Bai

Specifically, we propose an end-to-end trainable style retention network (SRNet) that consists of three modules: text conversion module, background inpainting module and fusion module.

Image Inpainting Image-to-Image Translation +1

Semi-supervised Skin Detection by Network with Mutual Guidance

no code implementations ICCV 2019 Yi He, Jiayuan Shi, Chuan Wang, Haibin Huang, Jiaming Liu, Guanbin Li, Risheng Liu, Jue Wang

In this paper we present a new data-driven method for robust skin detection from a single human portrait image.

Block Coordinate Regularization by Denoising

1 code implementation NeurIPS 2019 Yu Sun, Jiaming Liu, Ulugbek S. Kamilov

In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables.


Frame-Recurrent Video Inpainting by Robust Optical Flow Inference

no code implementations8 May 2019 Yifan Ding, Chuan Wang, Haibin Huang, Jiaming Liu, Jue Wang, Liqiang Wang

Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently.

Image Inpainting Optical Flow Estimation +1

Detecting Text in the Wild with Deep Character Embedding Network

no code implementations2 Jan 2019 Jiaming Liu, Chengquan Zhang, Yipeng Sun, Junyu Han, Errui Ding

However, text in the wild is usually perspectively distorted or curved, which can not be easily tackled by existing approaches.

TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network

no code implementations24 Dec 2018 Yipeng Sun, Chengquan Zhang, Zuming Huang, Jiaming Liu, Junyu Han, Errui Ding

Reading text from images remains challenging due to multi-orientation, perspective distortion and especially the curved nature of irregular text.

Optical Character Recognition

Image Restoration using Total Variation Regularized Deep Image Prior

no code implementations30 Oct 2018 Jiaming Liu, Yu Sun, Xiaojian Xu, Ulugbek S. Kamilov

In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction.

Deblurring Image Denoising +2

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