Search Results for author: Jinjian Wu

Found 19 papers, 11 papers with code

Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement

no code implementations14 Feb 2024 Huachen Fang, Jinjian Wu, Qibin Hou, Weisheng Dong, Guangming Shi

Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs.

Denoising

Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration

no code implementations23 Jan 2024 Yifan Zhang, Siyu Ren, Junhui Hou, Jinjian Wu, Guangming Shi

First, we propose the learnable transformation alignment to bridge the domain gap between image and point cloud data, converting features into a unified representation space for effective comparison and matching.

3D Semantic Segmentation Autonomous Driving +4

Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment

1 code implementation5 Jan 2024 Yongxu Liu, Yinghui Quan, Guoyao Xiao, Aobo Li, Jinjian Wu

In this work, instead of stacking up models, a more elegant data sampling method (named as SAMA, scaling and masking) is explored, which compacts both the local and global content in a regular input size.

Video Quality Assessment

Segment Any Events via Weighted Adaptation of Pivotal Tokens

1 code implementation24 Dec 2023 Zhiwen Chen, Zhiyu Zhu, Yifan Zhang, Junhui Hou, Guangming Shi, Jinjian Wu

One pivotal issue at the heart of this endeavor is the precise alignment and calibration of embeddings derived from event-centric data such that they harmoniously coincide with those originating from RGB imagery.

Event-based Object Segmentation

Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI

no code implementations20 Jun 2023 Lintao Zhang, Jinjian Wu, Lihong Wang, Li Wang, David C. Steffens, Shijun Qiu, Guy G. Potter, Mingxia Liu

Besides the encoder, the pretext model also contains two decoders for two auxiliary tasks (i. e., MRI reconstruction and brain tissue segmentation), while the downstream model relies on a predictor for classification.

Anatomy MRI Reconstruction +1

Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning

no code implementations3 Jan 2023 Xiaoyang Chen, Jinjian Wu, Wenjiao Lyu, Yicheng Zou, Kim-Han Thung, Siyuan Liu, Ye Wu, Sahar Ahmad, Pew-Thian Yap

In this paper, we make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning model.

Contrastive Learning Segmentation +1

Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring

no code implementations CVPR 2023 Zhenxuan Fang, Fangfang Wu, Weisheng Dong, Xin Li, Jinjian Wu, Guangming Shi

To address these issues, we propose to represent the field of motion blur kernels in a latent space by normalizing flows, and design CNNs to predict the latent codes instead of motion kernels.

Blind Image Deblurring Image Deblurring

Ecsnet: Spatio-temporal feature learning for event camera

1 code implementation IEEE Transactions on Circuits and Systems for Video Technology 2022 Zhiwen Chen, Jinjian Wu, Junhui Hou, Leida Li, Weisheng Dong, Guangming Shi

To fully exploit their inherent sparsity with reconciling the spatio-temporal information, we introduce a compact event representation, namely 2D-1T event cloud sequence (2D-1T ECS).

Action Recognition Event-based vision +2

Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolution

1 code implementation30 May 2022 Jinhui Hou, Zhiyu Zhu, Junhui Hou, Huanqiang Zeng, Jinjian Wu, Jiantao Zhou

Then, we incorporate the proposed feature embedding scheme into a source-consistent super-resolution framework that is physically-interpretable, producing lightweight PDE-Net, in which high-resolution (HR) HS images are iteratively refined from the residuals between input low-resolution (LR) HS images and pseudo-LR-HS images degenerated from reconstructed HR-HS images via probability-inspired HS embedding.

Hyperspectral Image Super-Resolution Image Super-Resolution

Robust Depth Completion with Uncertainty-Driven Loss Functions

no code implementations15 Dec 2021 Yufan Zhu, Weisheng Dong, Leida Li, Jinjian Wu, Xin Li, Guangming Shi

In this work, we introduce uncertainty-driven loss functions to improve the robustness of depth completion and handle the uncertainty in depth completion.

Depth Completion

Uncertainty-Driven Loss for Single Image Super-Resolution

no code implementations NeurIPS 2021 Qian Ning, Weisheng Dong, Xin Li, Jinjian Wu, Guangming Shi

Specifically, we introduce variance estimation characterizing the uncertainty on a pixel-by-pixel basis into SISR solutions so the targeted pixels in a high-resolution image (mean) and their corresponding uncertainty (variance) can be learned simultaneously.

Image Super-Resolution

Generalizable No-Reference Image Quality Assessment via Deep Meta-learning

1 code implementation IEEE Transactions on Circuits and Systems for Video Technology 2021 Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi

Based on these two task sets, an optimization-based meta-learning is proposed to learn the generalized NR-IQA model, which can be directly used to evaluate the quality of images with unseen distortions.

Meta-Learning No-Reference Image Quality Assessment +1

Searching Efficient Model-guided Deep Network for Image Denoising

no code implementations6 Apr 2021 Qian Ning, Weisheng Dong, Xin Li, Jinjian Wu, Leida Li, Guangming Shi

Similar to the success of NAS in high-level vision tasks, it is possible to find a memory and computationally efficient solution via NAS with highly competent denoising performance.

Image Denoising Neural Architecture Search

Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

1 code implementation CVPR 2021 Tao Huang, Weisheng Dong, Xin Yuan, Jinjian Wu, Guangming Shi

Different from existing GSM models using hand-crafted scale priors (e. g., the Jeffrey's prior), we propose to learn the scale prior through a deep convolutional neural network (DCNN).

Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment

1 code implementation ICCV 2021 Pengfei Chen, Leida Li, Jinjian Wu, Weisheng Dong, Guangming Shi

From this adaptation, we split the data in target domain into confident and uncertain subdomains using the proposed uncertainty-based ranking function, through measuring their prediction confidences.

Unsupervised Domain Adaptation Video Quality Assessment +1

MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment

1 code implementation CVPR 2020 Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, Guangming Shi

The underlying idea is to learn the meta-knowledge shared by human when evaluating the quality of images with various distortions, which can then be adapted to unknown distortions easily.

Meta-Learning No-Reference Image Quality Assessment +1

Feature-Fused SSD: Fast Detection for Small Objects

1 code implementation15 Sep 2017 Guimei Cao, Xuemei Xie, Wenzhe Yang, Quan Liao, Guangming Shi, Jinjian Wu

We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects.

object-detection Small Object Detection

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