no code implementations • 14 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.
no code implementations • 23 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.
1 code implementation • 5 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.
1 code implementation • 24 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.
Ranked #1 on Event-based Object Segmentation on DSEC-SEG
1 code implementation • 24 Aug 2023 • Yuqi Fang, Jinjian Wu, Qianqian Wang, Shijun Qiu, Andrea Bozoki, Huaicheng Yan, Mingxia Liu
The model pretrained on large-scale rs-fMRI data has been released to the public.
no code implementations • 20 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.
no code implementations • 3 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.
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.
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).
Ranked #1 on Event data classification on N-CARS
1 code implementation • 30 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.
no code implementations • 15 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.
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.
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.
no code implementations • 6 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.
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).
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.
1 code implementation • IEEE Transactions on Cybernetics 2020 • Hancheng Zhu, Leida Li, Jinjian Wu, Sicheng Zhao, Guiguang Ding, and Guangming Shi
Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an ``average'' user.
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.
1 code implementation • 15 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.