1 code implementation • 19 Mar 2024 • Yuehao Song, Xinggang Wang, Jingfeng Yao, Wenyu Liu, Jinglin Zhang, Xiangmin Xu
Our method achieves state-of-the-art (SOTA) performance among all single-modality methods (3. 4% improvement on AUC, 5. 1% improvement on AP) and very comparable performance against multi-modality methods with 59% number of parameters less.
no code implementations • 9 Dec 2023 • Dezhi Yang, Xintong He, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Jinglin Zhang
We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without exposing local data.
no code implementations • 15 Mar 2023 • Shuyao Shang, Zhengyang Shan, Guangxing Liu, LunQian Wang, XingHua Wang, Zekai Zhang, Jinglin Zhang
Adapting the Diffusion Probabilistic Model (DPM) for direct image super-resolution is wasteful, given that a simple Convolutional Neural Network (CNN) can recover the main low-frequency content.
no code implementations • 28 Nov 2022 • Zijun Gao, Jun Wang, Guoxian Yu, Zhongmin Yan, Carlotta Domeniconi, Jinglin Zhang
LtCMH firstly adopts auto-encoders to mine the individuality and commonality of different modalities by minimizing the dependency between the individuality of respective modalities and by enhancing the commonality of these modalities.
no code implementations • IEEE Geoscience and Remote Sensing Letters 2022 • Cong Bai, Feng Sun, Jinglin Zhang, Yi Song, ShengYong Chen
The experimental results show that Rainformer outperforms seven state of the arts methods on the benchmark database and provides more insights into the high-intensity rainfall prediction task.
Ranked #5 on Weather Forecasting on SEVIR
no code implementations • 28 May 2020 • Jinglin Zhang, Wenjun Xu, Hui Gao, Miao Pan, Zhu Han, Ping Zhang
Aiming to address the beam tracking difficulties, we propose to integrate the conformal array (CA) with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks.
no code implementations • 5 Sep 2019 • Wei Zhou, Likun Shi, Zhibo Chen, Jinglin Zhang
Light field image (LFI) quality assessment is becoming more and more important, which helps to better guide the acquisition, processing and application of immersive media.
no code implementations • 17 Aug 2019 • Likun Shi, Wei Zhou, Zhibo Chen, Jinglin Zhang
In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency.
1 code implementation • 16 Jan 2019 • Ming Gao, Xiangnan He, Leihui Chen, Tingting Liu, Jinglin Zhang, Aoying Zhou
Recent years have witnessed a widespread increase of interest in network representation learning (NRL).