no code implementations • 16 May 2024 • Risheng Liu, Zhu Liu, Wei Yao, Shangzhi Zeng, Jin Zhang
This work focuses on addressing two major challenges in the context of large-scale nonconvex Bi-Level Optimization (BLO) problems, which are increasingly applied in machine learning due to their ability to model nested structures.
no code implementations • 3 Mar 2024 • Zhu Liu, Cunliang Kong, Ying Liu, Maosong Sun
Large language models have achieved remarkable success in general language understanding tasks.
1 code implementation • 19 Oct 2023 • Yaohua Liu, Jiaxin Gao, Xianghao Jiao, Zhu Liu, Xin Fan, Risheng Liu
Adversarial Training (AT), pivotal in fortifying the robustness of deep learning models, is extensively adopted in practical applications.
no code implementations • 8 Aug 2023 • Zhu Deng, Jinjie Liu, Biao Luo, Can Yuan, Qingrun Yang, Lei Xiao, Wenwen Zhou, Zhu Liu
The product carbon footprint (PCF) is crucial for decarbonizing the supply chain, as it measures the direct and indirect greenhouse gas emissions caused by all activities during the product's life cycle.
3 code implementations • 8 Aug 2023 • Zhu Liu, JinYuan Liu, Benzhuang Zhang, Long Ma, Xin Fan, Risheng Liu
We first conduct systematic analyses about the components of image fusion, investigating the correlation with segmentation robustness under adversarial perturbations.
Ranked #16 on Thermal Image Segmentation on MFN Dataset
1 code implementation • 7 Aug 2023 • Yingchi Liu, Zhu Liu, Long Ma, JinYuan Liu, Xin Fan, Zhongxuan Luo, Risheng Liu
In this study, we propose a generic low-light vision solution by introducing a generative block to convert data from the RAW to the RGB domain.
2 code implementations • ICCV 2023 • JinYuan Liu, Zhu Liu, Guanyao Wu, Long Ma, Risheng Liu, Wei Zhong, Zhongxuan Luo, Xin Fan
Multi-modality image fusion and segmentation play a vital role in autonomous driving and robotic operation.
Ranked #3 on Semantic Segmentation on FMB Dataset
1 code implementation • 25 May 2023 • Risheng Liu, Zhu Liu, JinYuan Liu, Xin Fan, Zhongxuan Luo
Qualitative and quantitative experimental results on different categories of image fusion problems and related downstream tasks (e. g., visual enhancement and semantic understanding) substantiate the flexibility and effectiveness of our TIM.
1 code implementation • 22 May 2023 • Zhu Liu, Ying Liu
Word sense disambiguation (WSD), which aims to determine an appropriate sense for a target word given its context, is crucial for natural language understanding.
1 code implementation • 20 May 2023 • Zhu Liu, JinYuan Liu, Guanyao Wu, Xin Fan, Risheng Liu
In recent years, deep learning-based methods have achieved remarkable progress in multi-exposure image fusion.
2 code implementations • 11 May 2023 • Zhu Liu, JinYuan Liu, Guanyao Wu, Long Ma, Xin Fan, Risheng Liu
Recently, multi-modality scene perception tasks, e. g., image fusion and scene understanding, have attracted widespread attention for intelligent vision systems.
no code implementations • 23 Dec 2022 • Weichao Shen, Yuan Dong, Zonghao Chen, Zhengyi Zhao, Yang Gao, Zhu Liu
In this paper, we propose PanoViT, a panorama vision transformer to estimate the room layout from a single panoramic image.
1 code implementation • 22 Nov 2022 • Yuhui Wu, Zhu Liu, JinYuan Liu, Xin Fan, Risheng Liu
To address these challenges, in this letter, we develop a semantic-level fusion network to sufficiently utilize the semantic guidance, emancipating the experimental designed fusion rules.
no code implementations • 13 Apr 2022 • Teng Wang, Zhu Liu, Feng Zheng, Zhichao Lu, Ran Cheng, Ping Luo
This report describes the details of our approach for the event dense-captioning task in ActivityNet Challenge 2021.
no code implementations • CVPR 2022 • Isabella Liu, Edward Yang, Jianyu Tao, Rui Chen, Xiaoshuai Zhang, Qing Ran, Zhu Liu, Hao Su
First, we demonstrate the transferability of our method to out-of-distribution real data by using a mixed domain learning strategy.
no code implementations • 8 Nov 2021 • Pan Mu, Zhu Liu, Yaohua Liu, Risheng Liu, Xin Fan
In this paper, we develop a model-guided triple-level optimization framework to deduce network architecture with cooperating optimization and auto-searching mechanism, named Triple-level Model Inferred Cooperating Searching (TMICS), for dealing with various video rain circumstances.
no code implementations • 3 Mar 2021 • Zhu Liu, Zhu Deng, Philippe Ciais, Jianguang Tan, Biqing Zhu, Steven J. Davis, Robbie Andrew, Olivier Boucher, Simon Ben Arous, Pep Canadel, Xinyu Dou, Pierre Friedlingstein, Pierre Gentine, Rui Guo, Chaopeng Hong, Robert B. Jackson, Daniel M. Kammen, Piyu Ke, Corinne Le Quere, Crippa Monica, Greet Janssens-Maenhout, Glen Peters, Katsumasa Tanaka, Yilong Wang, Bo Zheng, Haiwang Zhong, Taochun Sun, Hans Joachim Schellnhuber
That even substantial world-wide lockdowns of activity led to a one-time decline in global CO$_2$ emissions of only 5. 4% in one year highlights the significant challenges for climate change mitigation that we face in the post-COVID era.
Atmospheric and Oceanic Physics General Economics Economics
1 code implementation • 10 Dec 2020 • Risheng Liu, Zhu Liu, Pan Mu, Xin Fan, Zhongxuan Luo
Specifically, by introducing a general energy minimization model and formulating its descent direction from different viewpoints (i. e., in a generative manner, based on the discriminative metric and with optimality-based correction), we construct three propagative modules to effectively solve the optimization models with flexible combinations.
no code implementations • 18 Nov 2020 • Xinyu Dou, Cuijuan Liao, Hengqi Wang, Ying Huang, Ying Tu, Xiaomeng Huang, Yiran Peng, Biqing Zhu, Jianguang Tan, Zhu Deng, Nana Wu, Taochun Sun, Piyu Ke, Zhu Liu
We advanced a Random Forest model integrated K-means (RF-K) for the estimates with multi-source parameters.
no code implementations • 5 Aug 2017 • Shervin Minaee, Zhu Liu
We first train this model on a large-scale public question-answering database, and then fine-tune it to transfer to the customer-care chat data.
no code implementations • 12 Aug 2016 • Yadong Mu, Zhu Liu
In this paper, we propose a novel algorithm that concurrently performs feature engineering and non-linear supervised hashing function learning.