no code implementations • 20 Apr 2024 • Yidan Liu, Weiying Xie, Kai Jiang, Jiaqing Zhang, Yunsong Li, Leyuan Fang
The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anomaly component is optimized by handcrafted sparse priors (e. g., $\ell_{2, 1}$-norm).
no code implementations • 19 Feb 2024 • Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen
Theoretical analysis yields sub-linear upper bounds for both loss regret and the cumulative violation of fairness constraints.
no code implementations • 13 Jan 2024 • Kai Jiang, Jiaxing Huang, Weiying Xie, Jie Lei, Yunsong Li, Ling Shao, Shijian Lu
Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various downstream data.
no code implementations • 13 Jan 2024 • Kai Jiang, Jiaxing Huang, Weiying Xie, Yunsong Li, Ling Shao, Shijian Lu
Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space.
no code implementations • 9 Jan 2024 • Jiaxing Huang, Kai Jiang, Jingyi Zhang, Han Qiu, Lewei Lu, Shijian Lu, Eric Xing
SAMs work with two types of prompts including spatial prompts (e. g., points) and semantic prompts (e. g., texts), which work together to prompt SAMs to segment anything on downstream datasets.
1 code implementation • 6 Jan 2024 • Jiaqing Zhang, Jie Lei, Weiying Xie, Kai Jiang, Mingxiang Cao, Yunsong Li
Accurate cloud recognition and warning are crucial for various applications, including in-flight support, weather forecasting, and climate research.
no code implementations • 27 Dec 2023 • Jiaxing Huang, Jingyi Zhang, Kai Jiang, Han Qiu, Shijian Lu
Traditional computer vision generally solves each single task independently by a dedicated model with the task instruction implicitly designed in the model architecture, arising two limitations: (1) it leads to task-specific models, which require multiple models for different tasks and restrict the potential synergies from diverse tasks; (2) it leads to a pre-defined and fixed model interface that has limited interactivity and adaptability in following user' task instructions.
no code implementations • 23 Nov 2023 • Chen Zhao, Kai Jiang, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Grant, Feng Chen
Achieving the generalization of an invariant classifier from source domains to shifted target domains while simultaneously considering model fairness is a substantial and complex challenge in machine learning.
no code implementations • 18 Sep 2023 • Haoliang Wang, Chen Zhao, Yunhui Guo, Kai Jiang, Feng Chen
In this study, we introduce a novel problem, semantic OOD detection across domains, which simultaneously addresses both distributional shifts.
no code implementations • 31 May 2023 • Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Christan Grant, Feng Chen
To this end, in this paper, we propose a novel algorithm under the assumption that data collected at each time can be disentangled with two representations, an environment-invariant semantic factor and an environment-specific variation factor.
1 code implementation • CVPR 2023 • Weiying Xie, Kai Jiang, Yunsong Li, Jie Lei, Leyuan Fang, Wen-jin Guo
Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework.
no code implementations • 12 Oct 2022 • Dan Wei, Tiejun Zhou, Yunqing Huang, Kai Jiang
The neural network model consists of two parts, a classifier and Structure-Parameter-Mapping (SPM) subnets.
no code implementations • 20 May 2022 • Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen
Furthermore, to determine a good model parameter at each round, we propose a novel adaptive fairness-aware online meta-learning algorithm, namely FairSAOML, which is able to adapt to changing environments in both bias control and model precision.
no code implementations • 21 Oct 2020 • Kai Jiang, Xiangyue Liu, Zheng Ju, Xiang Luo
Compared with MS-COCO, the dataset for the competition has a larger proportion of large objects which area is greater than 96x96 pixels.
no code implementations • 1 Jan 2020 • Kai Jiang, XiaoLong Qin
But the rewards in the actual environment are sparse, and even some environments will not rewards.