no code implementations • WASSA (ACL) 2022 • Hao Lin, Pradeep Nalluri, Lantian Li, Yifan Sun, Yongjun Zhang
We introduce new datasets from Twitter related to anti-Asian hate sentiment before and during the pandemic.
no code implementations • 14 Apr 2024 • Jieyi Tan, Yansheng Li, Sergey A. Bartalev, Bo Dang, Wei Chen, Yongjun Zhang, Liangqi Yuan
Remote sensing semantic segmentation (RSS) is an essential task in Earth Observation missions.
no code implementations • 11 Apr 2024 • Yansheng Li, Kun Li, Yongjun Zhang, LinLin Wang, Dingwen Zhang
To fill in the gap of the overhead view dataset, this paper constructs and releases an aerial image urban scene graph generation (AUG) dataset.
2 code implementations • 10 Apr 2024 • Ziyang Chen, Wei Long, He Yao, Yongjun Zhang, Bingshu Wang, Yongbin Qin, Jia Wu
In addition, edge variations in %potential feature channels of the reconstruction error map also affect details matching, we propose the Reconstruction Error Motif Penalty (REMP) module to further refine the full-resolution disparity estimation.
Ranked #1 on
Stereo Disparity Estimation
on KITTI 2015
no code implementations • 8 Feb 2024 • Qianchen Mao, Qiang Li, Bingshu Wang, Yongjun Zhang, Tao Dai, C. L. Philip Chen
To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets.
no code implementations • 31 Dec 2023 • Run Shao, Cheng Yang, Qiujun Li, Qing Zhu, Yongjun Zhang, Yansheng Li, Yu Liu, Yong Tang, Dapeng Liu, Shizhong Yang, Haifeng Li
We introduce the Language as Reference Framework (LaRF), a fundamental principle for constructing a multimodal unified model, aiming to strike a trade-off between the cohesion and autonomy among different modalities.
no code implementations • 15 Dec 2023 • Xin Guo, Jiangwei Lao, Bo Dang, Yingying Zhang, Lei Yu, Lixiang Ru, Liheng Zhong, Ziyuan Huang, Kang Wu, Dingxiang Hu, Huimei He, Jian Wang, Jingdong Chen, Ming Yang, Yongjun Zhang, Yansheng Li
Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation.
no code implementations • 5 Dec 2023 • Yansheng Li, Junwei Luo, Yongjun Zhang, Yihua Tan, Jin-Gang Yu, Song Bai
Therefore, to ensure the visibility and integrity of bridges, it is essential to perform holistic bridge detection in large-size very-high-resolution (VHR) RSIs.
1 code implementation • 30 Nov 2023 • Yongjun Zhang
First, the longformer model was fine-tuned using the Dynamic of Collective Action (DoCA) Corpus.
no code implementations • 28 Mar 2023 • Yameng Wang, Yi Wan, Yongjun Zhang, Bin Zhang, Zhi Gao
The present multi-modal methods usually map high-dimensional features to low-dimensional spaces as a preprocess before feature extraction to address the nonnegligible domain gap, which inevitably leads to information loss.
no code implementations • 16 Mar 2023 • Yansheng Li, Bo Dang, Wanchun Li, Yongjun Zhang
Global surface water detection in very-high-resolution (VHR) satellite imagery can directly serve major applications such as refined flood mapping and water resource assessment.
1 code implementation • 1 Mar 2023 • Jiayuan Li, Pengcheng Shi, Qingwu Hu, Yongjun Zhang
Multimodal image matching is an important prerequisite for multisource image information fusion.
no code implementations • CVPR 2023 • Haoliang Zhao, Huizhou Zhou, Yongjun Zhang, Jie Chen, Yitong Yang, Yong Zhao
In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo.
no code implementations • 22 Nov 2022 • Bo Dang, Yansheng Li, Yongjun Zhang, Jiayi Ma
Semi-supervised semantic segmentation focuses on the exploration of a small amount of labeled data and a large amount of unlabeled data, which is more in line with the demands of real-world image understanding applications.
no code implementations • 21 Nov 2022 • Wei Chen, Yansheng Li, Bo Dang, Yongjun Zhang
This paper presents EHSNet, a new end-to-end segmentation network designed for the holistic learning of large-size remote sensing image semantic segmentation (LRISS).
no code implementations • 14 Mar 2022 • Youming Deng, Yansheng Li, Yongjun Zhang, Xiang Xiang, Jian Wang, Jingdong Chen, Jiayi Ma
After the autonomous partition of coarse and fine predicates, the model is first trained on the coarse predicates and then learns the fine predicates.
no code implementations • 21 Feb 2022 • Yongjun Zhang, Siyuan Zou, Xinyi Liu, Xu Huang, Yi Wan, Yongxiang Yao
Next, we propose a riverbed enhancement function to optimize the cost volume of the LiDAR projection points and their homogeneous pixels to improve the matching robustness.
1 code implementation • 15 Jan 2022 • Weiwei Song, Zhi Gao, Renwei Dian, Pedram Ghamisi, Yongjun Zhang, Jón Atli Benediktsson
In this paper, we propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for RSIR.
no code implementations • CVPR 2022 • Dong Wei, Yi Wan, Yongjun Zhang, Xinyi Liu, Bin Zhang, Xiqi Wang
In this paper, we propose an efficient line segment reconstruction method called ELSR.
1 code implementation • 1 Nov 2021 • Ling Chen, Jun Cui, Xing Tang, Chaodu Song, Yuntao Qian, Yansheng Li, Yongjun Zhang
Therefore, neighbor aggregation-based representation learning (NARL) models are proposed, which encode the information in the neighbors of an entity into its embeddings.
1 code implementation • 27 Aug 2021 • Ling Chen, Jiahui Xu, Binqing Wu, Yuntao Qian, Zhenhong Du, Yansheng Li, Yongjun Zhang
The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively.
no code implementations • 6 Oct 2020 • Yansheng Li, Song Ouyang, Yongjun Zhang
As one kind of architecture from the deep learning family, deep semantic segmentation network (DSSN) achieves a certain degree of success on the semantic segmentation task and obviously outperforms the traditional methods based on hand-crafted features.
Segmentation Of Remote Sensing Imagery
Semantic Segmentation
no code implementations • 26 Jun 2019 • Ruoxiang Li, Dianxi Shi, Yongjun Zhang, Kaiyue Li, Ruihao Li
The proposed G-SAE maintenance algorithm and corner candidate selection algorithm greatly enhance the real-time performance for corner detection, while the corner candidate refinement algorithm maintains the accuracy of performance by using an improved event-based Harris detector.