no code implementations • 26 Feb 2024 • Yu Ming, Zihao Wu, Jie Yang, Danyi Li, Yuan Gao, Changxin Gao, Gui-Song Xia, Yuanqing Li, Li Liang, Jin-Gang Yu
In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL).
1 code implementation • International Joint Conference on Artificial Intelligence 2023 • Zecheng Li, Zening Zeng, Yuqi Liang, Jin-Gang Yu
To address this problem, we propose a novel approach for WSIS that focuses on the online refinement of complete instances through the use of MaskIoU heads to predict the integrity scores of proposals and a Complete Instances Mining (CIM) strategy to explicitly model the redundant segmentation problem and generate refined pseudo labels.
Ranked #1 on Image-level Supervised Instance Segmentation on COCO 2017 val (using extra training data)
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.
no code implementations • 13 Mar 2023 • Shuangping Huang, Yu Luo, Zhenzhou Zhuang, Jin-Gang Yu, Mengchao He, Yongpan Wang
Despite the success of deep neural network (DNN) on sequential data (i. e., scene text and speech) recognition, it suffers from the over-confidence problem mainly due to overfitting in training with the cross-entropy loss, which may make the decision-making less reliable.
1 code implementation • CVPR 2021 • Quankai Gao, Fudong Wang, Nan Xue, Jin-Gang Yu, Gui-Song Xia
Recently, deep learning based methods have demonstrated promising results on the graph matching problem, by relying on the descriptive capability of deep features extracted on graph nodes.
Ranked #7 on Graph Matching on Willow Object Class
no code implementations • CVPR 2020 • Zhibo Fan, Jin-Gang Yu, Zhihao Liang, Jiarong Ou, Changxin Gao, Gui-Song Xia, Yuanqing Li
Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for training.
1 code implementation • CVPR 2020 • Fu-Dong Wang, Nan Xue, Jin-Gang Yu, Gui-Song Xia
Graph matching (GM), as a longstanding problem in computer vision and pattern recognition, still suffers from numerous cluttered outliers in practical applications.
no code implementations • 24 Feb 2014 • Changxin Gao, Feifei Chen, Jin-Gang Yu, Rui Huang, Nong Sang
However, the task in tracking is to search for a specific object, rather than an object category as in detection.