no code implementations • 29 Jan 2024 • Huadeng Wang, Jiejiang Yu, Bingbing Li, Xipeng Pan, Zhenbing Liu, Rushi Lan, Xiaonan Luo
Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma.
1 code implementation • 18 Jan 2023 • Huadeng Wang, Hao Xu, Bingbing Li, Xipeng Pan, Lingqi Zeng, Rushi Lan, Xiaonan Luo
In the first stage, a detection network named M_det is proposed to detect as many mitoses as possible.
no code implementations • 27 Dec 2022 • Huadeng Wang, Zhipeng Liu, Rushi Lan, Zhenbing Liu, Xiaonan Luo, Xipeng Pan, Bingbing Li
In addition, the model also achieves good performance on GZMH dataset, which is prepared by our group and will be firstly released with the publication of this paper.
1 code implementation • 31 Aug 2022 • Xiaoqin Wang, Chen Chen, Rushi Lan, Licheng Liu, Zhenbing Liu, Huiyu Zhou, Xiaonan Luo
Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space.
1 code implementation • CVPR 2022 • Wanquan Feng, Jin Li, Hongrui Cai, Xiaonan Luo, Juyong Zhang
Different from traditional point cloud representation where each point only represents a position or a local plane in the 3D space, each point in Neural Points represents a local continuous geometric shape via neural fields.
no code implementations • 25 May 2021 • Bin Sun, Dehui Kong, Shaofan Wang, Jinghua Li, BaoCai Yin, Xiaonan Luo
In the sampling stage, we utilize a generative adversarial networks (GAN) trained by action features and word vectors of seen classes to synthesize the action features of unseen classes, which can balance the training sample data of seen classes and unseen classes.
no code implementations • 28 Dec 2018 • Fei Wang, Shujin Lin, Hanhui Li, Hefeng Wu, Junkun Jiang, Ruomei Wang, Xiaonan Luo
Traditional sketch segmentation methods mainly rely on handcrafted features and complicate models, and their performance is far from satisfactory due to the abstract representation of sketches.
no code implementations • 25 Aug 2018 • Tianshui Chen, Riquan Chen, Lin Nie, Xiaonan Luo, Xiaobai Liu, Liang Lin
This paper focuses on semantic task planning, i. e., predicting a sequence of actions toward accomplishing a specific task under a certain scene, which is a new problem in computer vision research.
1 code implementation • 14 Aug 2018 • Tianshui Chen, Wenxi Wu, Yuefang Gao, Le Dong, Xiaonan Luo, Liang Lin
In this work, we investigate simultaneously predicting categories of different levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework.
Ranked #51 on Fine-Grained Image Classification on CUB-200-2011
Fine-Grained Image Classification Fine-Grained Image Recognition +1
no code implementations • 2 Jul 2018 • Tianshui Chen, Liang Lin, Riquan Chen, Yang Wu, Xiaonan Luo
Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions.
Fine-Grained Image Classification Fine-Grained Image Recognition +1
no code implementations • 20 Jan 2018 • Hanhui Li, Xiangjian He, Hefeng Wu, Saeed Amirgholipour Kasmani, Ruomei Wang, Xiaonan Luo, Liang Lin
In this paper, we aim at tackling the problem of crowd counting in extremely high-density scenes, which contain hundreds, or even thousands of people.
no code implementations • 29 Dec 2017 • Daiguo Deng, Ruomei Wang, Hefeng Wu, Huayong He, Qi Li, Xiaonan Luo
Fabric image retrieval is beneficial to many applications including clothing searching, online shopping and cloth modeling.
2 code implementations • 20 Dec 2017 • Tianshui Chen, Liang Lin, WangMeng Zuo, Xiaonan Luo, Lei Zhang
In this work, aiming at a general and comprehensive way for neural network acceleration, we develop a Wavelet-like Auto-Encoder (WAE) that decomposes the original input image into two low-resolution channels (sub-images) and incorporate the WAE into the classification neural networks for joint training.
no code implementations • 4 Dec 2016 • Tianshui Chen, Liang Lin, Xian Wu, Nong Xiao, Xiaonan Luo
To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate object proposals from images.
no code implementations • 13 Nov 2015 • Tianshui Chen, Liang Lin, Lingbo Liu, Xiaonan Luo, Xuelong. Li
Our DISC framework is capable of uniformly highlighting the objects-of-interest from complex background while preserving well object details.