1 code implementation • EMNLP 2021 • Yunbin Tu, Liang Li, Chenggang Yan, Shengxiang Gao, Zhengtao Yu
In this paper, we propose a Relation-embedded Representation Reconstruction Network (Rˆ3Net) to explicitly distinguish the real change from the large amount of clutter and irrelevant changes.
no code implementations • 17 May 2023 • Hang Xu, Xinyuan Liu, Haonan Xu, Yike Ma, Zunjie Zhu, Chenggang Yan, Feng Dai
Oriented object detection has been developed rapidly in the past few years, where rotation equivariant is crucial for detectors to predict rotated bounding boxes.
no code implementations • CVPR 2023 • Hang Xu, Xinyuan Liu, Qiang Zhao, Yike Ma, Chenggang Yan, Feng Dai
Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance.
1 code implementation • 19 Nov 2022 • Shancheng Fang, Zhendong Mao, Hongtao Xie, Yuxin Wang, Chenggang Yan, Yongdong Zhang
In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input.
no code implementations • 10 Nov 2022 • Tingyu Wang, Zhedong Zheng, Zunjie Zhu, Yuhan Gao, Yi Yang, Chenggang Yan
Cross-view geo-localization aims to spot images of the same location shot from two platforms, e. g., the drone platform and the satellite platform.
no code implementations • 1 Sep 2022 • Jinkai Zheng, Xinchen Liu, Xiaoyan Gu, Yaoqi Sun, Chuang Gan, Jiyong Zhang, Wu Liu, Chenggang Yan
Current methods that obtain state-of-the-art performance on in-the-lab benchmarks achieve much worse accuracy on the recently proposed in-the-wild datasets because these methods can hardly model the varied temporal dynamics of gait sequences in unconstrained scenes.
no code implementations • 31 May 2022 • Hui Song, A. K. Qin, Chenggang Yan
The performance of MTO-CT is evaluated on solving each of these two sets of tasks in comparison to solving each task in the set independently without knowledge sharing under the same settings, which demonstrates the superiority of MTO-CT in terms of prediction accuracy.
no code implementations • 6 May 2022 • Yang Liu, Ersi Zhang, Lulu Xu, Chufan Xiao, Xiaoyun Zhong, Lijin Lian, Fang Li, Bin Jiang, Yuhan Dong, Lan Ma, Qiming Huang, Ming Xu, Yongbing Zhang, Dongmei Yu, Chenggang Yan, Peiwu Qin
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions.
1 code implementation • 18 Apr 2022 • Tingyu Wang, Zhedong Zheng, Yaoqi Sun, Tat-Seng Chua, Yi Yang, Chenggang Yan
This task is mostly regarded as an image retrieval problem.
1 code implementation • CVPR 2022 • Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei
Based on Gait3D, we comprehensively compare our method with existing gait recognition approaches, which reflects the superior performance of our framework and the potential of 3D representations for gait recognition in the wild.
Ranked #1 on Gait Recognition on Gait3D
1 code implementation • 20 Oct 2021 • Yunbin Tu, Liang Li, Chenggang Yan, Shengxiang Gao, Zhengtao Yu
In this paper, we propose a Relation-embedded Representation Reconstruction Network (R$^3$Net) to explicitly distinguish the real change from the large amount of clutter and irrelevant changes.
no code implementations • 18 Aug 2021 • Qiang Zhao, Bin Chen, Hang Xu, Yike Ma, XiaoDong Li, Bailan Feng, Chenggang Yan, Feng Dai
In this paper, we first identify that spherical rectangles are unbiased bounding boxes for objects in spherical images, and then propose an analytical method for IoU calculation without any approximations.
1 code implementation • 9 Feb 2021 • Jinkai Zheng, Xinchen Liu, Chenggang Yan, Jiyong Zhang, Wu Liu, XiaoPing Zhang, Tao Mei
Despite significant improvement in gait recognition with deep learning, existing studies still neglect a more practical but challenging scenario -- unsupervised cross-domain gait recognition which aims to learn a model on a labeled dataset then adapts it to an unlabeled dataset.
2 code implementations • 14 Jan 2021 • Xin He, Shihao Wang, Xiaowen Chu, Shaohuai Shi, Jiangping Tang, Xin Liu, Chenggang Yan, Jiyong Zhang, Guiguang Ding
The experimental results show that our automatically searched models (CovidNet3D) outperform the baseline human-designed models on the three datasets with tens of times smaller model size and higher accuracy.
1 code implementation • 26 Aug 2020 • Tingyu Wang, Zhedong Zheng, Chenggang Yan, Jiyong Zhang, Yaoqi Sun, Bolun Zheng, Yi Yang
Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas.
Ranked #1 on Image-Based Localization on cvact
no code implementations • 9 Aug 2020 • Chenggang Yan, Zhisheng Li, Yongbing Zhang, Yutao Liu, Xiangyang Ji, Yongdong Zhang
The depth images denoising are increasingly becoming the hot research topic nowadays because they reflect the three-dimensional (3D) scene and can be applied in various fields of computer vision.
1 code implementation • CVPR 2020 • Yutian Lin, Lingxi Xie, Yu Wu, Chenggang Yan, Qi Tian
Person re-identification (re-ID) is an important topic in computer vision.
no code implementations • 1 Feb 2020 • Chenggang Yan, Biao Gong, Yuxuan Wei, Yue Gao
Therefore, we try to introduce the multi-view deep neural network into the hash learning field, and design an efficient and innovative retrieval model, which has achieved a significant improvement in retrieval performance.
1 code implementation • 6 Aug 2019 • Qianyu Feng, Yu Wu, Hehe Fan, Chenggang Yan, Yi Yang
By this novel cascaded captioning-revising mechanism, CRN can accurately describe images with unseen objects.
1 code implementation • 12 May 2019 • Xiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han, Chenggang Yan
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i. e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices.
no code implementations • 12 Mar 2019 • Yaoqi Sun, Liang Li, Liang Zheng, Ji Hu, Yatong Jiang, Chenggang Yan
In the age of information explosion, image classification is the key technology of dealing with and organizing a large number of image data.
2 code implementations • 22 Aug 2018 • Yang He, Xuanyi Dong, Guoliang Kang, Yanwei Fu, Chenggang Yan, Yi Yang
With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable.
no code implementations • CVPR 2018 • Qi Cai, Yingwei Pan, Ting Yao, Chenggang Yan, Tao Mei
In this paper, we introduce the new ideas of augmenting Convolutional Neural Networks (CNNs) with Memory and learning to learn the network parameters for the unlabelled images on the fly in one-shot learning.
2 code implementations • 21 Mar 2017 • Yutian Lin, Liang Zheng, Zhedong Zheng, Yu Wu, Zhilan Hu, Chenggang Yan, Yi Yang
Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions.
Ranked #73 on Person Re-Identification on DukeMTMC-reID