Search Results for author: Chenggang Yan

Found 25 papers, 14 papers with code

Rˆ3Net:Relation-embedded Representation Reconstruction Network for Change Captioning

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.

Rethinking Boundary Discontinuity Problem for Oriented Object Detection

no code implementations17 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.

object-detection Object Detection +1

Gaussian Label Distribution Learning for Spherical Image Object Detection

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.

object-detection Object Detection +1

ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting

1 code implementation19 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.

Language Modelling Scene Text Recognition +1

Learning Cross-view Geo-localization Embeddings via Dynamic Weighted Decorrelation Regularization

no code implementations10 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.

Gait Recognition in the Wild with Multi-hop Temporal Switch

no code implementations1 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.

Gait Recognition in the Wild

Multi-task Optimization Based Co-training for Electricity Consumption Prediction

no code implementations31 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.

Transfer Learning

Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference

no code implementations6 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.

Computed Tomography (CT) Image Segmentation +2

Gait Recognition in the Wild with Dense 3D Representations and A Benchmark

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.

Gait Recognition in the Wild

R$^3$Net:Relation-embedded Representation Reconstruction Network for Change Captioning

1 code implementation20 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.

Unbiased IoU for Spherical Image Object Detection

no code implementations18 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.

object-detection Object Detection

TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition

1 code implementation9 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.

Gait Recognition

Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans

2 code implementations14 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.

Benchmarking Medical Diagnosis +1

Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization

1 code implementation26 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.

Drone navigation Drone-view target localization +2

Depth image denoising using nuclear norm and learning graph model

no code implementations9 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.

Image Denoising Image Restoration

Deep Multi-View Enhancement Hashing for Image Retrieval

no code implementations1 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.

Image Retrieval Retrieval

Cascaded Revision Network for Novel Object Captioning

1 code implementation6 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.

Image Captioning object-detection +1

Approximated Oracle Filter Pruning for Destructive CNN Width Optimization

1 code implementation12 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.

Image Classification base on PCA of Multi-view Deep Representation

no code implementations12 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.

Classification General Classification +1

Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks

2 code implementations22 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.

Image Classification

Memory Matching Networks for One-Shot Image Recognition

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.

One-Shot Learning Philosophy

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