Search Results for author: Chenggang Yan

Found 40 papers, 23 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.

Caption Generation Relation +1

Context-aware Difference Distilling for Multi-change Captioning

no code implementations31 May 2024 Yunbin Tu, Liang Li, Li Su, Zheng-Jun Zha, Chenggang Yan, Qingming Huang

Given an image pair, CARD first decouples context features that aggregate all similar/dissimilar semantics, termed common/difference context features.


Progressive Depth Decoupling and Modulating for Flexible Depth Completion

no code implementations15 May 2024 Zhiwen Yang, Jiehua Zhang, Liang Li, Chenggang Yan, Yaoqi Sun, Haibing Yin

However, previous depth discretization methods are easy to be impacted by depth distribution variations across different scenes, resulting in suboptimal scene depth distribution priors.

Depth Completion

Quality-aware Selective Fusion Network for V-D-T Salient Object Detection

1 code implementation13 May 2024 Liuxin Bao, Xiaofei Zhou, Xiankai Lu, Yaoqi Sun, Haibing Yin, Zhenghui Hu, Jiyong Zhang, Chenggang Yan

Therefore, we propose a quality-aware selective fusion network (QSF-Net) to conduct VDT salient object detection, which contains three subnets including the initial feature extraction subnet, the quality-aware region selection subnet, and the region-guided selective fusion subnet.

object-detection Object Detection +2

Benchmarking the Cell Image Segmentation Models Robustness under the Microscope Optical Aberrations

no code implementations12 Apr 2024 Boyuan Peng, Jiaju Chen, Qihui Ye, Minjiang Chen, Peiwu Qin, Chenggang Yan, Dongmei Yu, Zhenglin Chen

Overall, this research aims to guide researchers in effectively utilizing cell segmentation models in the presence of minor optical aberrations.

Benchmarking Cell Segmentation +4

SDPL: Shifting-Dense Partition Learning for UAV-View Geo-Localization

1 code implementation7 Mar 2024 Quan Chen, Tingyu Wang, Zihao Yang, Haoran Li, Rongfeng Lu, Yaoqi Sun, Bolun Zheng, Chenggang Yan

We propose a dense partition strategy (DPS), dividing the image into multiple parts to explore contextual information while explicitly maintaining the global structure.

Part-based Representation Learning

Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection

no code implementations4 Mar 2024 P. Bilha Githinji, Xi Yuan, Zhenglin Chen, Ijaz Gul, Dingqi Shang, Wen Liang, Jianming Deng, Dan Zeng, Dongmei Yu, Chenggang Yan, Peiwu Qin

Realizing sufficient separability between the distributions of healthy and pathological samples is a critical obstacle for pathology detection convolutional models.

StyleDubber: Towards Multi-Scale Style Learning for Movie Dubbing

no code implementations20 Feb 2024 Gaoxiang Cong, Yuankai Qi, Liang Li, Amin Beheshti, Zhedong Zhang, Anton Van Den Hengel, Ming-Hsuan Yang, Chenggang Yan, Qingming Huang

Given a script, the challenge in Movie Dubbing (Visual Voice Cloning, V2C) is to generate speech that aligns well with the video in both time and emotion, based on the tone of a reference audio track.

Voice Cloning

Coupled Confusion Correction: Learning from Crowds with Sparse Annotations

2 code implementations12 Dec 2023 Hansong Zhang, Shikun Li, Dan Zeng, Chenggang Yan, Shiming Ge

Moreover, we cluster the ``annotator groups'' who share similar expertise so that their confusion matrices could be corrected together.

PC-bzip2: a phase-space continuity enhanced lossless compression algorithm for light field microscopy data

no code implementations14 Oct 2023 Changqing Su, Zihan Lin, You Zhou, Shuai Wang, Yuhan Gao, Chenggang Yan, Bo Xiong

Moreover, by introducing the temporal continuity, our method shows the superior compression ratio on time series data of zebrafish blood vessels.

RawHDR: High Dynamic Range Image Reconstruction from a Single Raw Image

1 code implementation ICCV 2023 Yunhao Zou, Chenggang Yan, Ying Fu

Unlike existing methods, the core idea of this work is to incorporate more informative Raw sensor data to generate HDR images, aiming to recover scene information in hard regions (the darkest and brightest areas of an HDR scene).

HDR Reconstruction Image Reconstruction

Parsing is All You Need for Accurate Gait Recognition in the Wild

1 code implementation31 Aug 2023 Jinkai Zheng, Xinchen Liu, Shuai Wang, Lihao Wang, Chenggang Yan, Wu Liu

Furthermore, due to the lack of suitable datasets, we build the first parsing-based dataset for gait recognition in the wild, named Gait3D-Parsing, by extending the large-scale and challenging Gait3D dataset.

Gait Recognition in the Wild Human Parsing

Rethinking Boundary Discontinuity Problem for Oriented Object Detection

1 code implementation CVPR 2024 Hang Xu, Xinyuan Liu, Haonan Xu, Yike Ma, Zunjie Zhu, Chenggang Yan, Feng Dai

We decouple reversibility and joint-optim from single smoothing function into two distinct entities, which for the first time achieves the objectives of both correcting angular boundary and blending angle with other parameters. Extensive experiments on multiple datasets show that boundary discontinuity problem is well-addressed.

Object object-detection +2

Hybrid Spectral Denoising Transformer with Guided Attention

1 code implementation ICCV 2023 Zeqiang Lai, Chenggang Yan, Ying Fu

Challenges in adapting transformer for HSI arise from the capabilities to tackle existing limitations of CNN-based methods in capturing the global and local spatial-spectral correlations while maintaining efficiency and flexibility.

Hyperspectral Image Denoising Image Denoising

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 object-detection +2

Iterative Denoiser and Noise Estimator for Self-Supervised Image Denoising

no code implementations ICCV 2023 Yunhao Zou, Chenggang Yan, Ying Fu

However, the unavailable noise prior and inefficient feature extraction take these methods away from high practicality and precision.

Image Denoising

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.

Blocking Language Modelling +2

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

1 code implementation1 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 +3

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.

Caption Generation Relation +1

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 object-detection +1

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 +3

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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