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HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images

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arXiv 2021年

作者： Yuan, Zhimin Ma, Xiaofen Yi, Jiajin Luo, Zhengrong Peng, Jialin College of Computer Science and Technology Huaqiao University Xiamen361021 China Department of Medical Imaging Guangdong Second Provincial General Hospital Guangzhou510317 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China

Background and objective: With the advancement of electron microscopy (EM) imaging technology, neuroscientists can investigate the function of various intracellular organelles, e.g, mitochondria, at nano-scale. Semantic segmentation of electron microscopy (EM) is an essential step to efficiently obtain reliable morphological statistics. Despite the great success achieved using deep convolutional neural networks (CNNs), they still produce coarse segmentations with lots of discontinuities and false positives for mitochondria segmentation. Methods: In this study, we introduce a centerline-aware multitask network by utilizing centerline as an intrinsic shape cue of mitochondria to regularize the segmentation. Since the application of 3D CNNs on large medical volumes is usually hindered by their substantial computational cost and storage overhead, we introduce a novel hierarchical view-ensemble convolution (HVEC), a simple alternative of 3D convolution to learn 3D spatial contexts using more efficient 2D convolutions. The HVEC enables both decomposing and sharing multi-view information, leading to increased learning capacity. Results: Extensive validation results on two challenging benchmarks show that, the proposed method performs favorably against the state-of-the-art methods in accuracy and visual quality but with a greatly reduced model size. Moreover, the proposed model also shows significantly improved generalization ability, especially when training with quite limited amount of training data. Detailed sensitivity analysis and ablation study have also been conducted, which show the robustness of the proposed model and effectiveness of the proposed modules. Conclusions: The experiments highlighted that the proposed architecture enables both simplicity and efficiency leading to increased capacity of learning spatial contexts. Moreover, incorporating shape cues such as centerline information is a promising approach to improve the performance of mitochondria segmentati

关键词： Convolution

Frame attention networks for facial expression recognition in videos

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arXiv 2019年

作者： Meng, Debin Peng, Xiaojiang Wang, Kai Qiao, Yu Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen China University of Chinese Academy of Sciences Beijing China

The video-based facial expression recognition aims to classify a given video into several basic emotions. How to integrate facial features of individual frames is crucial for this task. In this paper, we propose the Frame Attention Networks (FAN)1, to automatically highlight some discriminative frames in an end-to-end framework. The network takes a video with a variable number of face images as its input and produces a fixed-dimension representation. The whole network is composed of two modules. The feature embedding module is a deep Convolutional Neural Network (CNN) which embeds face images into feature vectors. The frame attention module learns multiple attention weights which are used to adaptively aggregate the feature vectors to form a single discriminative video representation. We conduct extensive experiments on CK+ and AFEW8.0 datasets. Our proposed FAN shows superior performance compared to other CNN based methods and achieves state-of-the-art performance on CK+. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Collaborative Weighting for Graph Convolutional Networks

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Journal of Network Intelligence

Journal of Network Intelligence 2023年第2期8卷 432-447页

作者： Chen, Yong Xie, Xiao-Zhu Weng, Wei Zhang, Shan-Dan Li, Tong College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

Graph neural network (GNN), as a powerful method for graph representation, has attracted extensive research interest. Recently, Graph Convolutional Network (GCN) and Graph Attention Network (GAT) have shown superior performance on graph node classification and are considered as the most promising frameworks. Both GAT and GCN use a weighting mechanism in the information aggregation process. However, GCN is weighted by topological structure, while the node content is still unemployed. GAT is weighted by node content, while the topological structure remains being ignored. These weighting mechanism, which does not make full use of topology and node content, is not conducive to graph representation. In this paper, we propose a "Collaborative Weighting For Graph Convolutional Network" (CWGCN) that combines the advantages of GCN and GAT. Specifically, its weighting mechanism not only considers the topology of the graph, but also the node content. With this weighting method for representation learning, CWGCN achieves encouraging performance on a number of citation datasets in node classification. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Classification (of information)

Towards Accurate Scene Text recognition With Semantic Reasoning Networks

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Towards Accurate Scene Text Recognition With Semantic Reason...

Conference on computer vision and pattern recognition (CVPR)

作者： Deli Yu Xuan Li Chengquan Zhang Tao Liu Junyu Han Jingtuo Liu Errui Ding School of Artificial Intelligence University of Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Department of Computer Vision Technology(VIS) Baidu Inc.

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Scene text image contains two levels of contents: visual texture and semantic information. Although the previous scene text recognition methods have made great progress over the past few years, the research on mining semantic information to assist text recognition attracts less attention, only RNN-like structures are explored to implicitly model semantic information. However, we observe that RNN based methods have some obvious shortcomings, such as time-dependent decoding manner and one-way serial transmission of semantic context, which greatly limit the help of semantic information and the computation efficiency. To mitigate these limitations, we propose a novel end-to-end trainable framework named semantic reasoning network (SRN) for accurate scene text recognition, where a global semantic reasoning module (GSRM) is introduced to capture global semantic context through multi-way parallel transmission. The state-of-the-art results on 7 public benchmarks, including regular text, irregular text and non-Latin long text, verify the effectiveness and robustness of the proposed method. In addition, the speed of SRN has significant advantages over the RNN based methods, demonstrating its value in practical use.

关键词： Semantics Visualization Text recognition Cognition Feature extraction Decoding Robustness

RankSRGAN: Super resolution generative adversarial networks with learning to rank

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arXiv 2021年

作者： Zhang, Wenlong Liu, Yihao Dong, Chao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shanghai AI Lab Shanghai China

Generative Adversarial Networks (GAN) have demonstrated the potential to recover realistic details for single image super-resolution (SISR). To further improve the visual quality of super-resolved results, PIRM2018-SR Challenge employed perceptual metrics to assess the perceptual quality, such as PI, NIQE, and Ma. However, existing methods cannot directly optimize these indifferentiable perceptual metrics, which are shown to be highly correlated with human ratings. To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of different perceptual metrics. Specifically, we first train a Ranker which can learn the behaviour of perceptual metrics and then introduce a novel rank-content loss to optimize the perceptual quality. The most appealing part is that the proposed method can combine the strengths of different SR methods to generate better results. Furthermore, we extend our method to multiple Rankers to provide multi-dimension constraints for the generator. Extensive experiments show that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics and quality. Project page: https://***/Projects/RankSRGAN. © 2021, CC BY.

关键词： Generative adversarial networks

Exploring emotion features and fusion strategies for audio-video emotion recognition

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arXiv 2020年

作者： Zhou, Hengshun Meng, Debin Zhang, Yuanyuan Peng, Xiaojiang Du, Jun Wang, Kai Qiao, Yu University of Science and Technology of China China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

The audio-video based emotion recognition aims to classify a given video into basic emotions. In this paper, we describe our approaches in EmotiW 2019, which mainly explores emotion features and feature fusion strategies for audio and visual modality. For emotion features, we explore audio feature with both speech-spectrogram and Log Mel-spectrogram and evaluate several facial features with different CNN models and different emotion pretrained strategies. For fusion strategies, we explore intra-modal and cross-modal fusion methods, such as designing attention mechanisms to highlights important emotion feature, exploring feature concatenation and factorized bilinear pooling (FBP) for cross-modal feature fusion. With careful evaluation, we obtain 65.5% on the AFEW validation set and 62.48% on the test set and rank second in the challenge. © 2020, CC BY.

关键词： Speech recognition

RankSRGAN: Generative adversarial networks with ranker for image super-resolution

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arXiv 2019年

作者： Zhang, Wenlong Liu, Yihao Dong, Chao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences

Generative Adversarial Networks (GAN) have demonstrated the potential to recover realistic details for single image super-resolution (SISR). To further improve the visual quality of super-resolved results, PIRM2018-SR Challenge employed perceptual metrics to assess the perceptual quality, such as PI, NIQE, and Ma. However, existing methods cannot directly optimize these indifferentiable perceptual metrics, which are shown to be highly correlated with human ratings. To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of perceptual metrics. Specifically, we first train a Ranker which can learn the behavior of perceptual metrics and then introduce a novel rank-content loss to optimize the perceptual quality. The most appealing part is that the proposed method can combine the strengths of different SR methods to generate better results. Extensive experiments show that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics. Project page: https://***/Projects/RankSRGAN. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

Co-Clustering Image Features and Semantic Concepts

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Co-Clustering Image Features and Semantic Concepts

IEEE International Conference on Image Processing

作者： Manjeet Rege Ming Dong Farshad Fotouhi Department of Computer Science Machine Vision & Pattern Recognition Laboratory Wayne State University Detroit MI USA Database & Multimedia Systems Group Wayne State University Detroit MI USA

In this paper, we present a novel idea of co-clustering image features and semantic concepts. We accomplish this by modelling user feedback logs and low-level features using a bipartite graph. Our experiments demonstrate that (1) incorporating semantic information achieves better image clustering and (2) feature selection in co-clustering narrows the semantic gap, thus enabling efficient image retrieval.

关键词： Feedback Image retrieval Bipartite graph Image databases Clustering algorithms Machine vision pattern recognition Multimedia databases Spatial databases Multimedia systems

Finding a Semantic Structure Interactively in Image Databases

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Finding a Semantic Structure Interactively in Image Database...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Manjeet Rege Ming Dong Farshad Fotouhi Machine Vision & Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA Database & Multimedia Systems Group Department of Computer Science Wayne State University Detroit MI USA

We present a new approach to organize an image database by finding a semantic structure interactively based on multi-user relevance feedback. By treating user relevance feedbacks as weak classifiers and combining them together, we are able to capture the categories in the users' mind and build a semantic structure in the image database. Experiments performed on an image database consisting of general purpose images demonstrate that our system outperforms some of the other conventional methods

关键词： Image databases Feedback Image retrieval Machine learning algorithms Support vector machines Support vector machine classification Information retrieval Machine vision pattern recognition Multimedia databases