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Reasoning with Multi-Structure Commonsense Knowledge in Visual Dialog

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arXiv 2022年

作者： Zhang, Shunyu Jiang, Xiaoze Yang, Zequn Wan, Tao Qin, Zengchang Intelligent Computing and Machine Learning Lab School of ASEE Beihang University China School of BSME Beijing Advanced Innovation Center for Biomedical Engineering Beihang University China

Visual Dialog requires an agent to engage in a conversation with humans grounded in an image. Many studies on Visual Dialog focus on the understanding of the dialog history or the content of an image, while a considerable amount of commonsense-required questions are ignored. Handling these scenarios depends on logical reasoning that requires commonsense priors. How to capture relevant commonsense knowledge complementary to the history and the image remains a key challenge. In this paper, we propose a novel model by Reasoning with Multi-structure Commonsense Knowledge (RMK). In our model, the external knowledge is represented with sentence-level facts and graph-level facts, to properly suit the scenario of the composite of dialog history and image. On top of these multi-structure representations, our model can capture relevant knowledge and incorporate them into the vision and semantic features, via graph-based interaction and transformer-based fusion. Experimental results and analysis on VisDial v1.0 and VisDialCK datasets show that our proposed model effectively outperforms comparative methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphic methods

Pixel level data augmentation for semantic image segmentation using generative adversarial networks

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arXiv 2018年

作者： Liu, Shuangting Zhang, Jiaqi Chen, Yuxin Liu, Yifan Qin, Zengchang Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University China Keep Labs Keep Inc School of Biological Science and Medical Engineering Beihang University

Semantic segmentation is one of the basic topics in computer vision, it aims to assign semantic labels to every pixel of an image. Unbalanced semantic label distribution could have a negative influence on segmentation accuracy. In this paper, we investigate using data augmentation approach to balance the semantic label distribution in order to improve segmentation performance. We propose using generative adversarial networks (GANs) to generate realistic images for improving the performance of semantic segmentation networks. Experimental results show that the proposed method can not only improve segmentation performance on those classes with low accuracy, but also obtain 1.3% to 2.1% increase in average segmentation accuracy. It shows that this augmentation method can boost accuracy and be easily applicable to any other segmentation models. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

A HSC-based sample selection method for Support Vector machine

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A HSC-based sample selection method for Support Vector Machi...

International Conference on machine learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

A bag-of-tones model with MFCC features for musical genre classification

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A bag-of-tones model with MFCC features for musical genre cl...

9th International Conference on Advanced Data Mining and Applications, ADMA 2013

作者： Qin, Zengchang Liu, Wei Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing 100191 China School of Advanced Engineering Beihang University Beijing 100191 China School of Biological Science and Medical Engineering Beihang University Beijing 100191 China Department of Biomedical Engineering Case Western Reserve University Cleveland OH 44106 United States

ISBN: (纸本)9783642539138

Musical genres are categorical labels created by humans to characterize pieces of music. These labels may be highly subjective but typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. In this paper, we propose a model for music genre classification. The new model is referred to as the bag-of-tones (BOT) model which follows the conceptually similar idea of the bag-of-words (BOW) model in natural language processing and the bag-of-feature (BOF) model in image processing. The basic low-level music features such as Mel-frequency cepstral coefficients (MFCC) are clustered into a set of codewords referred to as "tones". By using such a model, each piece of music can be represented by a new feature vector of distribution on tones. Classical machine learning models such as support vector machines (SVM) can be applied for genre classification. The model is tested using two datasets. We found that the polynomial kernel function has the best performance in the SVM classification. By comparing to the previous work, we found the new proposed model outperform classical models on a given benchmark dataset. In general, this model can be used to structure the large collections of music available on the Web. It can play an important role in automatic digital music categorization and retrieval. © Springer-Verlag 2013.

关键词： Music

Text generation based on generative adversarial nets with latent variable

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arXiv 2017年

作者： Wang, Heng Qin, Zengchang Wan, Tao Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing100191 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China

In this paper, we propose a model using generative adver- sarial net (GAN) to generate realistic text. Instead of using standard GAN, we combine variational autoencoder (VAE) with generative ad- versarial net. The use of high-level latent random variables is helpful to learn the data distribution and solve the problem that generative adver- sarial net always emits the similar data. We propose the VGAN model where the generative model is composed of recurrent neural network and VAE. The discriminative model is a convolutional neural network. We train the model via policy gradient. We apply the proposed model to the task of text generation and compare it to other recent neural network based models, such as recurrent neural network language model and Seq- GAN. We evaluate the performance of the model by calculating negative log-likelihood and the BLEU score. We conduct experiments on three benchmark datasets, and results show that our model outperforms other previous models. Copyright © 2017, The Authors. All rights reserved.

关键词： Recurrent neural networks

KBGN: Knowledge-bridge graph network for adaptive vision-text reasoning in visual dialogue

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arXiv 2020年

作者： Jiang, Xiaoze Du, Siyi Qin, Zengchang Sun, Yajing Yu, Jing Intelligent Computing and Machine Learning Lab School of ASEE Beihang University Beijing China AI Research Codemao Inc Institute of Information Engineering Chinese Academy of Sciences Beijing China

Visual dialogue is a challenging task that needs to extract implicit information from both visual (image) and textual (dialogue history) contexts. Classical approaches pay more attention to the integration of the current question, vision knowledge and text knowledge, despising the heterogeneous semantic gaps between the cross-modal information. In the meantime, the concatenation operation has become de-facto standard to the cross-modal information fusion, which has a limited ability in information retrieval. In this paper, we propose a novel Knowledge-Bridge Graph Network (KBGN) model by using graph to bridge the cross-modal semantic relations between vision and text knowledge in fine granularity, as well as retrieving required knowledge via an adaptive information selection mode. Moreover, the reasoning clues for visual dialogue can be clearly drawn from intra-modal entities and inter-modal bridges. Experimental results on VisDial v1.0 and VisDial-Q datasets demonstrate that our model outperforms existing models with state-of-the-art results. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

Generalized label enhancement with sample correlations

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arXiv 2020年

作者： Zheng, Qinghai Zhu, Jihua Tang, Haoyu Liu, Xinyuan Li, Zhongyu Lu, Huimin Lab of Vision Computing and Machine Learning School of Software Engineering Xi'an Jiaotong University Xi'an710049 China Environment Recognition & Intelligent Computation Laboratory Kyushu Institute of Technology Japan

Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances. Different from single-label and multi-label annotations, label distributions describe the instance by multiple labels with different intensities and accommodate to more general scenes. Since most existing machine learning datasets merely provide logical labels, label distributions are unavailable in many real-world applications. To handle this problem, we propose two novel label enhancement methods, i.e., label Enhancement with Sample Correlations (LESC) and generalized label Enhancement with Sample Correlations (gLESC). More specifically, LESC employs a low-rank representation of samples in the feature space, and gLESC leverages a tensor multi-rank minimization to further investigate the sample correlations in both the feature space and label space. Benefitting from the sample correlations, the proposed methods can boost the performance of label enhancement. Extensive experiments on 14 benchmark datasets demonstrate the effectiveness and superiority of our methods. Copyright © 2020, The Authors. All rights reserved.

关键词： machine learning

An automatic breast cancer grading method in histopathological images based on pixel-, object-, and semantic-level features

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An automatic breast cancer grading method in histopathologic...

IEEE International Symposium on Biomedical Imaging

作者： Jiajia Cao Zengchang Qin Juan Jing Jianhui Chen Tao Wan Intelligent Computing & Machine Learning Lab Beihang University China School of Biological Science and Medical Engineering Beihang University China No. 91 Central Hospital of PLA Henan China

ISBN: (纸本)9781479923519

We present an automatic breast cancer grading method in histopathological images based on the computer extracted pixel-, object-, and semantic-level features derived from convolutional neural networks (CNN). The multiple level features allow not only characterization of nuclear polymorphism, but also extraction of structural and interpretable information within the images. In this study, a hybrid level set based segmentation method was used to segment nuclei from the images. A quantile normalization approach was utilized to improve image color consistency. The semantic level features are extracted by a CNN approach, which describe the proportions of nuclei belonging to the different grades, in conjunction with pixel-level (texture) and object-level (structure) features, to form an integrated set of attributes. A support vector machine classifier was trained to discriminate the breast cancer between low, intermediate, and high grades. The results demonstrated that our method achieved accuracy of 0.90 (low vs. high), and 0.74 (low vs. intermediate), and 0.76 (intermediate vs. high), suggesting that the present method could play a fundamental role in developing a computer-aided breast cancer grading system.

关键词： Feature extraction Breast cancer Image segmentation Support vector machines Image color analysis Neurons

Semantic modeling of textual relationships in cross-modal retrieval

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arXiv 2018年

作者： Yu, Jing Yang, Chenghao Qin, Zengchang Yang, Zhuoqian Hu, Yue Zhang, Weifeng Institute of Information Engineering Chinese Academy of Sciences China Intelligent Computing and Machine Learning Lab Beihang University China College of Mathematics Physics and Information Engineering Jiaxing University China

Feature modeling of different modalities is a basic problem in current research of cross-modal information retrieval. Existing models typically project texts and images into one embedding space, in which semantically similar information will have a shorter distance. Semantic modeling of textural relationships is notoriously difficult. In this paper, we propose an approach to model texts using a featured graph by integrating multi-view textual relationships including semantic relations, statistical co-occurrence, and prior relations in knowledge base. A dualpath neural network is adopted to learn multi-modal representations of information and cross-modal similarity measure jointly. We use a Graph Convolutional Network (GCN) for generating relation-aware text representations, and use a Convolutional Neural Network (CNN) with nonlinearities for image representations. The cross-modal similarity measure is learned by distance metric learning. Experimental results show that, by leveraging the rich relational semantics in texts, our model can outperform the state-of-the-art models by 3.4% and 6.3% on accuracy on two benchmark datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Knowledge graph