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Raven's Progressive Matrices Completion with Latent Gaussian Process Priors

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arXiv 2021年

作者： Shi, Fan Li, Bin Xue, Xiangyang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

Abstract reasoning ability is fundamental to human intelligence. It enables humans to uncover relations among abstract concepts and further deduce implicit rules from the relations. As a well-known abstract visual reasoning task, Raven's Progressive Matrices (RPM) are widely used in human IQ tests. Although extensive research has been conducted on RPM solvers with machine intelligence, few studies have considered further advancing the standard answer-selection (classification) problem to a more challenging answer-painting (generating) problem, which can verify whether the model has indeed understood the implicit rules. In this paper we aim to solve the latter one by proposing a deep latent variable model, in which multiple Gaussian processes are employed as priors of latent variables to separately learn underlying abstract concepts from RPMs;thus the proposed model is interpretable in terms of concept-specific latent variables. The latent Gaussian process also provides an effective way of extrapolation for answer painting based on the learned concept-changing rules. We evaluate the proposed model on RPMlike datasets with multiple continuously-changing visual concepts. Experimental results demonstrate that our model requires only few training samples to paint high-quality answers, generate novel RPM panels, and achieve interpretability through concept-specific latent variables. Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian distribution

Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study

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arXiv 2021年

作者： Yu, Haiyang Chen, Jingye Li, Bin Ma, Jianqi Guan, Mengnan Xu, Xixi Wang, Xiaocong Qu, Shaobo Xue, Xiangyang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

The flourishing blossom of deep learning has witnessed the rapid development of text recognition in recent years. However, the existing text recognition methods are mainly proposed for English texts. As another widely-spoken language, Chinese text recognition (CTR) in all ways has extensive application markets. Based on our observations, we attribute the scarce attention on CTR to the lack of reasonable dataset construction standards, unified evaluation protocols, and results of the existing baselines. To fill this gap, we manually collect CTR datasets from publicly available competitions, projects, and papers. According to application scenarios, we divide the collected datasets into four categories including scene, web, document, and handwriting datasets. Besides, we standardize the evaluation protocols in CTR. With unified evaluation protocols, we evaluate a series of representative text recognition methods on the collected datasets to provide baselines. The experimental results indicate that the performance of baselines on CTR datasets is not as good as that on English datasets due to the characteristics of Chinese texts that are quite different from the Latin alphabet. Moreover, we observe that by introducing radical-level supervision as an auxiliary task, the performance of baselines can be further boosted. The code and datasets are made publicly available at https://***/FudanVI/benchmarking-chinese-text-recognition. Copyright © 2021, The Authors. All rights reserved.

关键词： Character recognition

Heterogeneous graph neural networks for extractive document summarization 58

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Heterogeneous graph neural networks for extractive document ...

58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Wang, Danqing Liu, Pengfei Zheng, Yining Qiu, Xipeng Huang, Xuanjing Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University 825 Zhangheng Road Shanghai China

ISBN: (纸本)9781952148255

As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships. In this paper, we present a heterogeneous graph-based neural network for extractive summarization (HETERSUMGRAPH), which contains semantic nodes of different granularity levels apart from sentences. These additional nodes act as the intermediary between sentences and enrich the cross-sentence relations. Besides, our graph structure is flexible in natural extension from a single-document setting to multi-document via introducing document nodes. To our knowledge, we are the first one to introduce different types of nodes into graph-based neural networks for extractive document summarization and perform a comprehensive qualitative analysis to investigate their benefits. The code will be released on Github. © 2020 Association for Computational Linguistics

关键词： Graph neural networks

Unsupervised learning of compositional scene representations from multiple unspecified viewpoints

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arXiv 2021年

作者： Yuan, Jinyang Li, Bin Xue, Xiangyang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

Visual scenes are extremely rich in diversity, not only because there are infinite combinations of objects and background, but also because the observations of the same scene may vary greatly with the change of viewpoints. When observing a visual scene that contains multiple objects from multiple viewpoints, humans are able to perceive the scene in a compositional way from each viewpoint, while achieving the so-called "object constancy" across different viewpoints, even though the exact viewpoints are untold. This ability is essential for humans to identify the same object while moving and to learn from vision efficiently. It is intriguing to design models that have the similar ability. In this paper, we consider a novel problem of learning compositional scene representations from multiple unspecified viewpoints without using any supervision, and propose a deep generative model which separates latent representations into a viewpoint-independent part and a viewpoint-dependent part to solve this problem. To infer latent representations, the information contained in different viewpoints is iteratively integrated by neural networks. Experiments on several specifically designed synthetic datasets have shown that the proposed method is able to effectively learn from multiple unspecified viewpoints. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping

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arXiv 2022年

作者： Xu, Jilan Hou, Junlin Zhang, Yuejie Feng, Rui Zhao, Rui-Wei Zhang, Tao Lu, Xuequan Gao, Shang School of Computer Science Shanghai Key Lab of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China Academy for Engineer and Technology Fudan University China Shanghai University of Finance and Economics China Deakin University Australia

Weakly Supervised Object Localization (WSOL) aims to localize objects with image-level supervision. Existing works mainly rely on Class Activation Mapping (CAM) derived from a classification model. However, CAM-based methods usually focus on the most discriminative parts of an object (i.e., incomplete localization problem). In this paper, we empirically prove that this problem is associated with the mixup of the activation values between less discriminative foreground regions and the background. To address it, we propose Class RE-Activation Mapping (CREAM), a novel clustering-based approach to boost the activation values of the integral object regions. To this end, we introduce class-specific foreground and background context embeddings as cluster centroids. A CAM-guided momentum preservation strategy is developed to learn the context embeddings during training. At the inference stage, the re-activation mapping is formulated as a parameter estimation problem under Gaussian Mixture Model, which can be solved by deriving an unsupervised Expectation-Maximization based soft-clustering algorithm. By simply integrating CREAM into various WSOL approaches, our method significantly improves their performance. CREAM achieves the state-of-the-art performance on CUB, ILSVRC and OpenImages benchmark datasets. Code will be available at https://***/Jazzcharles/CREAM. © 2022, CC BY.

关键词： Object recognition

Zero-shot Chinese character recognition with stroke-level decomposition

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arXiv 2021年

作者： Chen, Jingye Li, Bin Xue, Xiangyang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

Chinese character recognition has attracted much research interest due to its wide applications. Although it has been studied for many years, some issues in this field have not been completely resolved yet, e.g. the zero-shot problem. Previous character-based and radical-based methods have not fundamentally addressed the zero-shot problem since some characters or radicals in test sets may not appear in training sets under a data-hungry condition. Inspired by the fact that humans can generalize to know how to write characters unseen before if they have learned stroke orders of some characters, we propose a stroke-based method by decomposing each character into a sequence of strokes, which are the most basic units of Chinese characters. However, we observe that there is a one-to-many relationship between stroke sequences and Chinese characters. To tackle this challenge, we employ a matching-based strategy to transform the predicted stroke sequence to a specific character. We evaluate the proposed method on handwritten characters, printed artistic characters, and scene characters. The experimental results validate that the proposed method outperforms existing methods on both character zero-shot and radical zero-shot tasks. Moreover, the proposed method can be easily generalized to other languages whose characters can be decomposed into strokes. © 2021, CC BY.

关键词： Technology transfer

Enhancing scientific papers summarization with citation graph

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arXiv 2021年

作者： An, Chenxin Zhong, Ming Chen, Yiran Wang, Danqing Qiu, Xipeng Huang, Xuanjing Shanghai Key Laboratory of Intelligent Information Processing Fudan University School of Computer Science China

Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network. However, scientific papers are full of uncommon domain-specific terms, making it almost impossible for the model to understand its true meaning without the help of the relevant research community. In this paper, we redefine the task of scientific papers summarization by utilizing their citation graph and propose a citation graph-based summarization model (CGSUM) which can incorporate the information of both the source paper and its references. In addition, we construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains and 661K citation relationships. The entire dataset constitutes a large connected citation graph. Extensive experiments show that our model can achieve competitive performance when compared with the pretrained models even with a simple architecture. The results also indicates the citation graph is crucial to better understand the content of papers and generate high-quality summaries. Our code and dataset are available on https://***/ChenxinAn-fdu/CGSum. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

Computational Methods for Prediction of Human Protein-Phenotype Associations:A Review

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Phenomics 2021年第4期1卷 171-185页

作者： Lizhi Liu Shanfeng Zhu School of Computer Science Fudan UniversityShanghai 200433China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of EducationShanghai 200433China MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433China Zhangjiang Fudan International Innovation Center Shanghai 200433China Shanghai Key Lab of Intelligent Information Processing Fudan UniversityShanghai 200433China

Deciphering the relationship between human proteins(genes)and phenotypes is one of the fundamental tasks in phenomics *** Human Phenotype Ontology(HPO)builds upon a standardized logical vocabulary to describe the abnormal phenotypes encountered in human diseases and paves the way towards the computational analysis of their genetic *** date,many computational methods have been proposed to predict the HPO annotations of *** this paper,we conduct a comprehensive review of the existing approaches to predicting HPO annotations of novel proteins,identifying missing HPO annotations,and prioritizing candidate proteins with respect to a certain HPO *** each topic,we first give the formalized description of the problem,and then systematically revisit the published literatures highlighting their advantages and disadvantages,followed by the discussion on the challenges and promising future *** addition,we point out several potential topics to be worthy of exploration including the selection of negative HPO annotations and detecting HPO *** believe that this review will provide insight to the researchers in the field of computational phenotype analyses in terms of comprehending and developing novel prediction algorithms.

关键词： Human Phenotype Ontology(HPO) Human protein-phenotype association HPO annotation Machine learning Deep learning

HF-Mid: A Hybrid Framework of Network Intrusion Detection for Multi-type and Imbalanced Data

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HF-Mid: A Hybrid Framework of Network Intrusion Detection fo...

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Tianbo Wang Guotao Huang Xiaopeng Liang Chunhe Xia Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for Multi-type and Imbalance Data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-Distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep Neural Network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

关键词： Training Privacy Fuses Noise Semantics Artificial neural networks Feature extraction