Jianguo Xiao

AAAI Conference 2019 Conference Paper

SCFont: Structure-Guided Chinese Font Generation via Deep Stacked Networks

• Yue Jiang
• Zhouhui Lian
• Yingmin Tang
• Jianguo Xiao

Automatic generation of Chinese fonts that consist of large numbers of glyphs with complicated structures is now still a challenging and ongoing problem in areas of AI and Computer Graphics (CG). Traditional CG-based methods typically rely heavily on manual interventions, while recentlypopularized deep learning-based end-to-end approaches often obtain synthesis results with incorrect structures and/or serious artifacts. To address those problems, this paper proposes a structure-guided Chinese font generation system, SCFont, by using deep stacked networks. The key idea is to integrate the domain knowledge of Chinese characters with deep generative networks to ensure that high-quality glyphs with correct structures can be synthesized. More specifically, we first apply a CNN model to learn how to transfer the writing trajectories with separated strokes in the reference font style into those in the target style. Then, we train another CNN model learning how to recover shape details on the contour for synthesized writing trajectories. Experimental results validate the superiority of the proposed SCFont compared to the state of the art in both visual and quantitative assessments.

IJCAI Conference 2017 Conference Paper

From Neural Sentence Summarization to Headline Generation: A Coarse-to-Fine Approach

• Jiwei Tan
• Xiaojun Wan
• Jianguo Xiao

Headline generation is a task of abstractive text summarization, and previously suffers from the immaturity of natural language generation techniques. Recent success of neural sentence summarization models shows the capacity of generating informative, fluent headlines conditioned on selected recapitulative sentences. In this paper, we investigate the extension of sentence summarization models to the document headline generation task. The challenge is that extending the sentence summarization model to consider more document information will mostly confuse the model and hurt the performance. In this paper, we propose a coarse-to-fine approach, which first identifies the important sentences of a document using document summarization techniques, and then exploits a multi-sentence summarization model with hierarchical attention to leverage the important sentences for headline generation. Experimental results on a large real dataset demonstrate the proposed approach significantly improves the performance of neural sentence summarization models on the headline generation task.

AAAI Conference 2016 Conference Paper

Tweet Timeline Generation with Determinantal Point Processes

• Jin-ge Yao
• Feifan Fan
• Wayne Xin Zhao
• Xiaojun Wan
• Edward Chang
• Jianguo Xiao

The task of tweet timeline generation (TTG) aims at selecting a small set of representative tweets to generate a meaningful timeline and providing enough coverage for a given topical query. This paper presents an approach based on determinantal point processes (DPPs) by jointly modeling the topical relevance of each selected tweet and overall selectional diversity. Aiming at better treatment for balancing relevance and diversity, we introduce two novel strategies, namely spectral rescaling and topical prior. Extensive experiments on the public TREC 2014 dataset demonstrate that our proposed DPP model along with the two strategies can achieve fairly competitive results against the state-of-the-art TTG systems.

IJCAI Conference 2015 Conference Paper

Aesthetic Visual Quality Evaluation of Chinese Handwritings

• Rongju Sun
• Zhouhui Lian
• Yingmin Tang
• Jianguo Xiao

Aesthetic evaluation of Chinese calligraphy is one of the most challenging tasks in Artificial Intelligence. This paper attempts to solve this problem by proposing a number of aesthetic feature representations and feeding them into Artificial Neural Networks. Specifically, 22 global shape features are presented to describe a given handwritten Chinese character from different aspects according to classical calligraphic rules, and a new 10-dimensional feature vector is introduced to represent the component layout information using sparse coding. Moreover, a Chinese Handwriting Aesthetic Evaluation Database (CHAED) is also built by collecting 1000 Chinese handwriting images with diverse aesthetic qualities and inviting 33 subjects to evaluate the aesthetic quality for each calligraphic image. Finally, back propagation neural networks are constructed with the concatenation of the proposed features as input and then trained on our CHAED database for the aesthetic evaluation of Chinese calligraphy. Experimental results demonstrate that the proposed AI system provides a comparable performance with human evaluation. Through our experiments, we also compare the importance of each individual feature and reveal the relationship between our aesthetic features and the aesthetic perceptions of human beings.

IJCAI Conference 2015 Conference Paper

Compressive Document Summarization via Sparse Optimization

• Jin-ge Yao
• Xiaojun Wan
• Jianguo Xiao

In this paper, we formulate a sparse optimization framework for extractive document summarization. The proposed framework has a decomposable convex objective function. We derive an efficient ADMM algorithm to solve it. To encourage diversity in the summaries, we explicitly introduce an additional sentence dissimilarity term in the optimization framework. We achieve significant improvement over previous related work under similar data reconstruction framework. We then generalize our formulation to the case of compressive summarization and derive a block coordinate descent algorithm to optimize the objective function. Performance on DUC 2006 and DUC 2007 datasets shows that our compressive summarization results are competitive against the state-of-the-art results while maintaining reasonable readability.

AAAI Conference 2015 Conference Paper

Learning to Recommend Quotes for Writing

• Jiwei Tan
• Xiaojun Wan
• Jianguo Xiao

In this paper, we propose and address a novel task of recommending quotes for writing. Quote is short for quotation, which is the repetition of someone else’s statement or thoughts. It is a common case in our writing when we would like to cite someone’s statement, like a proverb or a statement by some famous people, to make our composition more elegant or convincing. However, sometimes we are so eager to make a citation of quote somewhere, but have no idea about the relevant quote to express our idea. Because knowing or remembering so many quotes is not easy, it is exciting to have a system to recommend relevant quotes for us while writing. In this paper we tackle this appealing AI task, and build up a learning framework for quote recommendation. We collect abundant quotes from the Internet, and mine real contexts containing these quotes from large amount of electronic books, to build up a dataset for experiments. We explore the particular features of this task, and propose a few useful features to model the characteristics of quotes and the relevance of quotes to contexts. We apply a supervised learning to rank model to integrate multiple features. Experiment results show that, our proposed approach is appropriate for this task and it outperforms other recommendation methods.

AAAI Conference 2015 Conference Paper

Representation Learning for Aspect Category Detection in Online Reviews

• Xinjie Zhou
• Xiaojun Wan
• Jianguo Xiao

User-generated reviews are valuable resources for decision making. Identifying the aspect categories discussed in a given review sentence (e. g. , “food” and “service” in restaurant reviews) is an important task of sentiment analysis and opinion mining. Given a predefined aspect category set, most previous researches leverage handcrafted features and a classification algorithm to accomplish the task. The crucial step to achieve better performance is feature engineering which consumes much human effort and may be unstable when the product domain changes. In this paper, we propose a representation learning approach to automatically learn useful features for aspect category detection. Specifically, a semi-supervised word embedding algorithm is first proposed to obtain continuous word representations on a large set of reviews with noisy labels. Afterwards, we propose to generate deeper and hybrid features through neural networks stacked on the word vectors. A logistic regression classifier is finally trained with the hybrid features to predict the aspect category. The experiments are carried out on a benchmark dataset released by SemEval-2014. Our approach achieves the state-of-theart performance and outperforms the best participating team as well as a few strong baselines.

AAAI Conference 2014 Conference Paper

Cross-View Feature Learning for Scalable Social Image Analysis

• Wenxuan Xie
• Yuxin Peng
• Jianguo Xiao

Nowadays images on social networking websites (e. g. , Flickr) are mostly accompanied with user-contributed tags, which help cast a new light on the conventional content-based image analysis tasks such as image classification and retrieval. In order to establish a scalable social image analysis system, two issues need to be considered: 1) Supervised learning is a futile task in modeling the enormous number of concepts in the world, whereas unsupervised approaches overcome this hurdle; 2) Algorithms are required to be both spatially and temporally efficient to handle large-scale datasets. In this paper, we propose a cross-view feature learning (CVFL) framework to handle the problem of social image analysis effectively and efficiently. Through explicitly modeling the relevance between image content and tags (which is empirically shown to be visually and semantically meaningful), CVFL yields more promising results than existing methods in the experiments. More importantly, being general and descriptive, CVFL and its variants can be readily applied to other large-scale multi-view tasks in unsupervised setting.

AAAI Conference 2014 Conference Paper

Semantic Graph Construction for Weakly-Supervised Image Parsing

• Wenxuan Xie
• Yuxin Peng
• Jianguo Xiao

We investigate weakly-supervised image parsing, i. e. , assigning class labels to image regions by using imagelevel labels only. Existing studies pay main attention to the formulation of the weakly-supervised learning problem, i. e. , how to propagate class labels from images to regions given an affinity graph of regions. Notably, however, the affinity graph of regions, which is generally constructed in relatively simpler settings in existing methods, is of crucial importance to the parsing performance due to the fact that the weakly-supervised parsing problem cannot be solved within a single image, and that the affinity graph enables label propagation among multiple images. In order to embed more semantics into the affinity graph, we propose novel criteria by exploiting the weak supervision information carefully, and develop two graphs: L1 semantic graph and k-NN semantic graph. Experimental results demonstrate that the proposed semantic graphs not only capture more semantic relevance, but also perform significantly better than conventional graphs in image parsing.

AAAI Conference 2013 Conference Paper

Heterogeneous Metric Learning with Joint Graph Regularization for Cross-Media Retrieval

• Xiaohua Zhai
• Yuxin Peng
• Jianguo Xiao

As the major component of big data, unstructured heterogeneous multimedia content such as text, image, audio, video and 3D increasing rapidly on the Internet. User demand a new type of cross-media retrieval where user can search results across various media by submitting query of any media. Since the query and the retrieved results can be of different media, how to learn a heterogeneous metric is the key challenge. Most existing metric learning algorithms only focus on a single media where all of the media objects share the same data representation. In this paper, we propose a joint graph regularized heterogeneous metric learning (JGRHML) algorithm, which integrates the structure of different media into a joint graph regularization. In JGRHML, different media are complementary to each other and optimizing them simultaneously can make the solution smoother for both media and further improve the accuracy of the final metric. Based on the heterogeneous metric, we further learn a high-level semantic metric through label propagation. JGRHML is effective to explore the semantic relationship hidden across different modalities. The experimental results on two datasets with up to five media types show the effectiveness of our proposed approach.

IJCAI Conference 2009 Conference Paper

• Xiaojun Wan
• Jianguo Xiao

Graph-based manifold-ranking methods have been successfully applied to topic-focused multi-document summarization. This paper further proposes to use the multi-modality manifold-ranking algorithm for extracting topic-focused summary from multiple documents by considering the within-document sentence relationships and the cross-document sentence relationships as two separate modalities (graphs). Three different fusion schemes, namely linear form, sequential form and score combination form, are exploited in the algorithm. Experimental results on the DUC benchmark datasets demonstrate the effectiveness of the proposed multi-modality learning algorithms with all the three fusion schemes.

IJCAI Conference 2007 Conference Paper

• Xiaojun Wan
• Jianwu Yang
• Jianguo Xiao

Topic-focused multi-document summarization aims to produce a summary biased to a given topic or user profile. This paper presents a novel extractive approach based on manifold-ranking of sentences to this summarization task. The manifold-ranking process can naturally make full use of both the relationships among all the sentences in the documents and the relationships between the given topic and the sentences. The ranking score is obtained for each sentence in the manifold-ranking process to denote the biased information richness of the sentence. Then the greedy algorithm is employed to impose diversity penalty on each sentence. The summary is produced by choosing the sentences with both high biased information richness and high information novelty. Experiments on DUC2003 and DUC2005 are performed and the ROUGE evaluation results show that the proposed approach can significantly outperform existing approaches of the top performing systems in DUC tasks and baseline approaches.