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Liang Yao

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7 papers

1 author row

AAAI Conference 2026 System Paper

AirNavigation: Let UAV Navigation Tell Its Own Story

Jianyu Jiang
Zequan Wang
Liang Yao
Shengxiang Xu
Fan Liu

Testing autonomous navigation algorithms of Unmanned Aerial Vehicles (UAVs) in real-world scenarios often entails significant safety risks. In this paper, we aim to build a flexible yet user-friendly UAV autonomous navigation simulator. Ideally, it should closely emulate real-world environments, support diverse UAV models and algorithms, and provide a flexible evaluation framework. Existing frameworks fail to satisfy all three requirements simultaneously. To this end, we present AirNavigation, an integrated simulation platform designed to support the end-to-end workflow of UAV navigation research. Specifically, our system leverages Unreal Engine to simulate highly realistic environments and diverse UAV models. It further facilitates semi-automated scene generation and multi-modal synthetic training data production. To lower the barrier of adoption, we develop a suite of user-friendly interfaces to enable seamless integration of diverse navigation algorithms. Moreover, we introduce a novel evaluation system powered by large language models to deliver personalized and fine-grained performance analysis.

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AAAI Conference 2026 Conference Paper

RemoteReasoner: Towards Unifying Geospatial Reasoning Workflow

Liang Yao
Fan Liu
Hongbo Lu
Chuanyi Zhang
Rui Min
Shengxiang Xu
Shimin Di
Pai Peng

Remote sensing imagery presents vast, inherently unstructured spatial data, necessitating sophisticated reasoning to interpret complex user intents and contextual relationships beyond simple recognition tasks. In this paper, we aim to construct an Earth observation workflow to handle complex queries by reasoning about spatial context and user intent. As a reasoning workflow, it should autonomously explore and construct its own inference paths, rather than being confined to predefined ground‑truth sequences. Ideally, its architecture ought to be unified yet generalized, possessing capabilities to perform diverse reasoning tasks through one model without requiring additional fine-tuning. Existing remote sensing approaches rely on supervised fine-tuning paradigms and task‑specific heads, limiting both autonomous reasoning and unified generalization. To this end, we propose RemoteReasoner, a unified workflow for geospatial reasoning. The design of RemoteReasoner integrates a multi-modal large language model (MLLM) for interpreting user instructions and localizing targets, together with task transformation strategies that enable multi-granularity tasks, including object-, region-, and pixel-level. In contrast to existing methods, our framework is trained with reinforcement learning (RL) to endow the MLLM sufficient reasoning autonomy. At the inference stage, our transformation strategies enable diverse task output formats without requiring task-specific decoders or further fine-tuning. Experiments demonstrated that RemoteReasoner achieves state-of-the-art performance across multi-granularity reasoning tasks. Furthermore, it retains the MLLM's inherent generalization capability, demonstrating robust performance on unseen tasks and categories.

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EAAI Journal 2024 Journal Article

Multi-view clustering using a flexible and optimal multi-graph fusion method

YaoZu Kan
Gui-Fu Lu
Liang Yao
Bing Cai
JinBiao Zhao

Recently, many multi-view clustering (MVC) methods based on graphs have been proposed to address prevalent multi-view data. For these methods, the multi-graph fusion step, aim of which is to obtain a consensus graph is vital for obtaining good clustering performance. However, these methods still have two problems. First, the column sum of the consensus graph is restricted to one, which is not flexible enough for practical applications. Affected by the sum-to-one constraint, the similarity between some noisy points may be large, which may cause these abnormal points to be grouped into a separate cluster. Second, the cluster structure of the consensus graph is often not considered. To address these problems, in this paper, we propose a novel multi-view clustering using a flexible and optimal multi-graph fusion method (MVC/FOMF). Specifically, we first obtain the similarity graph of each view by using red the self-expressive method. Second, we fuse these graphs into a consensus graph whose column sum is constrained to s ( 0 < s ≤ 1 ), and we can adjust s to look for the best clustering performance. Third, we impose a rank constraint on the Laplacian matrix of the consensus graph to learn the best clustering structure. Finally, all these steps are unified into a framework and the corresponding optimization procedure, which is based on the alternating multiplier method, is also designed. More importantly, the complexity of our algorithm is also lower than those of many representative algorithms. Compared with that of the state-of-the-art algorithms, our algorithm shows very encouraging performance on some datasets. The code can be found at https: //github. com/wulala2233/MVCFOMF.

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AAAI Conference 2022 Conference Paper

DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation

Qi Xu
Liang Yao
Zhengkai Jiang
Guannan Jiang
Wenqing Chu
Wenhui Han
Wei Zhang
Chengjie Wang

Model generalization to the unseen scenes is crucial to realworld applications, such as autonomous driving, which requires robust vision systems. To enhance the model generalization, domain generalization through learning the domaininvariant representation has been widely studied. However, most existing works learn the shared feature space within multi-source domains but ignore the characteristic of the feature itself (e. g. , the feature sensitivity to the domain-specific style). Therefore, we propose the Domain-invariant Representation Learning (DIRL) for domain generalization which utilizes the feature sensitivity as the feature prior to guide the enhancement of the model generalization capability. The guidance reflects in two folds: 1) Feature re-calibration that introduces the Prior Guided Attention Module (PGAM) to emphasize the insensitive features and suppress the sensitive features. 2): Feature whiting that proposes the Guided Feature Whiting (GFW) to remove the feature correlations which are sensitive to the domain-specific style. We construct the domain-invariant representation which suppresses the effect of the domain-specific style on the quality and correlation of the features. As a result, our method is simple yet effective, and can enhance the robustness of various backbone networks with little computational cost. Extensive experiments over multiple domains generalizable segmentation tasks show the superiority of our approach to other methods.

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AAAI Conference 2019 Conference Paper

Graph Convolutional Networks for Text Classification

Liang Yao
Chengsheng Mao
Yuan Luo

Text classification is an important and classical problem in natural language processing. There have been a number of studies that applied convolutional neural networks (convolution on regular grid, e. g. , sequence) to classification. However, only a limited number of studies have explored the more flexible graph convolutional neural networks (convolution on non-grid, e. g. , arbitrary graph) for the task. In this work, we propose to use graph convolutional networks for text classification. We build a single text graph for a corpus based on word co-occurrence and document word relations, then learn a Text Graph Convolutional Network (Text GCN) for the corpus. Our Text GCN is initialized with one-hot representation for word and document, it then jointly learns the embeddings for both words and documents, as supervised by the known class labels for documents. Our experimental results on multiple benchmark datasets demonstrate that a vanilla Text GCN without any external word embeddings or knowledge outperforms state-of-the-art methods for text classification. On the other hand, Text GCN also learns predictive word and document embeddings. In addition, experimental results show that the improvement of Text GCN over state-of-the-art comparison methods become more prominent as we lower the percentage of training data, suggesting the robustness of Text GCN to less training data in text classification.

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AAAI Conference 2017 Conference Paper

Incorporating Knowledge Graph Embeddings into Topic Modeling

Liang Yao
Yin Zhang
Baogang Wei
Zhe Jin
Rui Zhang
Yangyang Zhang
Qinfei Chen

Probabilistic topic models could be used to extract lowdimension topics from document collections. However, such models without any human knowledge often produce topics that are not interpretable. In recent years, a number of knowledge-based topic models have been proposed, but they could not process fact-oriented triple knowledge in knowledge graphs. Knowledge graph embeddings, on the other hand, automatically capture relations between entities in knowledge graphs. In this paper, we propose a novel knowledge-based topic model by incorporating knowledge graph embeddings into topic modeling. By combining latent Dirichlet allocation, a widely used topic model with knowledge encoded by entity vectors, we improve the semantic coherence signiﬁcantly and capture a better representation of a document in the topic space. Our evaluation results will demonstrate the effectiveness of our method.

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EAAI Journal 2017 Journal Article

Mining coherent topics in documents using word embeddings and large-scale text data

Liang Yao
Yin Zhang
Qinfei Chen
Hongze Qian
Baogang Wei
Zhifeng Hu

Probabilistic topic models have been extensively used to extract low-dimension aspects from document collections. However, such models without any human knowledge often generate topics that are not interpretable. Recently, a number of knowledge-based topic models have been proposed, which enable users to input prior domain knowledge to produce more meaningful and coherent topics. Word embeddings, on the other hand, can automatically capture both semantic and syntactic information of words from a large amount of documents, and can be used to measure word similarities. In this paper, we incorporate word embeddings obtained from a large number of domains into topic modeling. By combining Latent Dirichlet Allocation, a widely used topic model with Skip-Gram, a well-known framework for learning word vectors, we improve the semantic coherence significantly. Our evaluation results using product review documents from 100 domains will demonstrate the effectiveness of our method.

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