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Xi Zhou

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14 papers

2 author rows

AAAI Conference 2026 Conference Paper

M3UCD: A Multi-task Multimodal Metaphor Understanding Challenge Dataset for LLMs

Tianlong Zheng
Yating Yang
Rui Dong
Bo Ma
Lei Wang
Xi Zhou
Siru Miao
Turghun Osman

Understanding multimodal metaphors represents a crucial pathway for machines to comprehend human cognition. However, current research remains constrained by superficial dataset annotations, insufficient systematic evaluation of large language models, and fragmented task frameworks. To bridge these gaps, the paper proposes a systematic solution featuring: (I) We present the largest fine-grained Multi-task Multimodal Metaphor Understanding Challenge Dataset (M3UCD) built via multi-perspective collaborative annotation. It contains 15,345 samples, each annotated with 12 manual attribute labels. (II) Systematic benchmarking of LLMs' capacity boundaries in metaphor understanding. Evaluation results reveal the persistent challenges LLMs face in this domain while validating M3UCD's effectiveness and potential. (III) A concise and unified multi-task baseline framework was developed and demonstrated its effectiveness in enhancing the metaphor understanding capabilities of MLLMs.

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AAAI Conference 2025 Conference Paper

Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment

Xiao Shen
Zhihao Chen
Shirui Pan
Shuang Zhou
Laurence T. Yang
Xi Zhou

Existing cross-network node classification methods are mainly proposed for closed-set setting, where the source network and the target network share exactly the same label space. Such a setting is restricted in real-world applications, since the target network might contain additional classes that are not present in the source. In this work, we study a more realistic open-set cross-network node classification (O-CNNC) problem, where the target network contains all the known classes in the source and further contains several target-private classes unseen in the source. Borrowing the concept from open-set domain adaptation, all target-private classes are defined as an additional “unknown” class. To address the challenging O-CNNC problem, we propose an unknown-excluded adversarial graph domain alignment (UAGA) model with a separate-adapt training strategy. Firstly, UAGA roughly separates known classes from unknown class, by training a graph neural network encoder and a neighborhood-aggregation node classifier in an adversarial framework. Then, unknown-excluded adversarial domain alignment is customized to align only target nodes from known classes with the source, while pushing target nodes from unknown class far away from the source, by assigning positive and negative domain adaptation coefficient to known class nodes and unknown class nodes. Extensive experiments on real-world datasets demonstrate significant outperformance of the proposed UAGA over state-of-the-art methods on O-CNNC.

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JBHI Journal 2024 Journal Article

Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering

Guodong Li
Bowei Zhao
Xiaorui Su
Yue Yang
Pengwei Hu
Xi Zhou
Lun Hu

As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.

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ECAI Conference 2024 Conference Paper

TabCGOK: Intra-Class Groups Retrieval and Inter-Class Ordinal Knowledge Augmented Network for Ordinal Tabular Data Prediction

Zhengdong Luo
Abibulla Atawulla
Fengyi Yang
Yongqing Zhu
Yixiao Ren
Yunfei Han
Xi Zhou

Ordinal tabular data, with advantages of structured knowledge representation in tabular data and the characteristic of inter-class ranks, has drawn increasing attention. However, existing retrieval-based tabular deep learning methods designed primarily for classical tabular data pay less attention to ordinal tabular data. Ordinal knowledge of ordinal tabular data provides a more explicit objective for tabular ordinal classification by considering both classification and regression properties. Furthermore, these approaches overlook the significance of intra-class group features which can balance the retrieved probability of various sample size groups and capture shared knowledge among multiple samples within same group. In this work, we propose the Intra-Class Groups Retrieval and Inter-Class Ordinal Knowledge Augmented Network (TabCGOK) model for ordinal tabular data prediction, equipped with Intra-Class Groups Retrieval (CG) module and Inter-Class Ordinal Knowledge Augmented (OK) module. The CG module provides intra-class group features candidate set for subsequent retrieval operation. It divides each class into several groups, then extracts the representation of each group as intra-class group features. And the intra-class group features candidate set consists of all intra-class group features from each class. The OK module is designed to capture inter-class ordinal knowledge. It estimates the ordinal distances by calculating inter-class feature distances, which could correspond to the inter-class non-isometric nature of ordinal knowledge, and then aggregates the previous ordinal distances to clarify the containment relationship of ordinal knowledge. OK module utilizes the attention mechanism for fusing the captured ordinal knowledge to retrieved intra-class group features. Finally, TabCGOK integrates fused intra-class group features with sample level features for ordinal tabular data prediction. Extensive experiments on several ordinal tabular datasets demonstrate the effectiveness of our method. The source code is available at https: //github. com/luozhengdong/TabCGOK.

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NeurIPS Conference 2024 Conference Paper

WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark

Chunhui Zhang
Li Liu
Guanjie Huang
Hao Wen
Xi Zhou
Yanfeng Wang

Underwater Object Tracking (UOT) is essential for identifying and tracking submerged objects in underwater videos, but existing datasets are limited in scale, diversity of target categories and scenarios covered, impeding the development of advanced tracking algorithms. To bridge this gap, we take the first step and introduce WebUOT-1M, \ie, the largest public UOT benchmark to date, sourced from complex and realistic underwater environments. It comprises 1. 1 million frames across 1, 500 video clips filtered from 408 target categories, largely surpassing previous UOT datasets, \eg, UVOT400. Through meticulous manual annotation and verification, we provide high-quality bounding boxes for underwater targets. Additionally, WebUOT-1M includes language prompts for video sequences, expanding its application areas, \eg, underwater vision-language tracking. Given that most existing trackers are designed for open-air conditions and perform poorly in underwater environments due to domain gaps, we propose a novel framework that uses omni-knowledge distillation to train a student Transformer model effectively. To the best of our knowledge, this framework is the first to effectively transfer open-air domain knowledge to the UOT model through knowledge distillation, as demonstrated by results on both existing UOT datasets and the newly proposed WebUOT-1M. We have thoroughly tested WebUOT-1M with 30 deep trackers, showcasing its potential as a benchmark for future UOT research. The complete dataset, along with codes and tracking results, are publicly accessible at \href{https: //github. com/983632847/Awesome-Multimodal-Object-Tracking}{\color{magenta}{here}}.

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AAAI Conference 2023 Conference Paper

A Domain-Transfer Meta Task Design Paradigm for Few-Shot Slot Tagging

Fengyi Yang
Xi Zhou
Yating Yang
Bo Ma
Rui Dong
Abibulla Atawulla

Few-shot slot tagging is an important task in dialogue systems and attracts much attention of researchers. Most previous few-shot slot tagging methods utilize meta-learning procedure for training and strive to construct a large number of different meta tasks to simulate the testing situation of insufficient data. However, there is a widespread phenomenon of overlap slot between two domains in slot tagging. Traditional meta tasks ignore this special phenomenon and cannot simulate such realistic few-shot slot tagging scenarios. It violates the basic principle of meta-learning which the meta task is consistent with the real testing task, leading to historical information forgetting problem. In this paper, we introduce a novel domain-transfer meta task design paradigm to tackle this problem. We distribute a basic domain to each target domain based on the coincidence degree of slot labels between these two domains. Unlike classic meta tasks which only rely on small samples of target domain, our meta tasks aim to correctly infer the class of target domain query samples based on both abundant data in basic domain and scarce data in target domain. To accomplish our meta task, we propose a Task Adaptation Network to effectively transfer the historical information from the basic domain to the target domain. We carry out sufficient experiments on the benchmark slot tagging dataset SNIPS and the name entity recognition dataset NER. Results demonstrate that our proposed model outperforms previous methods and achieves the state-of-the-art performance.

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AAAI Conference 2023 Conference Paper

Neighbor Contrastive Learning on Learnable Graph Augmentation

Xiao Shen
Dewang Sun
Shirui Pan
Xi Zhou
Laurence T. Yang

Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various graph datasets. In addition, the contrastive losses originally developed in computer vision have been directly applied to graph data, where the neighboring nodes are regarded as negatives and consequently pushed far apart from the anchor. However, this is contradictory with the homophily assumption of net-works that connected nodes often belong to the same class and should be close to each other. In this work, we propose an end-to-end automatic GCL method, named NCLA to apply neighbor contrastive learning on learnable graph augmentation. Several graph augmented views with adaptive topology are automatically learned by the multi-head graph attention mechanism, which can be compatible with various graph datasets without prior domain knowledge. In addition, a neighbor contrastive loss is devised to allow multiple positives per anchor by taking network topology as the supervised signals. Both augmentations and embeddings are learned end-to-end in the proposed NCLA. Extensive experiments on the benchmark datasets demonstrate that NCLA yields the state-of-the-art node classification performance on self-supervised GCL and even exceeds the supervised ones, when the labels are extremely limited. Our code is released at https://github.com/shenxiaocam/NCLA.

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IJCAI Conference 2022 Conference Paper

Diversity Features Enhanced Prototypical Network for Few-shot Intent Detection

Fengyi Yang
Xi Zhou
Yi Wang
Abibulla Atawulla
Ran Bi

Few-shot Intent Detection (FSID) is a challenging task in dialogue systems due to the scarcity of available annotated utterances. Although existing few-shot learning approaches have made remarkable progress, they fall short in adapting to the Generalized Few-shot Intent Detection (GFSID) task where both seen and unseen classes are present. A core problem of the simultaneous existence of these two tasks is that limited training samples fail to cover the diversity of user expressions. In this paper, we propose an effective Diversity Features Enhanced Prototypical Network (DFEPN) to enhance diversity features for novel intents by fully exploiting the diversity of known intent samples. Specially, DFEPN generates diversity features of samples in the hidden space via a diversity feature generator module and then fuses these features with original support vectors to get a more suitable prototype vector of each class. To evaluate the effectiveness of our model on both FSID and GFSID tasks, we carry out sufficient experiments on two benchmark intent detection datasets. Results demonstrate that our proposed model outperforms existing state-of-the-art methods and keeps stable performance on both two tasks.

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AAAI Conference 2021 Conference Paper

Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue

Longxiang Liu
Zhuosheng Zhang
Hai Zhao
Xi Zhou
Xiang Zhou

A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. Thus utterance- and speaker-aware clues are supposed to be well captured in models. However, in the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely by taking the pairwise dialogue history and candidate response as a whole, the hierarchical information on either utterance interrelation or speaker roles coupled in such representations is not well addressed. In this work, we propose a novel model to fill such a gap by modeling the effective utterance-aware and speakeraware representations entailed in a dialogue history. In detail, we decouple the contextualized word representations by masking mechanisms in Transformer-based PrLM, making each word only focus on the words in current utterance, other utterances, two speaker roles (i. e. , utterances of sender and utterances of receiver), respectively. Experimental results show that our method boosts the strong ELECTRA baseline substantially in four public benchmark datasets, and achieves various new state-of-the-art performance over previous methods. A series of ablation studies are conducted to demonstrate the effectiveness of our method.

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AAAI Conference 2021 Conference Paper

Semantics-Aware Inferential Network for Natural Language Understanding

Shuiliang Zhang
Hai Zhao
Junru Zhou
Xi Zhou
Xiang Zhou

For natural language understanding tasks, either machine reading comprehension or natural language inference, both semantics-aware and inference are favorable features of the concerned modeling for better understanding performance. Thus we propose a Semantics-Aware Inferential Network (SAIN) to meet such a motivation. Taking explicit contextualized semantics as a complementary input, the inferential module of SAIN enables a series of reasoning steps over semantic clues through an attention mechanism. By stringing these steps, the inferential network effectively learns to perform iterative reasoning which incorporates both explicit semantics and contextualized representations. In terms of well pre-trained language models as front-end encoder, our model achieves significant improvement on 11 tasks including machine reading comprehension and natural language inference.

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AAAI Conference 2020 Conference Paper

Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network

Jialin Gao
Zhixiang Shi
Guanshuo Wang
Jiani Li
Yufeng Yuan
Shiming Ge
Xi Zhou

Accurate temporal action proposals play an important role in detecting actions from untrimmed videos. The existing approaches have difﬁculties in capturing global contextual information and simultaneously localizing actions with different durations. To this end, we propose a Relation-aware pyramid Network (RapNet) to generate highly accurate temporal action proposals. In RapNet, a novel relation-aware module is introduced to exploit bi-directional long-range relations between local features for context distilling. This embedded module enhances the RapNet in terms of its multi-granularity temporal proposal generation ability, given predeﬁned anchor boxes. We further introduce a two-stage adjustment scheme to reﬁne the proposal boundaries and measure their conﬁdence in containing an action with snippet-level actionness. Extensive experiments on the challenging ActivityNet and THUMOS14 benchmarks demonstrate our RapNet generates superior accurate proposals over the existing state-of-the-art methods.

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AAAI Conference 2020 Conference Paper

DCMN+: Dual Co-Matching Network for Multi-Choice Reading Comprehension

Shuailiang Zhang
Hai Zhao
Yuwei Wu
Zhuosheng Zhang
Xi Zhou
Xiang Zhou

Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question. Previous approaches usually only calculate question-aware passage representation and ignore passage-aware question representation when modeling the relationship between passage and question, which cannot effectively capture the relationship between passage and question. In this work, we propose dual co-matching network (DCMN) which models the relationship among passage, question and answer options bidirectionally. Besides, inspired by how humans solve multi-choice questions, we integrate two reading strategies into our model: (i) passage sentence selection that ﬁnds the most salient supporting sentences to answer the question, (ii) answer option interaction that encodes the comparison information between answer options. DCMN equipped with the two strategies (DCMN+) obtains state-ofthe-art results on ﬁve multi-choice reading comprehension datasets from different domains: RACE, SemEval-2018 Task 11, ROCStories, COIN, MCTest.

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AAAI Conference 2020 Conference Paper

Semantics-Aware BERT for Language Understanding

Zhuosheng Zhang
Yuwei Wu
Hai Zhao
Zuchao Li
Shuailiang Zhang
Xi Zhou
Xiang Zhou

The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks. However, the existing language representation models including ELMo, GPT and BERT only exploit plain context-sensitive features such as character or word embeddings. They rarely consider incorporating structured semantic information which can provide rich semantics for language representation. To promote natural language understanding, we propose to incorporate explicit contextual semantics from pre-trained semantic role labeling, and introduce an improved language representation model, Semanticsaware BERT (SemBERT), which is capable of explicitly absorbing contextual semantics over a BERT backbone. Sem- BERT keeps the convenient usability of its BERT precursor in a light ﬁne-tuning way without substantial task-speciﬁc modi- ﬁcations. Compared with BERT, semantics-aware BERT is as simple in concept but more powerful. It obtains new state-ofthe-art or substantially improves results on ten reading comprehension and language inference tasks.

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AAAI Conference 2019 Conference Paper

Dependency or Span, End-to-End Uniform Semantic Role Labeling

Zuchao Li
Shexia He
Hai Zhao
Yiqing Zhang
Zhuosheng Zhang
Xi Zhou
Xiang Zhou

Semantic role labeling (SRL) aims to discover the predicateargument structure of a sentence. End-to-end SRL without syntactic input has received great attention. However, most of them focus on either span-based or dependency-based semantic representation form and only show specific model optimization respectively. Meanwhile, handling these two SRL tasks uniformly was less successful. This paper presents an end-to-end model for both dependency and span SRL with a unified argument representation to deal with two different types of argument annotations in a uniform fashion. Furthermore, we jointly predict all predicates and arguments, especially including long-term ignored predicate identification subtask. Our single model achieves new state-of-the-art results on both span (CoNLL 2005, 2012) and dependency (CoNLL 2008, 2009) SRL benchmarks.

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