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Xian Sun

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8 papers

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AAAI Conference 2025 Conference Paper

HyperMixer: Specializable Hypergraph Channel Mixing for Long-term Multivariate Time Series Forecasting

Changyuan Tian
Zhicong Lu
Zequn Zhang
Heming Yang
Wei Cao
Zhi Guo
Xian Sun
Li Jin

Long-term Multivariate Time Series (LMTS) forecasting aims to predict extended future trends based on channel-interrelated historical data. Considering the elusive channel correlations, most existing methods compromise by treating channels as independent or tentatively modeling pairwise channel interactions, making it challenging to handle the characteristics of both higher-order interactions and time variation in channel correlations. In this paper, we propose HyperMixer, a novel specializable hypergraph channel mixing plugin which introduces versatile hypergraph structures to capture group channel interactions and time-varying patterns for long-term multivariate time series forecasting. Specifically, to encode the higher-order channel interactions, we structure multiple channels into a hypergraph, achieving a two-phase message-passing mechanism: channel-to-group and group-to-channel. Moreover, the functionally specializable hypergraph structures are presented to boost the capability of hypergraph to capture the time-varying patterns across periods, further refining modeling of channel correlations. Extensive experimental results on seven available benchmark datasets demonstrate the effectiveness and generalization of our plugin in LMTS forecasting. The visual analysis further illustrates that HyperMixer with specializable hypergraphs tailors channel interactions specific to certain periods.

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AAAI Conference 2025 Conference Paper

SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing

Chen Chen
Liangjin Zhao
Yuanchun He
Yingxuan Long
Kaiqiang Chen
Zhirui Wang
Yanfeng Hu
Xian Sun

Semantic segmentation and 3D reconstruction are two fundamental tasks in remote sensing, typically treated as separate or loosely coupled tasks. Despite attempts to integrate them into a unified network, the constraints between the two heterogeneous tasks are not explicitly modeled, since the pioneering studies either utilize a loosely coupled parallel structure or engage in only implicit interactions, failing to capture the inherent connections. In this work, we explore the connections between the two tasks and propose a new network that imposes semantic constraints on the stereo matching task, both implicitly and explicitly. Implicitly, we transform the traditional parallel structure to a new cascade structure termed Semantic-Guided Cascade structure, where the deep features enriched with semantic information are utilized for the computation of initial disparity maps, enhancing semantic guidance. Explicitly, we propose a Semantic Selective Refinement (SSR) module and a Left-Right Semantic Consistency (LRSC) module. The SSR refines the initial disparity map under the guidance of the semantic map. The LRSC ensures semantic consistency between two views via reducing the semantic divergence after transforming the semantic map from one view to the other using the disparity map. Experiments on the US3D and WHU datasets demonstrate that our method achieves state-of-the-art performance for both semantic segmentation and stereo matching.

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NeurIPS Conference 2024 Conference Paper

Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond

Zhechao Wang
Peirui Cheng
Mingxin Chen
Pengju Tian
Zhirui Wang
Xinming Li
Xue Yang
Xian Sun

Collaborative trajectory prediction can comprehensively forecast the future motion of objects through multi-view complementary information. However, it encounters two main challenges in multi-drone collaboration settings. The expansive aerial observations make it difficult to generate precise Bird's Eye View (BEV) representations. Besides, excessive interactions can not meet real-time prediction requirements within the constrained drone-based communication bandwidth. To address these problems, we propose a novel framework named "Drones Help Drones" (DHD). Firstly, we incorporate the ground priors provided by the drone's inclined observation to estimate the distance between objects and drones, leading to more precise BEV generation. Secondly, we design a selective mechanism based on the local feature discrepancy to prioritize the critical information contributing to prediction tasks during inter-drone interactions. Additionally, we create the first dataset for multi-drone collaborative prediction, named "Air-Co-Pred", and conduct quantitative and qualitative experiments to validate the effectiveness of our DHD framework. The results demonstrate that compared to state-of-the-art approaches, DHD reduces position deviation in BEV representations by over 20\% and requires only a quarter of the transmission ratio for interactions while achieving comparable prediction performance. Moreover, DHD also shows promising generalization to the collaborative 3D object detection in CoPerception-UAVs.

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AAAI Conference 2023 Conference Paper

Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection

Xiaonan Lu
Wenhui Diao
Yongqiang Mao
Junxi Li
Peijin Wang
Xian Sun
Kun Fu

Few-shot object detection, expecting detectors to detect novel classes with a few instances, has made conspicuous progress. However, the prototypes extracted by existing meta-learning based methods still suffer from insufficient representative information and lack awareness of query images, which cannot be adaptively tailored to different query images. Firstly, only the support images are involved for extracting prototypes, resulting in scarce perceptual information of query images. Secondly, all pixels of all support images are treated equally when aggregating features into prototype vectors, thus the salient objects are overwhelmed by the cluttered background. In this paper, we propose an Information-Coupled Prototype Elaboration (ICPE) method to generate specific and representative prototypes for each query image. Concretely, a conditional information coupling module is introduced to couple information from the query branch to the support branch, strengthening the query-perceptual information in support features. Besides, we design a prototype dynamic aggregation module that dynamically adjusts intra-image and inter-image aggregation weights to highlight the salient information useful for detecting query images. Experimental results on both Pascal VOC and MS COCO demonstrate that our method achieves state-of-the-art performance in almost all settings. Code will be available at: https://github.com/lxn96/ICPE.

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AAAI Conference 2023 Conference Paper

TOT：Topology-Aware Optimal Transport for Multimodal Hate Detection

Linhao Zhang
Li Jin
Xian Sun
Guangluan Xu
Zequn Zhang
Xiaoyu Li
Nayu Liu
Qing Liu

Multimodal hate detection, which aims to identify the harmful content online such as memes, is crucial for building a wholesome internet environment. Previous work has made enlightening exploration in detecting explicit hate remarks. However, most of their approaches neglect the analysis of implicit harm, which is particularly challenging as explicit text markers and demographic visual cues are often twisted or missing. The leveraged cross-modal attention mechanisms also suffer from the distributional modality gap and lack logical interpretability. To address these semantic gap issues, we propose TOT: a topology-aware optimal transport framework to decipher the implicit harm in memes scenario, which formulates the cross-modal aligning problem as solutions for optimal transportation plans. Specifically, we leverage an optimal transport kernel method to capture complementary information from multiple modalities. The kernel embedding provides a non-linear transformation ability to reproduce a kernel Hilbert space (RKHS), which reflects significance for eliminating the distributional modality gap. Moreover, we perceive the topology information based on aligned representations to conduct bipartite graph path reasoning. The newly achieved state-of-the-art performance on two publicly available benchmark datasets, together with further visual analysis, demonstrate the superiority of TOT in capturing implicit cross-modal alignment.

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IJCAI Conference 2022 Conference Paper

Hypertron: Explicit Social-Temporal Hypergraph Framework for Multi-Agent Forecasting

Yu Tian
Xingliang Huang
Ruigang Niu
Hongfeng Yu
Peijin Wang
Xian Sun

Forecasting the future trajectories of multiple agents is a core technology for human-robot interaction systems. To predict multi-agent trajectories more accurately, it is inevitable that models need to improve interpretability and reduce redundancy. However, many methods adopt implicit weight calculation or black-box networks to learn the semantic interaction of agents, which obviously lack enough interpretation. In addition, most of the existing works model the relation among all agents in a one-to-one manner, which might lead to irrational trajectory predictions due to its redundancy and noise. To address the above issues, we present Hypertron, a human-understandable and lightweight hypergraph-based multi-agent forecasting framework, to explicitly estimate the motions of multiple agents and generate reasonable trajectories. The framework explicitly interacts among multiple agents and learns their latent intentions by our coarse-to-fine hypergraph convolution interaction module. Our experiments on several challenging real-world trajectory forecasting datasets show that Hypertron outperforms a wide array of state-of-the-art methods while saving over 60% parameters and reducing 30% inference time.

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AAAI Conference 2022 Conference Paper

PolygonE: Modeling N-ary Relational Data as Gyro-Polygons in Hyperbolic Space

Shiyao Yan
Zequn Zhang
Xian Sun
Guangluan Xu
Shuchao Li
Qing Liu
Nayu Liu
Shensi Wang

N-ary relational knowledge base (KBs) embedding aims to map binary and beyond-binary facts into low-dimensional vector space simultaneously. Existing approaches typically decompose n-ary relational facts into subtuples, and they generally model n-ary relational KBs in Euclidean space. However, n-ary relational facts are semantically and structurally intact; decomposition undermines the semantical and structural integrity. Moreover, compared to the binary relational KBs, n-ary ones are characterized by more abundant and complicated hierarchy structures, which could not be well expressed in Euclidean space. To address the issues, we propose a gyro-polygon embedding framework to realize n-ary fact integrity keeping and hierarchy capturing, termed PolygonE. Specifically, n-ary relational facts are modeled as gyropolygons in the hyperbolic space, where we denote entities in facts as vertexes of gyro-polygons and relations as entity translocation operations. Importantly, we design a fact plausibility measuring strategy based on the vertex-gyrocentroid geodesic to optimize the relation-adjusted gyro-polygon. Experimental results demonstrate that PolygonE shows SOTA performance on all benchmark datasets and generalizes well on binary data. Finally, we also visualize the embedding to help comprehend PolygonE’s awareness of hierarchies.

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AAAI Conference 2020 Conference Paper

FAS-Net: Construct Effective Features Adaptively for Multi-Scale Object Detection

Jiangqiao Yan
Yue Zhang
Zhonghan Chang
Tengfei Zhang
Menglong Yan
Wenhui Diao
Hongqi Wang
Xian Sun

Feature pyramid is the mainstream method for multi-scale object detection. In most detectors with feature pyramid, each proposal is predicted based on feature grids pooled from only one feature level, which is assigned heuristically. Recent studies report that the feature representation extracted using this method is sub-optimal, since they ignore the valid information exists on other unselected layers of the feature pyramid. To address this issue, researchers present to fuse valid information across all feature levels. However, these methods can be further improved: the feature fusion strategies, which use common operation (element-wise max or sum) in most detectors, should be replaced by a more ﬂexible way. In this work, a novel method called feature adaptive selection subnetwork (FAS-Net) is proposed to construct effective features for detecting objects of different scales. Particularly, its adaption consists of two level: global attention and local adaptive selection. First, we model the global context of each feature map with global attention based feature selection module (GAFSM), which can strengthen the effective features across each layer adaptively. Then we extract the features of each region of interest (RoI) on the entire feature pyramid to construct a RoI feature pyramid. Finally, the RoI feature pyramid is sent to the feature adaptive selection module (FASM) to integrate the strengthened features according to the input adaptively. Our FAS-Net can be easily extended to other two-stage object detectors with feature pyramid, and supports to analyze the importance of different feature levels for multi-scale objects quantitatively. Besides, FAS-Net can also be further applied to instance segmentation task and get consistent improvements. Experiments on PASCAL07/12 and MSCOCO17 demonstrate the effectiveness and generalization of the proposed method.

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