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Xiao Guo

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8 papers

2 author rows

EAAI Journal 2026 Journal Article

Long-term cooperative path planning for stratospheric airships based on hierarchical multi-agent reinforcement learning

Chao Lv
Ming Zhu
Xiao Guo
Jiajun Ou
Baojin Zheng
Liran Sun

Stratospheric airships are increasingly used for long-term collaborative tasks, requiring efficient path planning for multiple airships. Traditional methods struggle with collaborative optimization and state space explosion in such tasks. To address these issues, this paper presents a hierarchical cooperative airship path planning (HiCAPP). This HiCAPP employs a dual-layer control architecture, with the high-level controller responsible for task allocation and the low-level controller concentrating on path planning. Experimental results show that HiCAPP outperforms traditional multi-agent reinforcement learning methods in two critical metrics: average remaining energy and average distance to the task center. Additionally, through experiments with varying numbers of agents, task durations, and disturbances, HiCAPP has demonstrated robustness and scalability. These results confirm its effectiveness in long-term cooperative monitoring tasks and highlight the advantages of hierarchical decision-making in multi-agent systems.

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TMLR Journal 2025 Journal Article

Stochastic Block Model-Aware Topological Neural Networks for Graph Link Prediction

Yuzhou Chen
Xiao Guo
Shujie Ma

Link prediction is an important learning task for graph-structured data and is indispensable to understanding graphs' properties. Recent works focus on designing complicated graph neural networks (GNNs) architectures to explore and capture various pairwise interactions among graph nodes. Most GNNs are based on combining graph structural and node feature information by iterative message-passing schemes. However, despite GNNs revolutionizing the field of graph representation learning, some thorny questions are raised concerning whether GNNs can efficiently learn the edge probabilities based on topological structures (i.e., higher-order interactions) and node features, and provide statistically rigorous uncertainty estimates. In this paper, we tackle these challenges and propose a novel stochastic block model (SBM)-aware topological neural networks, called SBM-TNN, that uses SBMs to infer the latent community structure of nodes from graph structures and uses persistent homology to encode higher-order information. Furthermore, we theoretically study the entrywise bound and asymptotic normality of the estimated edge probability matrix to quantify the uncertainty in statistical inference of the edge probabilities. Our extensive experiments for link prediction on both graphs and knowledge graphs show that SBM-TNN achieves state-of-the-art performance over a set of popular baseline methods.

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NeurIPS Conference 2024 Conference Paper

On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detection

Xiufeng Song
Xiao Guo
Jiache Zhang
Qirui Li
Lei Bai
Xiaoming Liu
Guangtao Zhai
Xiaohong Liu

Large numbers of synthesized videos from diffusion models pose threats to information security and authenticity, leading to an increasing demand for generated content detection. However, existing video-level detection algorithms primarily focus on detecting facial forgeries and often fail to identify diffusion-generated content with a diverse range of semantics. To advance the field of video forensics, we propose an innovative algorithm named Multi-Modal Detection(MM-Det) for detecting diffusion-generated videos. MM-Det utilizes the profound perceptual and comprehensive abilities of Large Multi-modal Models (LMMs) by generating a Multi-Modal Forgery Representation (MMFR) from LMM's multi-modal space, enhancing its ability to detect unseen forgery content. Besides, MM-Det leverages an In-and-Across Frame Attention (IAFA) mechanism for feature augmentation in the spatio-temporal domain. A dynamic fusion strategy helps refine forgery representations for the fusion. Moreover, we construct a comprehensive diffusion video dataset, called Diffusion Video Forensics (DVF), across a wide range of forgery videos. MM-Det achieves state-of-the-art performance in DVF, demonstrating the effectiveness of our algorithm. Both source code and DVF are available at https: //github. com/SparkleXFantasy/MM-Det.

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NeurIPS Conference 2024 Conference Paper

Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network

Xiao Guo
Vishal Asnani
Sijia Liu
Xiaoming Liu

\textit{Model Parsing} defines the task of predicting hyperparameters of the generative model (GM), given a GM-generated image as the input. Since a diverse set of hyperparameters is jointly employed by the generative model, and dependencies often exist among them, it is crucial to learn these hyperparameter dependencies for improving the model parsing performance. To explore such important dependencies, we propose a novel model parsing method called Learnable Graph Pooling Network (LGPN), in which we formulate model parsing as a graph node classification problem, using graph nodes and edges to represent hyperparameters and their dependencies, respectively. Furthermore, LGPN incorporates a learnable pooling-unpooling mechanism tailored to model parsing, which adaptively learns hyperparameter dependencies of GMs used to generate the input image. Also, we introduce a Generation Trace Capturing Network (GTC) that can efficiently identify generation traces of input images, enhancing the understanding of generated images' provenances. Empirically, we achieve state-of-the-art performance in model parsing and its extended applications, showing the superiority of the proposed LGPN.

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ECAI Conference 2024 Conference Paper

Unidirectional Cross-Modal Fusion for RGB-T Tracking

Xiao Guo
Hangfei Li
Yufei Zha
Peng Zhang 0005

The key issue of RGB-T tracking is to obtain an effective multimodal representation of targets by utilizing complementary RGB and TIR modality information. Previous methods of template fusion or bidirectional search-template interaction potentially diminish the target representation, resulting from noise information of both templates and search regions. Meanwhile, the direct fusion of sole search features without interacting with templates cannot fully utilize target-relevant contextual information. To mitigate these issues, we present UCTrack, which fuses complementary multimodal search features conditioned on undisturbed RGB and TIR template features. Specifically, we design a Unidirectional Cross-modal Fusion (UCF) module to effectively minimize the influence of background noise on templates by pruning the unnecessary template-to-search cross-modal interaction and to mutually enhance RGB and TIR search features with target-relevant information through multimodal spatial fusion. Furthermore, this module is seamlessly integrated into different layers of a ViT backbone to facilitate feature extraction and cross-modal fusion for RGB-T tracking. Benefiting from the UCF module, UCTrack can effectively and accurately represent multimodal target features without unnecessary template-to-search interaction flow and direct template fusion, making the first proposal of unidirectional cross-modal fusion paradigm for RGB-T tracking to our best knowledge. Extensive experiments on three popular RGB-T tracking benchmarks demonstrate that our method achieves state-of-the-art performance.

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AAAI Conference 2023 Conference Paper

Bidirectional Optical Flow NeRF: High Accuracy and High Quality under Fewer Views

Shuo Chen
Binbin Yan
Xinzhu Sang
Duo Chen
Peng Wang
Xiao Guo
Chongli Zhong
Huaming Wan

Neural Radiance Fields (NeRF) can implicitly represent 3D-consistent RGB images and geometric by optimizing an underlying continuous volumetric scene function using a sparse set of input views, which has greatly benefited view synthesis tasks. However, NeRF fails to estimate correct geometry when given fewer views, resulting in failure to synthesize novel views. Existing works rely on introducing depth images or adding depth estimation networks to resolve the problem of poor synthetic view in NeRF with fewer views. However, due to the lack of spatial consistency of the single-depth image and the poor performance of depth estimation with fewer views, the existing methods still have challenges in addressing this problem. So this paper proposes Bidirectional Optical Flow NeRF(BOF-NeRF), which addresses this problem by mining optical flow information between 2D images. Our key insight is that utilizing 2D optical flow images to design a loss can effectively guide NeRF to learn the correct geometry and synthesize the right novel view. We also propose a view-enhanced fusion method based on geometry and color consistency to solve the problem of novel view details loss in NeRF. We conduct extensive experiments on the NeRF-LLFF and DTU MVS benchmarks for novel view synthesis tasks with fewer images in different complex real scenes. We further demonstrate the robustness of BOF-NeRF under different baseline distances on the Middlebury dataset. In all cases, BOF-NeRF outperforms current state-of-the-art baselines for novel view synthesis and scene geometry estimation.

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JMLR Journal 2023 Journal Article

Randomized Spectral Co-Clustering for Large-Scale Directed Networks

Xiao Guo
Yixuan Qiu
Hai Zhang
Xiangyu Chang

Directed networks are broadly used to represent asymmetric relationships among units. Co-clustering aims to cluster the senders and receivers of directed networks simultaneously. In particular, the well-known spectral clustering algorithm could be modified as the spectral co-clustering to co-cluster directed networks. However, large-scale networks pose great computational challenges to it. In this paper, we leverage sketching techniques and derive two randomized spectral co-clustering algorithms, one random-projection-based and the other random-sampling-based, to accelerate the co-clustering of large-scale directed networks. We theoretically analyze the resulting algorithms under two generative models – the stochastic co-block model and the degree-corrected stochastic co-block model, and establish their approximation error rates and misclustering error rates, indicating better bounds than the state-of-the-art results of co-clustering literature. Numerically, we design and conduct simulations to support our theoretical results and test the efficiency of the algorithms on real networks with up to millions of nodes. A publicly available R package RandClust is developed for better usability and reproducibility of the proposed methods. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

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AAAI Conference 2019 Conference Paper

Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies

Xiao Guo
Jongmoo Choi

Human motion prediction from motion capture data is a classical problem in the computer vision, and conventional methods take the holistic human body as input. These methods ignore the fact that, in various human activities, different body components (limbs and the torso) have distinctive characteristics in terms of the moving pattern. In this paper, we argue local representations on different body components should be learned separately and, based on such idea, propose a network, Skeleton Network (SkelNet), for long-term human motion prediction. Specifically, at each time-step, local structure representations of input (human body) are obtained via Skel- Net’s branches of component-specific layers, then the shared layer uses local spatial representations to predict the future human pose. Our SkelNet is the first to use local structure representations for predicting the human motion. Then, for short-term human motion prediction, we propose the second network, named as Skeleton Temporal Network (Skel-TNet). Skel-TNet consists of three components: SkelNet and a Recurrent Neural Network, they have advantages in learning spatial and temporal dependencies for predicting human motion, respectively; a feed-forward network that outputs the final estimation. Our methods achieve promising results on the Human3. 6M dataset and the CMU motion capture dataset, and the code is publicly available 1.

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