Author name cluster

Meng Shen

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

GRIP: Latent Field-Guided Graph Policy for Budget-Constrained Multi-Agent Routing

Yujiao Hu
Zuyu Chen
MengJie Lee
Jinchao Chen
Meng Shen
Hailun Zhang
Wei Li
Yan Pan

Subset selection under budget constraints is critical in applications like multi-robot patrolling, crime deterrence, and targeted marketing, where multiple agents must jointly select targets and plan feasible routes. We formalize this challenge as Multi-Subset Selection with Budget-Constrained Routing (MSS-BCR), involving complex, non-additive cost structures that defy traditional methods. We propose GRIP, a graph-based framework integrating spatial reward fields and policy learning to enable coordinated, budget-aware target selection and routing. GRIP uses attention-based embeddings and constraint-triggered pruning with utility recovery to produce high-quality, feasible solutions. Experiments based on multiple synthetic and real-world datasets show GRIP outperforms baselines in reward efficiency and scalability across varied scenarios.

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AAAI Conference 2026 Conference Paper

Hierarchical Schedule Optimization for Fast and Robust Diffusion Model Sampling

Aihua Zhu
Rui Su
Qinglin Zhao
Li Feng
Meng Shen
Shibo He

Diffusion probabilistic models have set a new standard for generative fidelity but are hindered by a slow iterative sampling process. A powerful training-free strategy to accelerate this process is Schedule Optimization, which aims to find an optimal distribution of timesteps for a fixed and small Number of Function Evaluations (NFE) to maximize sample quality. To this end, a successful schedule optimization method must adhere to four core principles: effectiveness, adaptivity, practical robustness, and computational efficiency. However, existing paradigms struggle to satisfy these principles simultaneously, motivating the need for a more advanced solution. To overcome these limitations, we propose the Hierarchical-Schedule-Optimizer (HSO), a novel and efficient bi-level optimization framework. HSO reframes the search for a globally optimal schedule into a more tractable problem by iteratively alternating between two synergistic levels: an upper-level global search for an optimal initialization strategy and a lower-level local optimization for schedule refinement. This process is guided by two key innovations: the Midpoint Error Proxy (MEP), a solver-agnostic and numerically stable objective for effective local optimization, and the Spacing-Penalized Fitness (SPF) function, which ensures practical robustness by penalizing pathologically close timesteps. Extensive experiments show that HSO sets a new state-of-the-art for training-free sampling in the extremely low-NFE regime. For instance, with an NFE of just 5, HSO achieves a remarkable FID of 11.94 on LAION-Aesthetics with Stable Diffusion v2.1. Crucially, this level of performance is attained not through costly retraining, but with a one-time optimization cost of less than 8 seconds, presenting a highly practical and efficient paradigm for diffusion model acceleration.

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ICRA Conference 2025 Conference Paper

Dynamic Compact Consensus Tracking for Aerial Robots

Xiaolou Sun
Zhibin Quan
Feng Zhang
Yuntian Li
Chunyan Wang
Wufei Si
Wenhui Ni
Runwei Guan

Existing one-stream trackers have attracted widespread attention. However, they are not applicable in real-time aerial robot tracking systems due to substantial computational overhead, especially when dynamic templates are introduced. To address this issue, we propose a novel Dynamic Compact Consensus Tracker (DC 2 T), constructed by stacking blocks that each consists of a Compact Token Encoder (CTE) and Dynamic Consensus Attention (DCA). Unlike traditional methods that convert images into a large number of tokens, the CTE, inspired by “superpixel”, extracts a compact set of representative tokens from both initial and dynamic templates, eliminating the need for a large token set. This strategic reduction in the number of compact tokens markedly decreases the computational load of CTE, enhancing the efficiency of subsequent attention operations. To achieve linear complexity of the DCA, compact dynamic template tokens (as keys) are requeried by search tokens (as queries) to perform dynamic consensus on the aggregated tokens (as values). This arrangement seamlessly incorporates dynamic spatio-temporal features into the DCA while avoiding the computational burden typically associated with dynamic templates. With the aim of further enhancing the system's responsiveness and accuracy, a direct control network is crafted to seamlessly incorporate the prediction of high-level control values into the tracking network, ensuring a cohesive and efficient interaction with the controller. Comprehensive experiments and real-world evaluations have proven DC 2 T's superior performance, accompanied by a significant reduction in FLOPs. Furthermore, we have conducted experiments that demonstrate the tracker's ability to integrate seamlessly with other technologies such as SLAM and detection, enabling precise tracking of arbitrary objects. The tracker code will be released in the github.com/xiaolousun/refine-pytracking.

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IROS Conference 2024 Conference Paper

CLAT: Convolutional Local Attention Tracker for Real-time UAV Target Tracking System with Feedback Information

Xiaolou Sun
Zhibin Quan
Wei Wang
Wufei Si
Chunyan Wang
Yuntian Li
Yuan Wu
Meng Shen

Real-time UAV vision target tracking systems encounter the intricate challenges of striking a trade-off for tracking speed and performance, and the robustness of the following control. In existing tracking systems, the global attention mechanism enhances tracking performance, but it introduces higher computational complexity, impacting target tracking speed; the local attention mechanism can reduce computational complexity but often exhibits limitations in modeling the receptive field. In this paper, we propose a new framework named Convolutional Local Attention Tracker (CLAT) to address these challenges. Firstly, we design a hierarchical convolutional local attention structure as the feature extractor for CLAT. This leverages convolutional projection before local window partitioning, facilitating connections between non-overlapping windows and expanding the receptive field. Secondly, we introduce a streamlined feature fusion network comprising the unshared-weights convolutional layer and a global attention network. The whole design can balance speed and accuracy. Furthermore, to enhance servo control robustness, we have redesigned the upper-level controller by integrating all bounding box information. To capture feedback spatiotemporal information in CLAT, a dynamic template update is implemented by incorporating an IOU head into the predictor. Extensive experiments on visual tracking benchmarks and in the real world demonstrate that CLAT achieves competitive performance. Moreover, we have developed a comprehensive tracking system demonstration capable of precisely tracking targets across various categories. The tracker code will be released on https://github.com/xiaolousun/refine-pytracking.git.

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AAAI Conference 2024 Conference Paper

MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation

Yanzuo Lu
Meng Shen
Andy J Ma
Xiaohua Xie
Jian-Huang Lai

Universal domain adaptation (UniDA) is a practical but challenging problem, in which information about the relation between the source and the target domains is not given for knowledge transfer. Existing UniDA methods may suffer from the problems of overlooking intra-domain variations in the target domain and difficulty in separating between the similar known and unknown class. To address these issues, we propose a novel Mutual Learning Network (MLNet) with neighborhood invariance for UniDA. In our method, confidence-guided invariant feature learning with self-adaptive neighbor selection is designed to reduce the intra-domain variations for more generalizable feature representation. By using the cross-domain mixup scheme for better unknown-class identification, the proposed method compensates for the misidentified known-class errors by mutual learning between the closed-set and open-set classifiers. Extensive experiments on three publicly available benchmarks demonstrate that our method achieves the best results compared to the state-of-the-arts in most cases and significantly outperforms the baseline across all the four settings in UniDA. Code is available at https://github.com/YanzuoLu/MLNet.

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EAAI Journal 2023 Journal Article

TCGNN: Packet-grained network traffic classification via Graph Neural Networks

Guangwu Hu
Xi Xiao
Meng Shen
Bin Zhang
Xia Yan
Yunxia Liu

Network traffic classification is the fundamental and vital function for network management, network security and so on. With the traffic scenarios becoming more and more complex, current commonly used practices, e. g. , port-based and payload-based classification methods, can hardly work. Even though the new emerging resorts, i. e. , machine learning or deep learning methods, have increased classification accuracy, the performance is still under improvement. To improve the classification accuracy and performance, we propose a novel Graph Neural Network (GNN) based Traffic Classification proposal named TCGNN considering the insight of observing packets from a graph aspect. TCGNN first transforms each network packet into an undirected graph. Then it adopts a two-layer graph convolutional network with three different aggregation strategies so as to learn the latent application representation from the packet-transformed graph. Finally, relying on GNN’s powerful ability in learning graph representation, TCGNN can identify unknown network packets with an extremely high accuracy rate. Extensive experiments on two real-world traffic classification datasets demonstrate the superior effectiveness of TCGNN over the existing packet-grained traffic classification methods.

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AAAI Conference 2021 Conference Paper

Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction

Xiangyun Tang
Dongliang Liao
Weijie Huang
Jin Xu
Liehuang Zhu
Meng Shen

Real-time forwarding prediction for predicting online contents’ popularity is beneficial to various social applications for enhancing interactive social behaviors. Cascade graphs, formed by online contents’ propagation, play a vital role in real-time forwarding prediction. Existing cascade graph modeling methods are inadequate to embed cascade graphs that have hub structures and deep cascade paths, or they fail to handle the short-term outbreak of forwarding amount. To this end, we propose a novel real-time forwarding prediction method that includes an effective approach for cascade graph embedding and a short-term variation sensitive method for time-series modeling, making the best of cascade graph features. Using two real world datasets, we demonstrate the significant superiority of the proposed method compared with the state-of-the-art. Our experiments also reveal interesting implications hidden in the performance differences between cascade graph embedding and time-series modeling.

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