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Yongming Huang

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3 papers

2 author rows

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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AAAI Conference 2025 Conference Paper

Fine-Grained Graph Representation Learning for Heterogeneous Mobile Networks with Attentive Fusion and Contrastive Learning

Shengheng Liu
Tianqi Zhang
Ningning Fu
Yongming Huang

AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent network self-driving holds the key, it heavily relies on expert experience and knowledge extracted from network data. In an effort to facilitate convenient analytics and utilization of wireless big data, we introduce the concept of knowledge graphs into the field of mobile networks, giving rise to what we term as wireless data knowledge graphs (WDKGs). However, the heterogeneous and dynamic nature of communication networks renders manual WDKG construction both prohibitively costly and error-prone, presenting a fundamental challenge. In this context, we propose an unsupervised data-and-model driven graph structure learning (DMGSL) framework, aimed at automating WDKG refinement and updating. Tackling WDKG heterogeneity involves stratifying the network into homogeneous layers and refining it at a finer granularity. Furthermore, to capture WDKG dynamics effectively, we segment the network into static snapshots based on the coherence time and harness the power of recurrent neural networks to incorporate historical information. Extensive experiments conducted on the established WDKG demonstrate the superiority of the DMGSL over the baselines, particularly in terms of node classification accuracy.

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AAAI Conference 2024 Conference Paper

Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB

Shengheng Liu
Xingkang Li
Zihuan Mao
Peng Liu
Yongming Huang

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. In this study, we address this challenge by reformulating the problem of angle-of-arrival (AoA) estimation into image reconstruction of spatial spectrum. To this end, we design a model-driven deep neural network (MoD-DNN), which can automatically calibrate the angular-dependent phase error. The proposed MoD-DNN approach employs an iterative optimization scheme between a convolutional neural network and a sparse conjugate gradient algorithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in enhancing spectrum calibration and AoA estimation.

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