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Liming Huang

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

Achieving Equilibrium Under Utility Heterogeneity: An Agent-Attention Framework for Multi-Agent Multi-Objective Reinforcement Learning

Zhuhui Li
Chunbo Luo
Liming Huang
Luyu Qi
Geyong Min

Multi-agent multi-objective systems (MAMOS) have emerged as powerful frameworks for modelling complex decision-making problems across various real-world domains, such as robotic exploration, autonomous traffic management, and sensor network optimisation. MAMOS enhances scalability and robustness through decentralised control and more accurately captures inherent trade-offs between conflicting objectives. In MAMOS, each agent uses utility functions that map return vectors to scalar values. Existing MAMOS optimisation methods face significant challenges in handling heterogeneous objective and utility function settings, where training non-stationarity is intensified due to private utility functions and the associated policies. In this paper, we first theoretically prove that direct access to, or structured modeling of, global utility functions is necessary to achieve the Bayesian Nash Equilibrium under decentralised execution constraints. To access the global utility functions while preserving the decentralised execution, we propose an Agent-Attention Multi-Agent Multi-Objective Reinforcement Learning (AA-MAMORL) framework. Our approach implicitly learns a joint belief over other agents’ utility functions and their associated policies during centralised training, effectively mapping global states and utilities to each agent's policy. During execution, each agent independently selects actions based on local observations and its private utility function to approximate a BNE, without relying on inter-agent communication. We evaluate our framework through extensive experiments in a custom-designed MAMO Particle environment and the standard MOMALand benchmark. The results demonstrate that accessibility to global preferences and our proposed AA-MAMORL significantly improves performance and consistently outperforms state-of-the-art methods.

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EAAI Journal 2023 Journal Article

RGB-T image analysis technology and application: A survey

Kechen Song
Ying Zhao
Liming Huang
Yunhui Yan
Qinggang Meng

RGB-Thermal infrared (RGB-T) image analysis has been actively studied in recent years. In the past decade, it has received wide attention and made a lot of important research progress in many applications. This paper provides a comprehensive review of RGB-T image analysis technology and application, including several hot fields: image fusion, salient object detection, semantic segmentation, pedestrian detection, object tracking, and person re-identification. The first two belong to the preprocessing technology for many computer vision tasks, and the rest belong to the application direction. This paper extensively reviews 400+ papers spanning more than 10 different application tasks. Furthermore, for each specific task, this paper comprehensively analyzes the various methods and presents the performance of the state-of-the-art methods. This paper also makes an in-deep analysis of challenges for RGB-T image analysis as well as some potential technical improvements in the future.

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EAAI Journal 2023 Journal Article

Thermal images-aware guided early fusion network for cross-illumination RGB-T salient object detection

Han Wang
Kechen Song
Liming Huang
Hongwei Wen
Yunhui Yan

RGB-T salient object detection (SOD) has been developed rapidly and achieved excellent results in recent years. However, some problems have not yet been solved. The current RGB-T datasets contain only a tiny amount of low-illumination data. The RGB-T SOD method trained based on these RGB-T datasets does not detect the salient objects in extremely low-illumination scenes very well. To improve the detection performance of low-illumination data, we can spend a lot of labor to label low-illumination data, but we tried a new idea to solve the problem by making full use of the properties of Thermal (T) images. Therefore, we propose a T-aware guided early fusion network for cross-illumination salient object detection. Specifically, in the training and testing stage, we use normal illumination data to train our network and then use low and extremely low-illumination data to verify the effectiveness of our method. In the early fusion stage, we propose a T-aware guided module (T-aware) for enhancing salient regions of RGB images at different illumination levels. Secondly, in the decoding stage, we use T images to guide the cross-modal fusion of RGB and T images. In addition, we propose a cross-modal fusion localization-remote correction module (CFL-RCM), which is used to deeply screen and correct redundant information generated by illumination variations. Comparative experiments on the VDT-2048 dataset validate the superior performance of our method on the cross-illumination RGB-T saliency detection. We also obtained favorable results on generalizability experiments with VT5000, VT1000, and VT821 datasets.

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ICRA Conference 2015 Conference Paper

Discovery of topical object in image collections

Huaping Liu 0001
Yunhui Liu 0003
Liming Huang
Fuchun Sun 0001
Di Guo 0002

Automatic discovery of topical objects from a set of image collections provides more strong cognitive capability of robot to understand the unstructured environment. In this paper, we propose a novel framework based on dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method admits a great advantage that the interested objects can be more reliably segmented. A dictionary learning method is proposed to discover the topical objects. Such an optimization model exploits the observation that any image only includes a few topical objects and therefore sparsity is encouraged. Further, a globally convergent algorithm is developed to solve the dictionary learning problem and extensive experiments show that the proposed method outperforms the state-of-the-arts.

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