Author name cluster

Ziqi Wei

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AAMAS Conference 2026 Conference Paper

Rethinking Priority Scheduling for Sequential Multi-Agent Decision Making in Stackelberg Games

Xiangyu Liu
Liang Zhang
Bo Jin
Ziqi Wei

Current research applying N-level Stackelberg Game to multi-agent systems often uses the default decision order of agents provided by the environment. However, this raises the question: Does the order of agents necessarily affect the final equilibrium point of the game? To address this, we formally analyze the N-level Stackelberg Game in which changing the order where the agents make decisions typically leads to an overdetermined system. As a result, the equilibrium point is shifted unless special structural conditions are met. Based on this, we propose the Hierarchical Priority Adjustment (HPA) method, which adjusts and selects the agents’ decision order. For the upper level, an upper policy dynamically selects the optimal decision order of agents based on the current game state; for the lower level, agents execute the strategy in the Spatio-Temporal Sequential Markov Game (STMG) based on the selectedorder. Tocoordinatelearningacrosstimescales, weemploy a slow-fast update scheme with shared intrinsic rewards derived from the upper policy advantage function. Experimental results on high-precision control tasks such as multi-agent MuJoCo show that HPA outperforms the benchmark algorithms and robustly adapts to changing environments. These results highlight the crucial role of optimizing the decision order of agents in N-level Stackelberg Game.

PDF

IJCAI Conference 2024 Conference Paper

Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition

Yang Wang
Haiyang Mei
Qirui Bao
Ziqi Wei
Mike Zheng Shou
Haizhou Li
Bo Dong
Xin Yang

We introduce a novel multimodality synergistic knowledge distillation scheme tailored for efficient single-eye motion recognition tasks. This method allows a lightweight, unimodal student spiking neural network (SNN) to extract rich knowledge from an event-frame multimodal teacher network. The core strength of this approach is its ability to utilize the ample, coarser temporal cues found in conventional frames for effective emotion recognition. Consequently, our method adeptly interprets both temporal and spatial information from the conventional frame domain, eliminating the need for specialized sensing devices, e. g. , event-based camera. The effectiveness of our approach is thoroughly demonstrated using both existing and our compiled single-eye emotion recognition datasets, achieving unparalleled performance in accuracy and efficiency over existing state-of-the-art methods.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Exploiting Polarized Material Cues for Robust Car Detection

Wen Dong
Haiyang Mei
Ziqi Wei
Ao Jin
Sen Qiu
Qiang Zhang
Xin Yang

Car detection is an important task that serves as a crucial prerequisite for many automated driving functions. The large variations in lighting/weather conditions and vehicle densities of the scenes pose significant challenges to existing car detection algorithms to meet the highly accurate perception demand for safety, due to the unstable/limited color information, which impedes the extraction of meaningful/discriminative features of cars. In this work, we present a novel learning-based car detection method that leverages trichromatic linear polarization as an additional cue to disambiguate such challenging cases. A key observation is that polarization, characteristic of the light wave, can robustly describe intrinsic physical properties of the scene objects in various imaging conditions and is strongly linked to the nature of materials for cars (e.g., metal and glass) and their surrounding environment (e.g., soil and trees), thereby providing reliable and discriminative features for robust car detection in challenging scenes. To exploit polarization cues, we first construct a pixel-aligned RGB-Polarization car detection dataset, which we subsequently employ to train a novel multimodal fusion network. Our car detection network dynamically integrates RGB and polarization features in a request-and-complement manner and can explore the intrinsic material properties of cars across all learning samples. We extensively validate our method and demonstrate that it outperforms state-of-the-art detection methods. Experimental results show that polarization is a powerful cue for car detection. Our code is available at https://github.com/wind1117/AAAI24-PCDNet.

PDF Details DOI