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Wei Xie

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7 papers

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EAAI Journal 2026 Journal Article

You only look once meets asymmetric pronged convolution: A unified framework for efficient fire and smoke detection

Shibo Dong
Ying Guo
Zhe Wu
Guokang Xie
Wei Xie
Yonghao Gu
Weifeng Xu

Fire detection remains a critical challenge in global public safety. Traditional sensor-based methods often suffer from delayed response and high false alarms, especially in early fire stages. To address these limitations, we propose several novel enhancements to the You Only Look Once (YOLO) object detection models. First, we introduce the Asymmetric pronged Convolution (APConv) module—a flexible plug-in that can replace standard convolution layers, improving multi-scale feature extraction and fine-grained recognition. Second, we design a Cross-stage Attention Feature Fusion (C2f-AFF) module along with a lightweight, memory-efficient self-attention detection head (LSADetect). Integrating these components into YOLO models (version 8–12) results in significant performance improvements, with mean Average Precision at 50% Intersection over Union threshold (mAP@50) gains ranging from 2. 6% to 4. 9%. Additionally, we propose the Efficient Cross-phase Network (ECN) module to further enhance feature fusion and computational efficiency. To enable robust evaluation, a new multi-source high-quality fire and smoke dataset Finaldata is constructed, which addresses common issues in existing open-source datasets like image duplication and watermarking. Experimental results on Finaldata show that our method improves mAP@50 by 4. 7% and 4. 5% in mean Average Precision at Intersection over Union thresholds from 0. 50 to 0. 95 (mAP@50–95) compared to the baseline, while achieving an impressive inference speed of 174 Frames Per Second (FPS). Moreover, we conducted additional experiments on two other public datasets. These results highlight the effectiveness of our approach in significantly enhancing detection accuracy and adaptability in complex fire and smoke environments. Our codes and dataset are publicly available at https: //github. com/123dsb-ux/Finaldata. git.

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AAAI Conference 2025 Conference Paper

NaviFormer: A Spatio-Temporal Context-Aware Transformer for Object Navigation

Wei Xie
Haobo Jiang
Yun Zhu
Jianjun Qian
Jin Xie

Learning discriminative state representations of agents, encompassing the spatial layout and temporal pose trajectory, is essential for effective navigation decisions. However, existing approaches often rely on simplistic plain networks for navigation information fusion, overlooking the complex long-range dependencies across spatio-temporal cues, which leads to suboptimal state perception and potential decision failures. In this paper, we introduce NaviFormer, an effective encoder-decoder navigation transformer, to aggregate discriminative spatio-temporal context information for object navigation. Our navigation encoder not only encodes spatial layouts and temporal agent poses but also innovatively constructs and encodes a passable frontier map, enriching the original state encoding with cues of potential exploration regions. Furthermore, our navigation decoder employs spatio-temporal self-attention and cross-attention mechanisms to model the dependencies among spatial layout encoding, temporal pose encoding, and passable frontier encoding, thereby facilitating comprehensive contextual state feature aggregation. Finally, we leverage these learned spatio-temporal contextual state representations for PPO-based navigation decisions. Extensive experiments on the Gibson, Habitat-Matterport3D (HM3D) and Matterport3D (MP3D) datasets demonstrate the superiority of our approach.

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NeurIPS Conference 2025 Conference Paper

PC-Net: Weakly Supervised Compositional Moment Retrieval via Proposal-Centric Network

Mingyao Zhou
Hao Sun
Wei Xie
Ming Dong
Chengji Wang
Mang Ye

With the exponential growth of video content, aiming at localizing relevant video moments based on natural language queries, video moment retrieval (VMR) has gained significant attention. Existing weakly supervised VMR methods focus on designing various feature modeling and modal interaction modules to alleviate the reliance on precise temporal annotations. However, these methods have poor generalization capabilities on compositional queries with novel syntactic structures or vocabulary in real-world scenarios. To this end, we propose a new task: weakly supervised compositional moment retrieval (WSCMR). This task trains models using only video-query pairs without precise temporal annotations, while enabling generalization to complex compositional queries. Furthermore, a proposal-centric network (PC-Net) is proposed to tackle this challenging task. First, video and query features are extracted through frozen feature extractors, followed by modality interaction to obtain multimodal features. Second, to handle compositional queries with explicit temporal associations, a dual-granularity proposal generator decodes multimodal global and frame-level features to obtain query-relevant proposal boundaries with fine-grained temporal perception. Third, to improve the discrimination of proposal features, a proposal feature aggregator is constructed to conduct semantic alignment of frames and queries, and employ a learnable peak-aware Gaussian distributor to fit the frame weights within the proposals to derive proposal features from the video frame features. Finally, the proposal quality is assessed based on the results of reconstructing the masked query using the obtained proposal features. To further enhance the model's ability to capture semantic associations between proposals and queries, a quality margin regularizer is constructed to dynamically stratify proposals into high and low query-relevance subsets and enhance the association between queries and common elements within proposals, and suppress spurious correlations via inter-subset contrastive learning. Notably, PC-Net achieves superior performance with 54\% fewer parameters than prior works by parameter-efficient design. Experiments on Charades-CG and ActivityNet-CG demonstrate PC-Net’s ability to generalize across diverse compositional queries. Code is available at https: //github. com/mingyao1120/PC-Net.

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NeurIPS Conference 2025 Conference Paper

Purest Quantum State Identification

Yingqi Yu
Honglin Chen
Jun Wu
Wei Xie
Xiangyang Li

Quantum noise constitutes a fundamental obstacle to realizing practical quantum technologies. To address the pivotal challenge of identifying quantum systems least affected by noise, we introduce the purest quantum state identification, which can be used to improve the accuracy of quantum computation and communication. We formulate a rigorous paradigm for identifying the purest quantum state among $K$ unknown $n$-qubit quantum states using total $N$ quantum state copies. For incoherent strategies, we derive the first adaptive algorithm achieving error probability $\exp\left(- \Omega\left(\frac{N H_1}{\log(K) 2^n }\right) \right)$, fundamentally improving quantum property learning through measurement optimization. By developing a coherent measurement protocol with error bound $\exp\left(- \Omega\left(\frac{N H_2}{\log(K) }\right) \right)$, we demonstrate a significant separation from incoherent strategies, formally quantifying the power of quantum memory and coherent measurement. Furthermore, we establish a lower bound by demonstrating that all strategies with fixed two-outcome incoherent POVM must suffer error probability exceeding $ \exp\left( - O\left(\frac{NH_1}{2^n}\right)\right)$. This research advances the characterization of quantum noise through efficient learning frameworks. Our results establish theoretical foundations for noise-adaptive quantum property learning while delivering practical protocols for enhancing the reliability of quantum hardware.

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NeurIPS Conference 2025 Conference Paper

Revisiting Generative Infrared and Visible Image Fusion Based on Human Cognitive Laws

Lin Guo
Xiaoqing Luo
Wei Xie
Zhancheng Zhang
Hui Li
Rui Wang
Zhenhua Feng
Xiaoning Song

Existing infrared and visible image fusion methods often face the dilemma of balancing modal information. Generative fusion methods reconstruct fused images by learning from data distributions, but their generative capabilities remain limited. Moreover, the lack of interpretability in modal information selection further affects the reliability and consistency of fusion results in complex scenarios. This manuscript revisits the essence of generative image fusion under the inspiration of human cognitive laws and proposes a novel infrared and visible image fusion method, termed HCLFuse. First, HCLFuse investigates the quantification theory of information mapping in unsupervised fusion networks, which leads to the design of a multi-scale mask-regulated variational bottleneck encoder. This encoder applies posterior probability modeling and information decomposition to extract accurate and concise low-level modal information, thereby supporting the generation of high-fidelity structural details. Furthermore, the probabilistic generative capability of the diffusion model is integrated with physical laws, forming a time-varying physical guidance mechanism that adaptively regulates the generation process at different stages, thereby enhancing the ability of the model to perceive the intrinsic structure of data and reducing dependence on data quality. Experimental results show that the proposed method achieves state-of-the-art fusion performance in qualitative and quantitative evaluations across multiple datasets and significantly improves semantic segmentation metrics. This fully demonstrates the advantages of this generative image fusion method, drawing inspiration from human cognition, in enhancing structural consistency and detail quality.

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IJCAI Conference 2024 Conference Paper

Bandits with Concave Aggregated Reward

Yingqi Yu
Sijia Zhang
Shaoang Li
Lan Zhang
Wei Xie
Xiang-Yang Li

Multi-armed bandit is a simple but powerful algorithmic framework, and many effective algorithms have been proposed for various online models. In numerous applications, the decision-maker faces diminishing marginal utility. With non-linear aggregations, those algorithms often have poor regret bounds. Motivated by this, we study a bandit problem with diminishing marginal utility, which we termed the bandits with concave aggregated reward(BCAR). To tackle this problem, we propose two algorithms SW-BCAR and SWUCB-BCAR. Through theoretical analysis, we establish the effectiveness of these algorithms in addressing the BCAR issue. Extensive simulations demonstrate that our algorithms achieve better results than the most advanced bandit algorithms.

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AAAI Conference 2024 Conference Paper

TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detection

Hao Sun
Mingyao Zhou
Wenjing Chen
Wei Xie

Video moment retrieval (MR) and highlight detection (HD) based on natural language queries are two highly related tasks, which aim to obtain relevant moments within videos and highlight scores of each video clip. Recently, several methods have been devoted to building DETR-based networks to solve both MR and HD jointly. These methods simply add two separate task heads after multi-modal feature extraction and feature interaction, achieving good performance. Nevertheless, these approaches underutilize the reciprocal relationship between two tasks. In this paper, we propose a task-reciprocal transformer based on DETR (TR-DETR) that focuses on exploring the inherent reciprocity between MR and HD. Specifically, a local-global multi-modal alignment module is first built to align features from diverse modalities into a shared latent space. Subsequently, a visual feature refinement is designed to eliminate query-irrelevant information from visual features for modal interaction. Finally, a task cooperation module is constructed to refine the retrieval pipeline and the highlight score prediction process by utilizing the reciprocity between MR and HD. Comprehensive experiments on QVHighlights, Charades-STA and TVSum datasets demonstrate that TR-DETR outperforms existing state-of-the-art methods. Codes are available at https://github.com/mingyao1120/TR-DETR.

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