3D lane detection has become a critical component in the perception task of autonomous vehicles. Unlike 2D lane detection, which operates in the image plane, 3D lane detection estimates the spatial layout of lanes in real-world coordinates, enabling fine-grained localization, map construction, and planning. However, the task remains challenging due to depth ambiguity, sensor limitations, and diverse road conditions. Existing surveys mostly focus on 2D or organize 3D lane detection by sensor modality, lacking a systematic treatment of algorithmic designs. In this paper, we present a comprehensive survey of deep learning-based 3D lane detection methods. We introduce a dual-axis taxonomy that jointly considers modeling paradigms and representation spaces. Based on this framework, we categorize existing methods into four primary paradigms: geometry-based, end-to-end, query-based, and implicit field-based. We analyze how each paradigm interacts with spatial representations such as image, BEV, 3D, and topological spaces. For each category, we review representative frameworks, architectural principles, and performance trade-offs. We also provide an extensive summary of public datasets, evaluation metrics, and state-of-the-art results across multiple benchmarks. Finally, we identify current limitations and outline future research directions toward robust, scalable, and interpretable 3D lane detection.

Consensus learning has been widely adopted in clustering tasks due to its robustness to noise and outliers, as well as its ability to aggregate diverse base results from multiple models. However, existing methods are often limited by feature alignment issues arising from heterogeneous feature dimensionalities and label permutation inconsistencies across models. To address these limitations, this paper introduces a novel Consensus Fuzzy Representation Learning (CFRL) framework. The CFRL framework initially employs various fuzzy clustering methods to generate diverse membership matrices, which are then transformed into affinity matrices to serve as base fuzzy representations. This transformation strategy not only effectively resolves feature alignment issues but also provides a unified processing mechanism for both single-view and multi-view data scenarios. To derive robust consensus features, the tensor Schatten$p$-norm encourages low-rank structure in the tensorized fuzzy representations, whereas an$l_{1}$-norm regularized error term captures and suppresses sparse noise. Moreover, a block diagonal regularizer is incorporated into the objective function, which guides the consensus feature matrix toward an optimal block diagonal structure. This structural constraint enhances cluster discriminability and enables reliable final cluster assignments. Comprehensive experimental evaluations validate that the proposed CFRL method achieves superior performance compared to state-of-the-art approaches.

Solutions to time-varying problems are crucial for research areas such as predicting changes in human body shape over time. While recurrent neural networks have made significant advancements in this field, their reliance on centralized processing has led to challenges such as model silos and data isolation. In response, distributed AI systems like federated learning have emerged to facilitate dynamic collaboration among models; however, they still depend on central coordinators, which pose risks to system security and efficiency. Moreover, traditional federated learning primarily supports homogeneous models and lacks effective strategies for the interaction of heterogeneous models. To address these limitations, we propose a novel method called Dynamic Collaboration of Heterogeneous Models (DCHM), based on Isomerism Learning, which leverages a consortium blockchain network to enhance model credibility and facilitate coordination among heterogeneous models. Additionally, we introduce a Distributed Hierarchical Aggregation (DHA) algorithm that enables permissioned nodes within each group to aggregate local model results and share them for standardized processing. After several iterative cycles, these nodes perform secondary integration of local results to produce global outcomes. Experimental results demonstrate that DCHM effectively analyzes the temporal variability of body shape changes with high efficiency.

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives.

With the rapid adoption of biometric authentication technologies, safeguarding users’ sensitive biometric data has become increasingly critical. Traditional centralized training methods pose significant risks due to exposure of raw biometric data. Federated learning offers a decentralized alternative but faces challenges from data and model heterogeneity, which can degrade performance and compromise privacy. To overcome these challenges, this work proposes Bio-IL, a decentralized and robust Biometric recognition system that integrates Isomerism Learning with heterogeneous models over a private blockchain infrastructure. The use of private blockchain ensures secure and tamper-resistant coordination of distributed training while safe-guarding data privacy. At the core of Bio-IL lies the novel IsoFus aggregation algorithm, an Isomerism Learning-based Fusion method that effectively combines heterogeneous local models and significantly improves recognition accuracy in realistic decentralized settings. Extensive experiments, with face recognition as a representative application, demonstrate that the proposed model outperforms baseline methods in both accuracy and robustness. This framework offers a scalable, secure, and adaptable solution for decentralized biometric authentication, making it well-suited for practical identity verification tasks.

Inertial navigation enables self-contained localization using only Inertial Measurement Units (IMUs), making it widely applicable in various domains such as navigation, augmented reality, and robotics. However, existing methods suffer from drift accumulation due to the sensor noise and difficulty capturing long-range temporal dependencies, limiting their robustness and accuracy. To address these challenges, we propose DiffusionIMU, a novel diffusion-based framework for inertial navigation. DiffusionIMU enhances direct velocity regression from IMU data through an iterative generative denoising process, progressively refining motion state estimation. It integrates the noise-adaptive feature modulation for sensor variability handling, the feature alignment mechanism for representation consistency, and the diffusion-based temporal modeling to decrease accumulated drift. Experiments show that DiffusionIMU consistently outperforms existing methods, demonstrating superior generalization to unseen users while alleviating the impact of the sensor noise.

The dynamic variations of food quality across spatial and temporal scales pose significant challenges for global food safety and nutrition research, requiring comprehensive analysis of diverse, multi-modal, and distributed data while preserving privacy. Existing centralized approaches suffer from data silos and limited collaboration, and although federated learning and blockchain technologies have shown promise independently, their combined potential for incentivized, privacy-preserving, and heterogeneous model collaboration remains underexplored. In this paper, we propose the concept of a Global Spatial-Temporal Food Memory-a novel research paradigm that envisions secure, decentralized, and incentivized collaboration among multiple stakeholders worldwide, leveraging blockchain-enabled token-based rewards integrated with federated learning of heterogeneous models. We discuss the scientific challenges and opportunities inherent in this vision, including multi-modal data fusion, trustworthy incentive mechanisms, and scalable long-term temporal analysis. This work aims to open new avenues in multimedia research by bridging decentralized AI, blockchain, and spatiotemporal food quality monitoring, providing a foundation for future explorations in privacy-preserving, collaborative, and large-scale multimedia data analysis.

The dissemination of public opinion in the social media network is driven by public sentiment, which can be used to promote the effective resolution of social incidents. However, public sentiments for incidents are often affected by environmental factors such as geography, politics, and ideology, which increases the complexity of the sentiment acquisition task. Therefore, a hierarchical mechanism is designed to reduce complexity and utilize processing at multiple phases to improve practicality. Through serial processing between different phases, the task of public sentiment acquisition can be decomposed into two subtasks, which are the classification of report text to locate incidents and sentiment analysis of individuals' reviews. Performance has been improved through improvements to the model structure, such as embedding tables and gating mechanisms. That being said, the traditional centralized structure model is not only easy to form model silos in the process of performing tasks but also faces security risks. In this article, a novel distributed deep learning model called isomerism learning based on blockchain is proposed to address these challenges, the trusted collaboration between models can be realized through parallel training. In addition, for the problem of text heterogeneity, we also designed a method to measure the objectivity of events to dynamically assign the weights of models to improve aggregation efficiency. Extensive experiments demonstrate that the proposed method can effectively improve performance and outperform the state-of-the-art methods significantly.