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Mingyu Fan

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9 papers

2 author rows

ICML Conference 2025 Conference Paper

Three-Dimensional Trajectory Prediction with 3DMoTraj Dataset

Hao Zhou 0014
Xu Yang 0004
Mingyu Fan
Lu Qi 0001
Xiangtai Li
Ming-Hsuan Yang 0001
Fei Luo

With the growing interest in embodied and spatial intelligence, accurately predicting trajectories in 3D environments has become increasingly critical. However, no datasets have been explicitly designed to study 3D trajectory prediction. To this end, we contribute a 3D motion trajectory (3DMoTraj) dataset collected from unmanned underwater vehicles (UUVs) operating in oceanic environments. Mathematically, trajectory prediction becomes significantly more complex when transitioning from 2D to 3D. To tackle this challenge, we analyze the prediction complexity of 3D trajectories and propose a new method consisting of two key components: decoupled trajectory prediction and correlated trajectory refinement. The former decouples inter-axis correlations, thereby reducing prediction complexity and generating coarse predictions. The latter refines the coarse predictions by modeling their inter-axis correlations. Extensive experiments show that our method significantly improves 3D trajectory prediction accuracy and outperforms state-of-the-art methods. Both the 3DMoTraj dataset and the method are available at https: //github. com/zhouhao94/3DMoTraj.

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TIST Journal 2021 Journal Article

Simultaneous Past and Current Social Interaction-aware Trajectory Prediction for Multiple Intelligent Agents in Dynamic Scenes

Yanliang Zhu
Dongchun Ren
Yi Xu
Deheng Qian
Mingyu Fan
Xin Li
Huaxia Xia

Trajectory prediction of multiple agents in a crowded scene is an essential component in many applications, including intelligent monitoring, autonomous robotics, and self-driving cars. Accurate agent trajectory prediction remains a significant challenge because of the complex dynamic interactions among the agents and between them and the surrounding scene. To address the challenge, we propose a decoupled attention-based spatial-temporal modeling strategy in the proposed trajectory prediction method. The past and current interactions among agents are dynamically and adaptively summarized by two separate attention-based networks and have proven powerful in improving the prediction accuracy. Moreover, it is optional in the proposed method to make use of the road map and the plan of the ego-agent for scene-compliant and accurate predictions. The road map feature is efficiently extracted by a convolutional neural network, and the features of the ego-agent’s plan is extracted by a gated recurrent network with an attention module based on the temporal characteristic. Experiments on benchmark trajectory prediction datasets demonstrate that the proposed method is effective when the ego-agent plan and the the surrounding scene information are provided and achieves state-of-the-art performance with only the observed trajectories.

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ICRA Conference 2021 Conference Paper

Star Topology based Interaction for Robust Trajectory Forecasting in Dynamic Scene

Yanliang Zhu
Dongchun Ren
Deheng Qian
Mingyu Fan
Xin Li
Huaxia Xia

Motion prediction of multiple agents in a dynamic scene is a crucial component in many real applications, including intelligent monitoring and autonomous driving. Due to the complex interactions among the agents and their interactions with the surrounding scene, accurate trajectory prediction is still a great challenge. In this paper, we propose a new method for robust trajectory prediction of multiple intelligent agents in a dynamic scene. The input of the method includes the observed trajectories of all agents, and optionally, the planning of the ego-agent and the surrounding high definition map at every time steps. Given observed trajectories, an efficient approach in a star computational topology is utilized to compute both the spatiotemporal interaction features and the current interaction features between the agents, where the time complexity scales linearly to the number of agents. Moreover, on an autonomous vehicle, the proposed prediction method can make use of the planning of ego-agent to improve the modeling of the interaction between surrounding agents. To increase the robustness to upstream perception noises, at the training stage, we randomly mask out the input data, a. k. a. the points on the observed trajectories of agents and the lane sequence. Experiments on autonomous driving and pedestrian-walking datasets demonstrate that the proposed method is not only effective when the planning of ego-agent and the high definition map are provided, but also achieves state-of-the-art performance with only the observed trajectories.

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AAAI Conference 2017 Conference Paper

Structure Regularized Unsupervised Discriminant Feature Analysis

Mingyu Fan
Xiaojun Chang
Dacheng Tao

Feature selection is an important technique in machine learning research. An effective and robust feature selection method is desired to simultaneously identify the informative features and eliminate the noisy ones of data. In this paper, we consider the unsupervised feature selection problem which is particularly difﬁcult as there is not any class labels that would guide the search for relevant features. To solve this, we propose a novel algorithmic framework which performs unsupervised feature selection. Firstly, the proposed framework implements structure learning, where the data structures (including intrinsic distribution structure and the data segment) are found via a combination of the alternative optimization and clustering. Then, both the intrinsic data structure and data segmentation are formulated as regularization terms for discriminant feature selection. The results of the feature selection also affect the structure learning step in the following iterations. By leveraging the interactions between structure learning and feature selection, we are able to capture more accurate structure of data and select more informative features. Clustering and classiﬁcation experiments on real world image data sets demonstrate the effectiveness of our method.

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IJCAI Conference 2017 Conference Paper

Top-k Supervise Feature Selection via ADMM for Integer Programming

Mingyu Fan
Xiaojun Chang
Xiaoqin Zhang
Di Wang
Liang Du

Recently, structured sparsity inducing based feature selection has become a hot topic in machine learning and pattern recognition. Most of the sparsity inducing feature selection methods are designed to rank all features by certain criterion and then select the k top ranked features, where k is an integer. However, the k top features are usually not the top k features and therefore maybe a suboptimal result. In this paper, we propose a novel supervised feature selection method to directly identify the top k features. The new method is formulated as a classic regularized least squares regression model with two groups of variables. The problem with respect to one group of the variables turn out to be a 0-1 integer programming, which had been considered very hard to solve. To address this, we utilize an efficient optimization method to solve the integer programming, which first replaces the discrete 0-1 constraints with two continuous constraints and then utilizes the alternating direction method of multipliers to optimize the equivalent problem. The obtained result is the top subset with k features under the proposed criterion rather than the subset of k top features. Experiments have been conducted on benchmark data sets to show the effectiveness of proposed method.

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AAAI Conference 2016 Conference Paper

Semi-Supervised Dictionary Learning via Structural Sparse Preserving

Di Wang
Xiaoqin Zhang
Mingyu Fan
Xiuzi Ye

While recent techniques for discriminative dictionary learning have attained promising results on the classiﬁcation tasks, their performance is highly dependent on the number of labeled samples available for training. However, labeling samples is expensive and time consuming due to the signiﬁcant human effort involved. In this paper, we present a novel semi-supervised dictionary learning method which utilizes the structural sparse relationships between the labeled and unlabeled samples. Speciﬁcally, by connecting the sparse reconstruction coefﬁcients on both the original samples and dictionary, the unlabeled samples can be automatically grouped to the different labeled samples, and the grouped samples share a small number of atoms in the dictionary via mixed 2, pnorm regularization. This makes the learned dictionary more representative and discriminative since the shared atoms are learned by using the labeled and unlabeled samples potentially from the same class. Minimizing the derived objective function is a challenging task because it is non-convex and highly non-smooth. We propose an efﬁcient optimization algorithm to solve the problem based on the block coordinate descent method. Moreover, we have a rigorous proof of the convergence of the algorithm. Extensive experiments are presented to show the superior performance of our method in classiﬁcation applications.

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IJCAI Conference 2015 Conference Paper

An Efficient Classifier Based on Hierarchical Mixing Linear Support Vector Machines

Di Wang
Xiaoqin Zhang
Mingyu Fan
Xiuzi Ye

Support vector machines (SVMs) play a very dominant role in data classification due to their good generalization performance. However, they suffer from the high computational complexity in the classification phase when there are a considerable number of support vectors (SVs). Then it is desirable to design efficient algorithms in the classification phase to deal with the datasets of realtime pattern recognition systems. To this end, we propose a novel classifier called HMLSVMs (Hierarchical Mixing Linear Support Vector Machines) in this paper, which has a hierarchical structure with a mixing linear SVMs classifier at each node and predicts the label of a sample using only a few hyperplanes. We also give a generalization error bound for the class of locally linear SVMs (LLSVMs) based on the Rademacher theory, which ensures that overfitting can be effectively avoided. Experimental evaluations shows, while maintaining a comparable classification performance to kernel SVMs (KSVMs), the proposed classifier achieves the high efficiency in the classification stage.

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IJCAI Conference 2015 Conference Paper

Multi-Modality Tracker Aggregation: From Generative to Discriminative

Xiaoqin Zhang
Wei Li
Mingyu Fan
Di Wang
Xiuzi Ye

Visual tracking is an important research topic in computer vision community. Although there are numerous tracking algorithms in the literature, no one performs better than the others under all circumstances, and the best algorithm for a particular dataset may not be known a priori. This motivates a fundamental problem-the necessity of an ensemble learning of different tracking algorithms to overcome their drawbacks and to increase the generalization ability. This paper proposes a multimodality ranking aggregation framework for fusion of multiple tracking algorithms. In our work, each tracker is viewed as a ‘ranker’ which outputs a rank list of the candidate image patches based on its own appearance model in a particular modality. Then the proposed algorithm aggregates the rankings of different rankers to produce a joint ranking. Moreover, the level of expertise for each ‘ranker’ based on the historical ranking results is also effectively used in our model. The proposed model not only provides a general framework for fusing multiple tracking algorithms on multiple modalities, but also provides a natural way to combine the advantages of the generative model based trackers and the the discriminative model based trackers. It does not need to directly compare the output results obtained by different trackers, and such a comparison is usually heuristic. Extensive experiments demonstrate the effectiveness of our work.

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IJCAI Conference 2015 Conference Paper

Robust Multiple Kernel K-means Using L21-Norm

Liang Du
Peng Zhou
Lei Shi
Hanmo Wang
Mingyu Fan
Wenjian Wang
Yi-Dong Shen

The k-means algorithm is one of the most often used method for data clustering. However, the standard k-means can only be applied in the original feature space. The kernel k-means, which extends k-means into the kernel space, can be used to capture the non-linear structure and identify arbitrarily shaped clusters. Since both the standard k-means and kernel k-means apply the squared error to measure the distances between data points and cluster centers, a few outliers will cause large errors and dominate the objection function. Besides, the performance of kernel method is largely determined by the choice of kernel. Unfortunately, the most suitable kernel for a particular task is often unknown in advance. In this paper, we first present a robust kmeans using `2, 1-norm in the feature space and then extend it to the kernel space. To recap the powerfulness of kernel methods, we further propose a novel robust multiple kernel k-means (RMKKM) algorithm that simultaneously finds the best clustering label, the cluster membership and the optimal combination of multiple kernels. An alternating iterative schema is developed to find the optimal value. Extensive experiments well demonstrate the effectiveness of the proposed algorithms.

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