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Mingmin Chi

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6 papers
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6

AAAI Conference 2025 Conference Paper

MM-Tracker: Motion Mamba for UAV-platform Multiple Object Tracking

  • Mufeng Yao
  • Jinlong Peng
  • Qingdong He
  • Bo Peng
  • Hao Chen
  • Mingmin Chi
  • Chao Liu
  • Jon Atli Benediktsson

Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) platforms requires efficient motion modeling. This is because UAV-MOT faces both local object motion and global camera motion. Motion blur also increases the difficulty of detecting large moving objects. Previous UAV motion modeling approaches either focus only on local motion or ignore motion blurring effects, thus limiting their tracking performance and speed. To address these issues, we propose the Motion Mamba Module, which explores both local and global motion features through cross-correlation and bi-directional Mamba Modules for better motion modeling. To address the detection difficulties caused by motion blur, we also design motion margin loss to effectively improve the detection accuracy of motion blurred objects. Based on the Motion Mamba module and motion margin loss, our proposed MM-Tracker surpasses the state-of-the-art in two widely open-source UAV-MOT datasets.

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AAAI Conference 2024 Conference Paper

Solving Spectrum Unmixing as a Multi-Task Bayesian Inverse Problem with Latent Factors for Endmember Variability

  • Dong Wu
  • Mingmin Chi
  • Xuan Zang
  • Bo Peng

With the increasing customization of spectrometers, spectral unmixing has become a widely used technique in fields such as remote sensing, textiles, and environmental protection. However, endmember variability is a common issue for unmixing, where changes in lighting, atmospheric, temporal conditions, or the intrinsic spectral characteristics of materials, can all result in variations in the measured spectrum. Recent studies have employed deep neural networks to tackle endmember variability. However, these approaches rely on generic networks to implicitly resolve the issue, which struggles with the ill-posed nature and lack of effective convergence constraints for endmember variability. This paper proposes a streamlined multi-task learning model to rectify this problem, incorporating abundance regression and multi-label classification with Unmixing as a Bayesian Inverse Problem, denoted as BIPU. To address the issue of the ill-posed nature, the uncertainty of unmixing is quantified and minimized through the Laplace approximation in a Bayesian inverse solver. In addition, to improve convergence under the influence of endmember variability, the paper introduces two types of constraints. The first separates background factors of variants from the initial factors for each endmember, while the second identifies and eliminates the influence of non-existent endmembers via multi-label classification during convergence. The effectiveness of this model is demonstrated not only on a self-collected near-infrared spectral textile dataset (FENIR), but also on three commonly used remote sensing hyperspectral image datasets, where it achieves state-of-the-art unmixing performance and exhibits strong generalization capabilities.

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IJCAI Conference 2023 Conference Paper

Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection

  • Supeng Wang
  • Yuxi Li
  • Ming Xie
  • Mingmin Chi
  • Yabiao Wang
  • Chengjie Wang
  • Wenbing Zhu

Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes, previous effort mostly pays attention to learning representative feature descriptors of a single image, while the difference information is either modeled with simple difference operations or implicitly embedded via feature interactions. Nevertheless, such difference modeling can be noisy since it suffers from non-semantic changes and lacks explicit guidance from image content or context. In this paper, we revisit the importance of feature difference for change detection in RSI, and propose a series of operations to fully exploit the difference information: Alignment, Perturbation and Decoupling (APD). Firstly, alignment leverages contextual similarity to compensate for the non-semantic difference in feature space. Next, a difference module trained with semantic-wise perturbation is adopted to learn more generalized change estimators, which reversely bootstraps feature extraction and prediction. Finally, a decoupled dual-decoder structure is designed to predict semantic changes in both content-aware and content-agnostic manners. Extensive experiments are conducted on benchmarks of LEVIR-CD, WHU-CD and DSIFN-CD, demonstrating our proposed operations bring significant improvement and achieve competitive results under similar comparative conditions. Code is available at https: //github. com/wangsp1999/CD-Research/tree/main/openAPD

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ECAI Conference 2008 Conference Paper

MTForest: Ensemble Decision Trees based on Multi-Task Learning

  • Qing Wang
  • Liang Zhang 0019
  • Mingmin Chi
  • Jiankui Guo

Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noise-free data while some of them are better than others on noisy data. But in reality, ensemble methods that can consistently gain good performance in situations with or without noise are more desirable. In this paper, we propose a new method namely MTForest, to ensemble decision tree learning algorihms by enumerating each input attribute as extra task to introduce different additional inductive bias to generate diverse yet accurate component decision tree learning algorithms in the ensemble. The experimental results show that in situations without classification noise, MTForest is comparable to Boosting and Random Forest and significantly better than Bagging, while in situations with classification noise, MTForest is significantly better than Boosting and Random Forest and is slightly better than Bagging. So MTForest is a good choice for ensemble decision tree learning algorithms in situations with or without noise. We conduct the experiments on the basis of 36 widely used UCI data sets that cover a wide range of domains and data characteristics and run all the algorithms within the Weka platform.

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