Jian Shi

EAAI Journal 2026 Journal Article

A deep learning detection framework for gas pipeline leaks suitable for low-power edge devices

• Jian Shi
• Yuanquan Xu
• Pin Lyu

EAAI Journal 2026 Journal Article

A novel framework for wave forecasting based on deep learning: A case study in the Gulf of Aden

• Feng Luo
• Yifan Qin
• Jian Shi
• Zhipeng Chen
• Yongzhi Wang
• Aifeng Tao
• Jinhai Zheng
• Lin Lv

AAAI Conference 2026 Conference Paper

Exploring Reliable Spatiotemporal Dependencies for Efficient Visual Tracking

• Junze Shi
• Yang Yu
• Jian Shi
• Haibo Luo

Recent advances in transformer-based lightweight object tracking have established new standards across benchmarks, leveraging the global receptive field and powerful feature extraction capabilities of attention mechanisms. Despite these achievements, existing methods universally employ sparse sampling during training—utilizing only one template and one search image per sequence—which fails to comprehensively explore spatiotemporal information in videos. This limitation constrains performance and causes the gap between lightweight and high-performance trackers. To bridge this divide while maintaining real-time efficiency, we propose STDTrack, a framework that pioneers the integration of reliable spatiotemporal dependencies into lightweight trackers. Our approach implements dense video sampling to maximize spatiotemporal information utilization. We introduce a temporally propagating spatiotemporal token to guide per-frame feature extraction. To ensure comprehensive target state representation, we design the Multi-frame Information Fusion Module (MFIFM), which augments current dependencies using historical context. The MFIFM operates on features stored in our constructed Spatiotemporal Token Maintainer (STM), where a quality-based update mechanism ensures information reliability. Considering the scale variation among tracking targets, we develop a multi-scale prediction head to dynamically adapt to objects of different sizes. Extensive experiments demonstrate state-of-the-art results across six benchmarks. Notably, on GOT-10k, STDTrack rivals certain high-performance non-real-time trackers (e.g., MixFormer) while operating at 192 FPS (GPU) and 41 FPS (CPU).

EAAI Journal 2026 Journal Article

Improved self-attention multi-model fusion for load forecasting in regional integrated energy systems

• Jian Shi
• Jiashen Teh
• Bader Alharbi

AAAI Conference 2025 Conference Paper

HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation

• Wentian Qu
• Jiahe Li
• Jian Cheng
• Jian Shi
• Chenyu Meng
• Cuixia Ma
• Hongan Wang
• Xiaoming Deng

Understanding of bimanual hand-object interaction plays an important role in robotics and virtual reality. However, due to significant occlusions between hands and object as well as the high degree-of-freedom motions, it is challenging to collect and annotate a high-quality, large-scale dataset, which prevents further improvement of bimanual hand-object interaction-related baselines. In this work, we propose a new 3D Gaussian Splatting based data augmentation framework for bimanual hand-object interaction, which is capable of augmenting existing dataset to large-scale photorealistic data with various hand-object pose and viewpoints. First, we use mesh-based 3DGS to model objects and hands, and to deal with the rendering blur problem due to multi-resolution input images used, we design a super-resolution module. Second, we extend the single hand grasping pose optimization module for the bimanual hand object to generate various poses of bimanual hand-object interaction, which can significantly expand the pose distribution of the dataset. Third, we conduct an analysis for the impact of different aspects of the proposed data augmentation on the understanding of the bimanual hand-object interaction. We perform our data augmentation on two benchmarks, H2O and Arctic, and verify that our method can improve the performance of the baselines.

NeurIPS Conference 2025 Conference Paper

Preference-Based Dynamic Ranking Structure Recognition

• Nan Lu
• Jian Shi
• Xinyu Tian

Preference-based data often appear complex and noisy but may conceal underlying homogeneous structures. This paper introduces a novel framework of ranking structure recognition for preference-based data. We first develop an approach to identify dynamic ranking groups by incorporating temporal penalties into a spectral estimation for the celebrated Bradley-Terry model. To detect structural changes, we introduce an innovative objective function and present a practicable algorithm based on dynamic programming. Theoretically, we establish the consistency of ranking group recognition by exploiting properties of a random 'design matrix' induced by a reversible Markov chain. We also tailor a group inverse technique to quantify the uncertainty in item ability estimates. Additionally, we prove the consistency of structure change recognition, ensuring the robustness of the proposed framework. Experiments on both synthetic and real-world datasets demonstrate the practical utility and interpretability of our approach.

JBHI Journal 2024 Journal Article

Isolated Random Forest Assisted Spatio-Temporal Ant Colony Evolutionary Algorithm for Cell Tracking in Time-Lapse Sequences

• Benlian Xu
• Di Wu
• Jian Shi
• Jinliang Cong
• Mingli Lu
• Feng Yang
• Brett Nener

Multi-Object tracking in real world environments is a tough problem, especially for cell morphogenesis with division. Most cell tracking methods are hard to achieve reliable mitosis detection, efficient inter-frame matching, and accurate state estimation simultaneously within a unified tracking framework. In this paper, we propose a novel unified framework that leverages a spatio-temporal ant colony evolutionary algorithm to track cells amidst mitosis under measurement uncertainty. Each Bernoulli ant colony representing a migrating cell is able to capture the occurrence of mitosis through the proposed Isolation Random Forest (IRF)-assisted temporal mitosis detection algorithm with the assumption that mitotic cells exhibit unique spatio-temporal features different from non-mitotic ones. Guided by prediction of a division event, multiple ant colonies evolve between consecutive frames according to an augmented assignment matrix solved by the extended Hungarian method. To handle dense cell populations, an efficient group partition between cells and measurements is exploited, which enables multiple assignment tasks to be executed in parallel with a reduction in matrix dimension. After inter-frame traversing, the ant colony transitions to a foraging stage in which it begins approximating the Bernoulli parameter to estimate cell state by iteratively updating its pheromone field. Experiments on multi-cell tracking in the presence of cell mitosis and morphological changes are conducted, and the results demonstrate that the proposed method outperforms state-of-the-art approaches, striking a balance between accuracy and computational efficiency.

JBHI Journal 2021 Journal Article

A Joint Tracking Approach via Ant Colony Evolution for Quantitative Cell Cycle Analysis

• Benlian Xu
• Mingli Lu
• Jian Shi
• Jinliang Cong
• Brett Nener

In this paper, we use an ant colony heuristic method to tackle the integration of data association and state estimation in the presence of cell mitosis, morphological change and uncertainty of measurement. Our approach first models the scouting behavior of an unlabeled ant colony as a chaotic process to generate a set of cell candidates in the current frame, then a labeled ant colony foraging process is modeled to construct an interframe matching between previously estimated cell states and current cell candidates through minimizing the optimal sub-pattern assignment metric for track (OSPA-T). The states of cells in the current frame are finally estimated using labeled ant colonies via a multi-Bernoulli parameter set approximated by individual food pheromone fields and heuristic information within the same region of support, the resulting trail pheromone fields over frames constitutes the cell lineage trees of the tracks. A four-stage track recovery strategy is proposed to monitor the history of all established tracks to reconstruct broken tracks in a computationally economic way. The labeling method used in this work is an improvement on previous techniques. The method has been evaluated on publicly available, challenging cell image sequences, and a satisfied performance improvement is achieved in contrast to the state-of-the-art methods.

IROS Conference 2015 Conference Paper

Dynamic in-hand sliding manipulation

• Jian Shi
• James Zachary Woodruff
• Kevin M. Lynch

This paper presents a framework for planning the motion of an n-fingered robot hand to create an inertial load on a grasped object to achieve a desired in-grasp sliding motion. The model of the sliding dynamics is based on a soft-finger limit surface contact model at each fingertip. The framework is applied to the problem of regrasping a block held in a pinch grasp. The approach is applied to two examples in simulation, one of which is tested experimentally.

IROS Conference 2009 Conference Paper

Motion controller for the Atomic Force Microscopy based nanomanipulation system

• Ruiguo Yang
• Ning Xi 0001
• King Wai Chiu Lai
• Bingtuan Gao
• Hongzhi Chen
• Chanmin Su
• Jian Shi

Nanomanipulation with Atomic force microscopy (AFM) is one of the fundamental tools for nano-manufacturing. The control of the nanomanipulation system requires accurate feedback from the piezoelectric actuator and high frequency response of the control system. We designed and implemented two distinct control schemes by using real-time Linux. The aim is to study various factors in the control of the AFM based nanomanipulation system. By integrating the original controller with the external Linux real-time controller, we achieved a stable system with high response frequency. Finally this multiple input single output (MISO) system is validated to be an effective and efficient tool for the controlling of the nanolithography operation through a haptic device.