Author name cluster

Long Qian

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12 papers

2 author rows

AAAI Conference 2026 Conference Paper

Quality-Aware Language-Conditioned Local Auto-Regressive Anomaly Synthesis and Detection

Long Qian
Bingke Zhu
Yingying Chen
Ming Tang
Jinqiao Wang

Despite substantial progress in anomaly synthesis, existing diffusion-based and coarse inpainting pipelines commonly suffer from structural deficiencies such as micro-structural discontinuities, limited semantic controllability, and inefficient generation. To overcome these limitations, we introduce ARAS, a language-conditioned, auto-regressive anomaly synthesis approach that precisely injects local, text-specified defects into normal images via token-anchored latent editing. Leveraging a hard-gated auto-regressive operator and a training-free, context-preserving masked sampling kernel, ARAS significantly enhances defect realism, preserves fine-grained material textures, and provides continuous semantic control over synthesized anomalies. Integrated within our Quality-Aware Re-weighted Anomaly Detection (QARAD) framework, we propose a dynamic weighting strategy that emphasizes high-quality synthetic samples by computing an image-text similarity score with a dual-encoder model. Extensive experiments across three datasets, MVTec AD, VisA, and BTAD, demonstrate that our QARAD outperforms SOTA methods in both image- and pixel-level anomaly detection tasks, achieving improved accuracy, robustness, and a 5× synthesis speedup compared to diffusion-based alternatives.

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I&C Journal 2026 Journal Article

The geometry of computable Banach spaces

Rod Downey
Noam Greenberg
Long Qian
Ruofei Xie

We investigate the complexity of a computable Banach space having a Schauder basis, and of related properties, such as the approximation property, and having a local basis structure.

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EAAI Journal 2025 Journal Article

A reliable enhanced learning algorithm for ship detection in inland water maritime surveillance system

Yuanzhou Zheng
Long Qian
Jingxin Cao
Haichao Huang
Wenbo Hou
Yeyu Liu

Accurate ship detection is crucial for intelligent maritime surveillance systems. Given the complicated navigation environment of the inland waterway and the low accuracy and efficiency of model detection, this work introduces an enhanced detection algorithm. Specifically, the learning and detection capabilities of the model are enhanced by optimizing the network structure, introducing an attention mechanism, and designing a non-maximal value suppression method based on bounding box loss. In addition, based on the physical principle of imaging in severe weather conditions, a data enhancement strategy that simulates low-quality images is proposed to enrich the dataset further and train the detection model using a learning approach, which can enable the algorithm to exhibit strong detection performance in severe weather conditions, such as rainy, hazy, and low-light. As demonstrated in the experiments, the enhanced learning algorithm achieves optimal detection performance in several state-of-the-art comparison algorithms, showcasing the effectiveness and superiority of the proposed algorithm.

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YNIMG Journal 2025 Journal Article

Alterations in brain iron and myelination in children with ASD: A susceptibility source separation imaging study

Xiang Zhou
Ya‑Yin Deng
Long Qian
Shuang‑Shuang Zhong
Feng‑Yun Zou
Li‑Shan Shen
Xiao‑Wen Luo
Bo-Ya Yin

Autism spectrum disorder (ASD) may have both brain iron and myelin changes, but traditional methods fail to differentiate them. This study utilized an advanced susceptibility source separation technique, APART-QSM (iterAtive magnetic suscePtibility sources sepARaTion), to investigate brain iron and myelination alterations in children with ASD and link neuroimaging findings to clinical symptom severity. Sixty-five school-aged children with ASD and Sixty age- and sex-matched typically developing children were included. By providing enhanced and broader detection capabilities compared to conventional QSM, APART-QSM uncovered reduced iron content across multiple deep gray matters and decreased myelin content in the globus pallidum in ASD. The iron and myelin contents in the globus pallidum and iron content in the substantia nigra were significantly negatively correlated with ASD symptom severity. Coexisting abnormal brain iron and myelin contents in ASD, particularly in the globus pallidus, offer innovative and promising insights into ASD pathology and potential biomarkers.

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ICLR Conference 2025 Conference Paper

Bootstrapped Model Predictive Control

Yuhang Wang
Hanwei Guo
Sizhe Wang
Long Qian
Xuguang Lan

Model Predictive Control (MPC) has been demonstrated to be effective in continuous control tasks. When a world model and a value function are available, planning a sequence of actions ahead of time leads to a better policy. Existing methods typically obtain the value function and the corresponding policy in a model-free manner. However, we find that such an approach struggles with complex tasks, resulting in poor policy learning and inaccurate value estimation. To address this problem, we leverage the strengths of MPC itself. In this work, we introduce Bootstrapped Model Predictive Control (BMPC), a novel algorithm that performs policy learning in a bootstrapped manner. BMPC learns a network policy by imitating an MPC expert, and in turn, uses this policy to guide the MPC process. Combined with model-based TD-learning, our policy learning yields better value estimation and further boosts the efficiency of MPC. We also introduce a lazy reanalyze mechanism, which enables computationally efficient imitation learning. Our method achieves superior performance over prior works on diverse continuous control tasks. In particular, on challenging high-dimensional locomotion tasks, BMPC significantly improves data efficiency while also enhancing asymptotic performance and training stability, with comparable training time and smaller network sizes. Code is available at https://github.com/wertyuilife2/bmpc.

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AAMAS Conference 2025 Conference Paper

Offline Multi-Agent Preference-based Reinforcement Learning with Agent-aware Direct Preference Optimization

Qian Kou
Mingyang Li
Zeyang Liu
Long Qian
Zhuoran Chen
Lipeng Wan
Xingyu Chen
Xuguang Lan

Multi-agent Preference-Based Reinforcement Learning (MAPbRL) is promising in offline policy learning by leveraging human preferences to replace complex manual reward designing. Current MAPbRL methods use complicated structures to realize better reward modeling with off-the-shelf MARL algorithms and obtain the joint policy based on it. However, it faces a severe preference-behavior mismatch problem stemming from the instability of RL training and global-local preference inconsistency datasets in offline MARL, resulting in potential suboptimal policy convergence. To address this problem, we propose Agent-aware Multi-Agent Direct Preference Optimization (AMADPO) by utilizing a multi-agent preference predictor to guide agent-aware direct optimization from imbalanced preference labels, which can learn coordination policy from both positive and negative segments. Experimental results in SMAC environment show substantial improvements in global-local preference inconsistency datasets, demonstrating the effectiveness of AMADPO in solving the preference-behavior mismatch problem.

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NeurIPS Conference 2024 Conference Paper

Grounded Answers for Multi-agent Decision-making Problem through Generative World Model

Zeyang Liu
Xinrui Yang
Shiguang Sun
Long Qian
Lipeng Wan
Xingyu Chen
Xuguang Lan

Recent progress in generative models has stimulated significant innovations in many fields, such as image generation and chatbots. Despite their success, these models often produce sketchy and misleading solutions for complex multi-agent decision-making problems because they miss the trial-and-error experience and reasoning as humans. To address this limitation, we explore a paradigm that integrates a language-guided simulator into the multi-agent reinforcement learning pipeline to enhance the generated answer. The simulator is a world model that separately learns dynamics and reward, where the dynamics model comprises an image tokenizer as well as a causal transformer to generate interaction transitions autoregressively, and the reward model is a bidirectional transformer learned by maximizing the likelihood of trajectories in the expert demonstrations under language guidance. Given an image of the current state and the task description, we use the world model to train the joint policy and produce the image sequence as the answer by running the converged policy on the dynamics model. The empirical results demonstrate that this framework can improve the answers for multi-agent decision-making problems by showing superior performance on the training and unseen tasks of the StarCraft Multi-Agent Challenge benchmark. In particular, it can generate consistent interaction sequences and explainable reward functions at interaction states, opening the path for training generative models of the future.

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ICML Conference 2024 Conference Paper

Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning

Long Qian
Juncheng Li 0006
Yu Wu 0011
Yaobo Ye
Hao Fei 0001
Tat-Seng Chua
Yueting Zhuang
Siliang Tang

Large Language Models (LLMs) demonstrate remarkable proficiency in comprehending and handling text-based tasks. Many efforts are being made to transfer these attributes to video modality, which are termed Video-LLMs. However, existing Video-LLMs can only capture the coarse-grained semantics and are unable to effectively handle tasks related to comprehension or localization of specific video segments. In light of these challenges, we propose Momentor, a Video-LLM capable of accomplishing fine-grained temporal understanding tasks. To support the training of Momentor, we design an automatic data generation engine to construct Moment-10M, a large-scale video instruction dataset with segment-level instruction data. We train Momentor on Moment-10M, enabling it to perform segment-level reasoning and localization. Zero-shot evaluations on several tasks demonstrate that Momentor excels in fine-grained temporally grounded comprehension and localization.

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YNICL Journal 2022 Journal Article

Fast and equilibrium CEST imaging of brain tumor patients at 3T

Yin Wu
Zhou Liu
Qian Yang
Liyan Zou
Fan Zhang
Long Qian
Xin Liu
Hairong Zheng

Chemical exchange saturation transfer (CEST) MRI, versatile for detecting endogenous mobile proteins and tissue pH, has proved valuable in tumor imaging. However, CEST MRI scans are often performed under non-equilibrium conditions, which confound tissue characterization. This study proposed a quasi-steady-state (QUASS) CEST MRI algorithm to standardize fast and accurate tumor imaging at 3 T. The CEST signal evolution was modeled by longitudinal relaxation rate during relaxation delay (Td) and spinlock relaxation during RF saturation time (Ts), from which the QUASS CEST effect is derived. Numerical simulation and human MR imaging experiments (7 healthy volunteers and 19 tumor patients) were conducted at 3 T to compare the CEST measurements obtained under two representative experimental conditions. In addition, amide proton transfer (APT), combined magnetization transfer (MT) and nuclear overhauser enhancement (NOE) effects, and direct water saturation were isolated using a 3-pool Lorentzian fitting in white matter and gray matter of healthy volunteers and for patients in the contralateral normal-appearing white matter and tumor regions. Finally, the student's t-test was performed between conventional and QUASS CEST measurements. The routine APT and combined MT & NOE measures significantly varied with Ts and Td (P .05), indicating the accuracy and robustness of QUASS CEST MRI for tumor imaging. To summarize, the QUASS CEST reconstruction algorithm enables fast and accurate tumor CEST imaging at 3 T, promising to expedite and standardize clinical CEST MRI.

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NeurIPS Conference 2022 Conference Paper

Fine-Grained Semantically Aligned Vision-Language Pre-Training

Juncheng Li
Xin He
Longhui Wei
Long Qian
Linchao Zhu
Lingxi Xie
Yueting Zhuang
Qi Tian

Large-scale vision-language pre-training has shown impressive advances in a wide range of downstream tasks. Existing methods mainly model the cross-modal alignment by the similarity of the global representations of images and text, or advanced cross-modal attention upon image and text features. However, they fail to explicitly learn the fine-grained semantic alignment between visual regions and textual phrases, as only global image-text alignment information is available. In this paper, we introduce LOUPE, a fine-grained semantically aLigned visiOn-langUage PrE-training framework, which learns fine-grained semantic alignment from the novel perspective of game-theoretic interactions. To efficiently estimate the game-theoretic interactions, we further propose an uncertainty-aware neural Shapley interaction learning module. Experiments show that LOUPE achieves state-of-the-art performance on a variety of vision-language tasks. Without any object-level human annotations and fine-tuning, LOUPE achieves competitive performance on object detection and visual grounding. More importantly, LOUPE opens a new promising direction of learning fine-grained semantics from large-scale raw image-text pairs.

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IROS Conference 2020 Conference Paper

FlexiVision: Teleporting the Surgeon's Eyes via Robotic Flexible Endoscope and Head-Mounted Display

Long Qian
Chengzhi Song
Yiwei Jiang
Qi Luo
Xin Ma 0008
Philip Wai Yan Chiu
Zheng Li 0012
Peter Kazanzides

A flexible endoscope introduces more dexterity to the image capturing in endoscopic surgery. However, manual control or automatic control based on instrument tracking does not handle the misorientation between the endoscopic video and the surgeon. We propose an automatic flexible endoscope control method that tracks the surgeon's head with respect to the object in the surgical scene. The robotic flexible endoscope is actuated so that it captures the surgical scene from the same perspective as the surgeon. The surgeon wears a head-mounted display to observe the endoscopic video. The frustum of the flexible endoscope is rendered as an augmented reality overlay to provide surgical guidance. We developed the prototype, FlexiVision, integrating a 6-DOF robotic flexible endoscope based on the da Vinci Research Kit and Microsoft HoloLens. We evaluated the proposed automatic control method via a lesion observation task, and evaluated the AR surgical guidance in a lesion targeting task. The multi-user study results demonstrated that, for both tasks, FlexiVision significantly reduced the completion time (by 59% and 58%), number of errors (by 75% and 95%) and subjective task load level. With FlexiVision, the flexible endoscope could act as the surgeon's eyes teleported into the abdominal cavity of the patient.

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ICRA Conference 2019 Conference Paper

Augmented Reality Assisted Instrument Insertion and Tool Manipulation for the First Assistant in Robotic Surgery

Long Qian
Anton Deguet
Zerui Wang
Yun-Hui Liu 0001
Peter Kazanzides

In robotic-assisted laparoscopic surgery, the first assistant (FA) stands at the bedside assisting the intervention, while the surgeon sits at the console teleoperating the robot. Tasks for the FA include navigating new instruments into the surgeon's field-of-view and passing in or retracting materials from the body using hand-held tools. We previously developed ARssist, an augmented reality application based on an optical see-through head-mounted display, to aid the FA. In this paper, we refine the system and first perform a pilot study with three experienced surgeons for two specific tasks: instrument insertion and tool manipulation. The results suggest that ARssist would be especially useful for less experienced assistants and for difficult hand-eye configurations. We then perform a multi-user study with inexperienced subjects. The results show that ARssist can reduce navigation time by 34. 57%, enhance insertion path consistency by 41. 74%, reduce root-mean-square path deviation by 40. 04%, and reduce tool manipulation time by 72. 25%. Thus, ARssist has the potential to improve efficiency, safety and hand-eye coordination, especially for novice assistants.

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