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Pan Zhou

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51 papers

2 author rows

EAAI Journal 2026 Journal Article

A curriculum-guided and explainable reinforcement learning framework for fixed-wing unmanned aerial vehicle autopilots

Yunxiao Lian
Ni Li
Pan Zhou
Ruiguang Hu
Changyin Dong

Current artificial intelligence methods when used for autopilots often suffer from poor control stability and a lack of explainability. To address these challenges, this paper proposes a curriculum-guided and explainable reinforcement learning framework for fixed-wing unmanned aerial vehicle autopilots. First, a sequential proximal policy optimization algorithm is developed for autopilots’ decision-making; the designed coupled reward function refines the guidance strategy for action selection, significantly improving the control performance of autopilots. Second, to tackle sparse rewards' impact on algorithm convergence, a curriculum learning method based on difficulty coefficient matrices is introduced to effectively guide agents' training process. Third, instead of treating the policy as a “black box”, semantic-grouped Shapley additive explanations are proposed to explain the policy. Finally, the efficacy of the designed autopilot is rigorously tested by a high-fidelity simulator JSBSim. The results indicate that the autopilot can adapt to various waypoint tracking tasks while exhibiting strong policy explainability.

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AAAI Conference 2026 Conference Paper

DRFGD: Disentangled Representation-Focused Generative Defense for Attack-Tolerant Cross-Modal Hashing

Zhongqing Yu
Xin Liu
Yiu-ming Cheung
Zhikai Hu
Wentao Fan
Pan Zhou

With the widespread deployment of cross-modal retrieval in real-world scenarios, ensuring robustness against adversarial attacks is increasingly critical. Remarkably, deep cross-modal hashing is highly vulnerable to adversarial attacks due to its discrete nature and low-dimensional hash codes, while existing defense methods often fail to suppress perturbations embedded in vulnerable features and lack the capacity to model modality-specific structural differences, resulting in suboptimal adversarial robustness. To address these challenges, we propose a novel Disentangled Representation-Focused Generative Defense (DRFGD) framework for attack-tolerant cross-modal hashing. Without altering the structure of retrieval model, DRFGD defends against adversarial attacks by disentangling input representations into adversarial-robust and adversarial-vulnerable components, by an efficient dual-branch semantic-aware encoder. Guided by such disentangled robust features, an attack-tolerant generative module is seamlessly designed to synthesize semantically aligned and perturbation-resilient examples for robust adversarial training, thereby significantly promoting collaborative defense robustness to attackers. Consequently, the semantically consistent hash codes can be well obtained to enhance adversarial robustness in complex cross-modal attacking scenarios. Extensive experiments on public benchmarks demonstrate that DRFGD substantially improves retrieval robustness under various attacking scenarios, and shows its improved defense performance in comparison with the SOTA works.

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AAAI Conference 2026 Conference Paper

ReAlign: Text-to-Motion Generation via Step-Aware Reward-Guided Alignment

Wanjiang Weng
Xiaofeng Tan
Junbo Wang
Guo-Sen Xie
Pan Zhou
Hongsong Wang

Text-to-motion generation, which synthesizes 3D human motions from text inputs, holds immense potential for applications in gaming, film, and robotics. Recently, diffusion-based methods have been shown to generate more diversity and realistic motion. However, there exists a misalignment between text and motion distributions in diffusion models, which leads to semantically inconsistent or low-quality motions. To address this limitation, we propose Reward-guided sampling Alignment (ReAlign), comprising a step-aware reward model to assess alignment quality during the denoising sampling and a reward-guided strategy that directs the diffusion process toward an optimally aligned distribution. This reward model integrates step-aware tokens and combines a text-aligned module for semantic consistency and a motion-aligned module for realism, refining noisy motions at each timestep to balance probability density and alignment. Extensive experiments of both motion generation and retrieval tasks demonstrate that our approach significantly improves text-motion alignment and motion quality compared to existing state-of-the-art methods.

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AAAI Conference 2026 Conference Paper

Revisiting the Canonicalization for Fast and Accurate Crystal Tensor Property Prediction

haowei hua
Jingwen Yang
Wanyu Lin
Pan Zhou

Predicting the tensor properties of crystalline materials is a fundamental task in materials science. Unlike single-value property prediction, which is inherently invariant, tensor property prediction requires maintaining O(3) group tensor equivariance. Such equivariance constraint often requires specialized architecture designs to achieve effective predictions, inevitably introducing tremendous computational costs. Canonicalization, a classical technique for geometry, has recently been explored for efficient learning with symmetry. In this work, we revisit the problem of crystal tensor property prediction through the lens of canonicalization. Specifically, we demonstrate how polar decomposition, a simple yet efficient algebraic method, can serve as a form of canonicalization and be leveraged to ensure equivariant tensor property prediction. Building upon this insight, we propose a general O(3)-equivariant framework for efficient crystal tensor property prediction, referred to as GoeCTP. By utilizing canonicalization, GoeCTP achieves high efficiency without requiring the explicit incorporation of equivariance constraints into the network architecture. Experimental results indicate that GoeCTP achieves the best prediction performance and runs at most 13 times faster compared to existing state-of-the-art methods in benchmarking datasets, underscoring its effectiveness and efficiency.

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TIST Journal 2026 Journal Article

SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization

Wen Shi
Wenchao Xu
Haozhao Wang
Xingshuo Han
Pan Zhou
Ruixuan Li

Natural language video localization (NLVL) is an important task in the vision-language understanding area, which calls for an in-depth understanding of not only computer vision and natural language side alone, but more importantly the interplay between both sides. Adversarial attack has been well-recognized as a critical security issue of deep neural network models, which requires prudent investigation. Despite its extensive yet separated studies in video and language two kinds of unimodal tasks, current understanding of the adversarial attack in NLVL tasks including both the vision and language modality is less developed. This paper therefore aims to comprehensively investigate the adversarial attacks of NLVL models by examining three different facets of adversarial attack methods. To achieve the attack goal, we propose a new adversarial attack paradigm called synonymous sentences-aware adversarial attack on NLVL (SNEAK), which captures the cross-modality interplay between the vision and language sides. To further enhance the stealthiness of SNEAK, we propose a frame importance-guided pruning with SNEAK attack (PSA) mechanism to reduce the amount of perturbed frames. Extensive experiments on five NLVL models and two datasets demonstrate the effectiveness and stealthiness of the proposed attack methods.

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NeurIPS Conference 2025 Conference Paper

BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization

Xueyang Zhou
Guiyao Tie
Guowen Zhang
Hecheng Wang
Pan Zhou
Lichao Sun

Vision-Language-Action (VLA) models have advanced robotic control by enabling end-to-end decision-making directly from multimodal inputs. However, their tightly coupled architectures expose novel security vulnerabilities. Unlike traditional adversarial perturbations, backdoor attacks represent a stealthier, persistent, and practically significant threat—particularly under the emerging Training-as-a-Service paradigm—but remain largely unexplored in the context of VLA models. To address this gap, we propose BadVLA, a backdoor attack method based on Objective-Decoupled Optimization, which for the first time exposes the backdoor vulnerabilities of VLA models. Specifically, it consists of a two-stage process: (1) explicit feature-space separation to isolate trigger representations from benign inputs, and (2) conditional control deviations that activate only in the presence of the trigger, while preserving clean-task performance. Empirical results on multiple VLA benchmarks demonstrate that BadVLA consistently achieves near-100\% attack success rates with minimal impact on clean task accuracy. Further analyses confirm its robustness against common input perturbations, task transfers, and model fine-tuning, underscoring critical security vulnerabilities in current VLA deployments. Our work offers the first systematic investigation of backdoor vulnerabilities in VLA models, highlighting an urgent need for secure and trustworthy embodied model design practices.