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Xiaoya Zhang

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5 papers

2 author rows

EAAI Journal 2025 Journal Article

Rapid prediction of thermal stress on satellites via domain decomposition-based Hybrid Fourier Neural Operator

Kangrui Zhou
Wei Peng
Xiaoya Zhang
Xu Liu
Wen Yao

Rapid thermal stress analysis is crucial for the thermal design of satellites. To overcome the disadvantages of traditional algorithms in terms of efficiency, deep learning methods have been used to tackle these problems. However, using uniform grid-based techniques is challenging when faced with complex geometric shapes. To address this, we introduce the domain decomposition-based Hybrid Fourier Neural Operator (HFNO), a comprehensive framework for learning a multi-scale and end-to-end operator on two-dimensional point clouds. We then propose two decomposition metrics: a stress gradient-based metric for scenarios with prior knowledge of training data, and a mesh density-based metric for scenarios without prior knowledge. Leveraging K-Dimension tree-based domain decomposition optimized via Monte Carlo tree search, we decompose the computational domain into several disjoint rectangular subdomains. In the proposed hybrid framework, a Geometry-aware Fourier Neural Operator (Geo-FNO) is used to deal with subdomains with high-frequency information, while a Non-Uniform Fourier Neural Operator (NU-FNO) is used to deal with subdomains with low-frequency information. This framework effectively combines the advantages of two Fourier Neural Operator variants, overcoming the issue of large prediction errors on the subdomains with high-frequency information and ensuring stable prediction performance across different positions. Furthermore, we introduce a boundary loss term during the training process to enhance continuity across subdomain boundaries. The numerical results demonstrate that our method achieves a superior balance between efficiency and precision, surpassing that of a single algorithm.

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AAAI Conference 2025 Conference Paper

Re-Attentional Controllable Video Diffusion Editing

Yuanzhi Wang
Yong Li
Mengyi Liu
Xiaoya Zhang
Xin Liu
Zhen Cui
Antoni B. Chan

Editing videos with textual guidance has garnered popularity due to its streamlined process which mandates users to solely edit the text prompt corresponding to the source video. Recent studies have explored and exploited large-scale text-to-image diffusion models for text-guided video editing, resulting in remarkable video editing capabilities. However, they may still suffer from some limitations such as mislocated objects, incorrect number of objects. Therefore, the controllability of video editing remains a formidable challenge. In this paper, we aim to challenge the above limitations by proposing a Re-Attentional Controllable Video Diffusion Editing (ReAtCo) method. Specially, to align the spatial placement of the target objects with the edited text prompt in a training-free manner, we propose a Re-Attentional Diffusion (RAD) to refocus the cross-attention activation responses between the edited text prompt and the target video during the denoising stage, resulting in a spatially location-aligned and semantically high-fidelity manipulated video. In particular, to faithfully preserve the invariant region content with less border artifacts, we propose an Invariant Region-guided Joint Sampling (IRJS) strategy to mitigate the intrinsic sampling errors w.r.t the invariant regions at each denoising timestep and constrain the generated content to be harmonized with the invariant region content. Experimental results verify that ReAtCo consistently improves the controllability of video diffusion editing and achieves superior video editing performance.

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AAAI Conference 2025 Conference Paper

Scene Graph-Grounded Image Generation

Fuyun Wang
Tong Zhang
Yuanzhi Wang
Xiaoya Zhang
Xin Liu
Zhen Cui

With the beneft of explicit object-oriented reasoning capabilities of scene graphs, scene graph-to-image generation has made remarkable advancements in comprehending object coherence and interactive relations. Recent state-of-the-arts typically predict the scene layouts as an intermediate representation of a scene graph before synthesizing the image. Nevertheless, transforming a scene graph into an exact layout may restrict its representation capabilities, leading to discrepancies in interactive relationships (such as standing on, wearing, or covering) between the generated image and the input scene graph. In this paper, we propose a Scene Graph-Grounded Image Generation (SGG-IG) method to mitigate the above issues. Specifcally, to enhance the scene graph representation, we design a masked auto-encoder module and a relation embedding learning module to integrate structural knowledge and contextual information of the scene graph with a mask self-supervised manner. Subsequently, to bridge the scene graph with visual content, we introduce a spatial constraint and image-scene alignment constraint to capture the fne-grained visual correlation between the scene graph symbol representation and the corresponding image representation, thereby generating semantically consistent and high-quality images. Extensive experiments demonstrate the effectiveness of the method both quantitatively and qualitatively.

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IROS Conference 2023 Conference Paper

OA-Bug: An Olfactory-Auditory Augmented Bug Algorithm for Swarm Robots in a Denied Environment

Siqi Tan
Xiaoya Zhang
Jingyao Li
Ruitao Jing
Mufan Zhao
Yang Liu
Quan Quan

Searching in a denied environment is challenging for swarm robots as no assistance from GNSS, mapping, data sharing, and central processing is allowed. However, using olfactory and auditory signals to cooperate like animals could be an important way to improve the collaboration of swarm robots. In this paper, an Olfactory-Auditory augmented Bug algorithm (OA-Bug) is proposed for a swarm of autonomous robots to explore a denied environment. A simulation environment is built to measure the performance of OA-Bug. The coverage of the search task can reach 96. 93% using OA-Bug, which is significantly improved compared with a similar algorithm, SGBA [1]. Furthermore, experiments are conducted on real swarm robots to prove the validity of OA-Bug. Results show that OA-Bug can improve the performance of swarm robots in a denied environment. Video: https://youtu.be/vj9cRiSmgeM.

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AAAI Conference 2022 Conference Paper

FCA: Learning a 3D Full-Coverage Vehicle Camouflage for Multi-View Physical Adversarial Attack

Donghua Wang
Tingsong Jiang
Jialiang Sun
Weien Zhou
Zhiqiang Gong
Xiaoya Zhang
Wen Yao
Xiaoqian Chen

Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an individual adversarial patch, which only covers the planar part of the vehicle’s surface and fails to attack the detector in physical scenarios for multi-view, long-distance and partially occluded objects. To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors. Specifically, we first try rendering the non-planar camouflage texture over the full vehicle surface. To mimic the real-world environment conditions, we then introduce a transformation function to transfer the rendered camouflaged vehicle into a photo-realistic scenario. Finally, we design an efficient loss function to optimize the camouflage texture. Experiments show that the full-coverage camouflage attack can not only outperform state-of-the-art methods under various test cases but also generalize to different environments, vehicles, and object detectors.

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