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Weihao Xia

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3 papers

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AAAI Conference 2026 Conference Paper

Multigranular Evaluation for Brain Visual Decoding

Weihao Xia
Cengiz Oztireli

Existing evaluation protocols for brain visual decoding predominantly rely on coarse metrics that obscure inter-model differences, lack neuroscientific foundation, and fail to capture fine-grained visual distinctions. To address these limitations, we introduce BASIC, a unified, multigranular evaluation framework that jointly quantifies structural fidelity, inferential alignment, and contextual coherence between decoded and ground-truth images. For the structural level, we introduce a hierarchical suite of segmentation-based metrics, including foreground, semantic, instance, and component masks, anchored in granularity-aware correspondence across mask structures. For the semantic level, we extract structured scene representations encompassing objects, attributes, and relationships using multimodal large language models, enabling detailed, scalable, and context-rich comparisons with ground-truth stimuli. We benchmark a diverse set of visual decoding methods across multiple stimulus-neuroimaging datasets within this unified evaluation framework. Together, these criteria provide a more discriminative, interpretable, and comprehensive foundation for evaluating brain visual decoding methods.

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AAAI Conference 2026 Conference Paper

Single-Stage fMRI-to-3D Reconstruction via Viewpoint-Aware Embedding and Hierarchical Guidance

Xun Zhang
Weihao Xia
Yulong Liu
Bo Yang
Alessandro Bozzon
Pan Wang

Understanding the neural basis of three-dimensional (3D) perception is a fundamental objective in cognitive neuroscience. Despite advances in decoding 2D visual stimuli from neural data, reconstructing high-fidelity 3D objects with detailed texture and geometry remains largely unexplored. In this work, we introduce NeuroSculptor3D, the first single-stage, end-to-end framework for reconstructing textured 3D shapes directly from brain activity. NeuroSculptor3D integrates a viewpoint-aware brain embedding module that captures fine-grained spatial variations across visual perspectives, and a hierarchical guidance mechanism that aligns brain-derived features with perceptual, semantic, and structural priors. Together, these components facilitate the generation of consistent multi-view embeddings, which are then decoded via TRELLIS to produce high-quality textured 3D reconstructions. Experiments on the fMRI-Shape dataset demonstrate that NeuroSculptor3D outperforms existing baselines across multiple settings, achieving significant improvements in both structural accuracy and semantic consistency. Code will be released to facilitate further research.

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AAAI Conference 2025 Short Paper

Temporal Streaming Batch Principal Component Analysis for Time Series Classification (Student Abstract)

Enshuo Yan
Huachuan Wang
Weihao Xia

In multivariate time series classification, although current sequence analysis models have excellent classification capabilities, they show significant shortcomings when dealing with long sequence multivariate data. This paper focuses on optimizing model performance for long-sequence multivariate data by mitigating the impact of extended time series and multiple variables on the model. We propose a principal component analysis (PCA)-based temporal streaming compression and dimensionality reduction algorithm for time series data (temporal streaming batch PCA, TSBPCA), which continuously updates the compact representation of the entire sequence through streaming PCA time estimation with time block updates, enhancing the data representation capability of a range of sequence analysis models.We evaluated this method using various models on five datasets, and the experimental results show that our method demonstrates outstanding performance in both classification accuracy and time efficiency.

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