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Huiguang He

7 accepted papers

2026

Mind-Omni: A Unified Multi-Task Framework for Brain-Vision-Language Modeling via Discrete Diffusion

ICML 2026spotlight

Modeling the interplay between external stimuli and internal neural representations is a pivotal research area for Brain-Computer Interfaces (BCIs). A major limitation of prior work is the prevailing paradigm of specialized, single-task models, which curtails versatility and neglects inter-task syne…

Cited by 0SourceScholar
2025

Animate Your Thoughts: Reconstruction of Dynamic Natural Vision from Human Brain Activity

ICLR 2025poster

Reconstructing human dynamic vision from brain activity is a challenging task with great scientific significance. Although prior video reconstruction methods have made substantial progress, they still suffer from several limitations, including: (1) difficulty in simultaneously reconciling semantic…

2025

EmoGrowth: Incremental Multi-label Emotion Decoding with Augmented Emotional Relation Graph

ICML 2025poster

Emotion recognition systems face significant challenges in real-world applications, where novel emotion categories continually emerge and multiple emotions often co-occur. This paper introduces multi-label fine-grained class incremental emotion decoding, which aims to develop models capable of incre…

2025

ThicknessVAE: Learning a Lateral Prior for Clothed Human Body Reconstruction

ICASSP 2025accepted

Sandwich-like structures have shown remarkable efficacy in clothed human reconstruction. However, these approaches often generate unrealistic side geometries due to inadequate handling of lateral regions. This paper addresses this limitation by incorporating the side geometry of clothed humans as a…

Cited by 0SourceScholar
2024

CLIP-MUSED: CLIP-Guided Multi-Subject Visual Neural Information Semantic Decoding

ICLR 2024poster

The study of decoding visual neural information faces challenges in generalizing single-subject decoding models to multiple subjects, due to individual differences. Moreover, the limited availability of data from a single subject has a constraining impact on model performance. Although prior multi-s…

2022

Going Deeper into Permutation-Sensitive Graph Neural Networks

ICML 2022spotlight

The invariance to permutations of the adjacency matrix, i.e., graph isomorphism, is an overarching requirement for Graph Neural Networks (GNNs). Conventionally, this prerequisite can be satisfied by the invariant operations over node permutations when aggregating messages. However, such an invariant…