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Chunyu Hu

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3 papers

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AAAI Conference 2026 Conference Paper

Adaptive Momentum and EMA-weighted Modeling for Imbalanced Label Distribution Learning

Yongbiao Gao
Xiangcheng Sun
Chao Tan
Chunyu Hu
Guohua Lv

Label Distribution Learning (LDL) is a groundbreaking paradigm for addressing the task with label ambiguity. Subjectivity in annotating label description degrees often leads to imbalanced label distribution. Existing approaches either adopt representation alignment or decoupling strategies to solve the imbalanced label distribution learning (ILDL). However, representation alignment-based methods overlook the issue of gradient vanishing for non-dominant branches within imbalanced label distributions, while decoupling-based approaches fail to achieve adaptive weight optimization. To address these issues, we propose Adaptive Momentum and Exponential Moving Average weighted modeling (AMEMA). AMEMA combines EMA-based loss weighting with momentum allocation to mitigate gradient attenuation in non-dominant label learning and adaptively balance the optimization signals between dominant and non-dominant branches. It computes and updates Kullback-Leibler divergence losses for each branch using EMA, and applies different initial momenta to facilitate branch-specific optimization dynamics. Dynamic weighting coefficients, derived from EMA-smoothed losses, allow the model to adjust its learning direction adaptively and improve the learning of non-dominant labels. Extensive experiments on benchmark datasets show that AMEMA consistently outperforms state-of-the-art ILDL methods across various evaluation metrics.

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AAAI Conference 2026 Conference Paper

Invariant Conditional Molecular Generation Under Distribution Shift

Chunyu Hu
Tianyin Liao
Yicheng Sui
Ran Zhang
Xiao Wang
Ziwei Zhang

Conditional molecular generation, aiming to generate 2D and 3D molecules that satisfy given properties, has achieved remarkable progress, thanks to the advances in deep generative models such as graph diffusion. However, existing methods generally assume that the given conditions for training and testing are consistent, failing to handle the realistic challenge when there exist distribution shifts between training and testing conditions. Invariant learning is a mainstream paradigm for addressing distribution shifts, but fusing invariant learning principles with conditional molecular generation faces three core challenges: (1) existing invariant learning methods focus on discriminative tasks and cannot be directly adapted to molecule generative tasks; (2) how to distinguish between invariant subgraph and variant subgraph of a molecule graph, which is treated as an integrated input; (3) how to fuse invariant subgraphs, variant subgraphs, and property conditions for effective generation. To tackle these challenges, we propose Invariant Conditional MOLecular generation (IC-MOL), a framework that combines invariant learning with graph diffusion to improve the generalization ability of conditional molecular generation under distribution shifts. Specifically, we first disentangle molecular graphs into invariant and variant subgraphs while maintaining SE(3) equivariance, an important inductive bias for molecular generation. On this basis, we further design a two-phase graph diffusion generation model. In the first phase, we generate an invariant molecular consistent with the target property. In the second phase, we propose a cross-attention mechanism to fuse variant subgraph representations and property conditions to guide the generation of complete molecules while maintaining property alignment. Extensive experiments on the benchmark dataset show that IC-MOL consistently outperforms state-of-the-art baselines across six property conditions under distribution shifts.

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AAAI Conference 2024 Conference Paper

QI-IRA: Quantum-Inspired Interactive Ranking Aggregation for Person Re-identification

Chunyu Hu
Hong Zhang
Chao Liang
Hao Huang

Ranking aggregation (RA), the process of aggregating multiple rankings derived from multiple search strategies, has been proved effective in person re-identification (re-ID) because of a single re-ID method can not always achieve consistent superiority for different scenarios. Existing RA research mainly focus on unsupervised and fully-supervised methods. The former lack external supervision to optimize performance, while the latter are costly because of expensive labeling effort required for training. To address the above challenges, this paper proposes a quantum-inspired interactive ranking aggregation (QI-IRA) method, which (1) utilizes quantum theory to interpret and model the generation and aggregation of multiple basic rankings, (2) approximates or even exceeds the performance of fully-supervised RA methods with much less labeling cost, even as low as only two feedbacks per query on Market1501, MARS and DukeMTMC-VideoReID datasets. Comparative experiments conducted on six public re-ID datasets validate the superiority of the proposed QI-IRA method over existing unsupervised, interactive, and fully-supervised RA approaches.

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