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Zhangling Duan

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3 papers

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AAAI Conference 2026 Conference Paper

CLASP: Cross-modal Salient Anchor-based Semantic Propagation for Weakly-supervised Dense Audio-Visual Event Localization

Jinxing Zhou
Ziheng Zhou
Yanghao Zhou
Yuxin Mao
Zhangling Duan
Dan Guo

The Dense Audio-Visual Event Localization (DAVEL) task aims to temporally localize events in untrimmed videos that occur simultaneously in both the audio and visual modalities. This paper explores DAVEL under a new and more challenging weakly-supervised setting (W-DAVEL task), where only video-level event labels are provided and the temporal boundaries of each event are unknown. We address W-DAVEL by exploiting cross-modal salient anchors, which are defined as reliable timestamps that are well predicted under weak supervision and exhibit highly consistent event semantics across audio and visual modalities. Specifically, we propose a Mutual Event Agreement Evaluation module, which generates an agreement score by measuring the discrepancy between the predicted audio and visual event classes. Then, the agreement score is utilized in a Cross-modal Salient Anchor Identification module, which identifies the audio and visual anchor features through global-video and local temporal window identification mechanisms. The anchor features after multimodal integration are fed into an Anchor-based Temporal Propagation module to enhance event semantic encoding in the original temporal audio and visual features, facilitating better temporal localization under weak supervision. We establish benchmarks for W-DAVEL on both the UnAV-100 and ActivityNet1.3 datasets. Extensive experiments demonstrate that our method achieves state-of-the-art performance.

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EAAI Journal 2025 Journal Article

Confidence-aware iterative training for cross-lingual entity alignment

Fan Ye
Yu Zeng
Zhangling Duan
Zhaolong Ling
Yun Yang

Entity alignment (EA) aims to identify equivalent entities across knowledge graphs, serving as a critical step in integrating multi-source knowledge graphs. In recent years, EA methods relying on alignment seeds have achieved impressive performance. However, the high cost of manually labeled alignment seeds has posed a significant limitation to their practical applications in the real world. Most methods adopt iterative strategies to generate pseudo-alignment seeds automatically. However, unreliable iterative strategies introduce a large number of noisy pseudo-alignment seeds, leading to low-quality entity embeddings. In this paper, we propose a Confidence-Aware Iterative Training (CAIT) framework for unsupervised EA tasks. The framework initially extracts semantic and structural features of entities from knowledge graphs, then generates pseudo-alignment seeds by the confidence-aware iterative strategy, which limits the quantity of noisy pseudo-alignment seeds and progressively enhances the quality of entity embeddings. Extensive experiments conducted on widely used benchmark datasets demonstrate that CAIT outperforms existing state-of-the-art methods in cross-lingual EA tasks, both with and without prior alignment seeds.

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IJCAI Conference 2025 Conference Paper

Gradient-based Causal Feature Selection

Zhaolong Ling
Mengxiang Guo
Xingyu Wu
Debo Cheng
Peng Zhou
Tianci Li
Zhangling Duan

Causal feature selection leverages causal discovery techniques to identify critical features associated with a target variable using observational data. Traditional methodologies primarily rely on constraint-based or score-based techniques, which are fraught with limitations. For example, conditional independence tests often yield unreliable results in the presence of noise and complex data generation processes, while the computational complexity of learning directed acyclic graphs increases exponentially with the number of variables involved. In light of recent advancements in deep learning, gradient-based methods have shown promise for global causal discovery. However, significant challenges arise when focusing on the identification of local causal features, particularly in defining the local causal constraint space to achieve both minimality and completeness. To address these issues, we introduce a novel gradient-based causal feature selection method (GCFS) that leverages an AutoEncoder to simultaneously model the target variable alongside other variables, thereby capturing of causal associations within a divide-and-conquer framework. Additionally, our approach incorporates a mask pruning strategy that transforms the search process into the minimization of a non-cyclic local reconstruction loss objective function. This function is then effectively optimized using a gradient-based method to accurately identify the causal features related to the target variable. Experimental results substantiate that GCFS surpasses existing methodologies across both synthetic and real datasets.

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