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MingMing Kong

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EAAI Journal 2026 Journal Article

Entity-relation enhanced bidirectional information fusion for relational triples extraction

Haoran Liao
Yuan Rong
MingMing Kong
Chao Zhang
Xianjun Tian
Vladimir Simic
Dragan Pamucar

Relational triple extraction refers to identifying triples consisting of entities and relations form unstructured texts. The existing studies usually adopt an unidirectional extraction strategy, which fails to fully explore the semantic information related to entities and relations. And they rely heavily on initial extraction results when conducting multi-step extraction. To address this issue, we propose a novel Entity-Relation Enhanced Bidirectional Information Fusion approach (ER-EBIF). Specifically, we adopt a bidirectional extraction strategy of ”entity-to-relation” and ”relation-to-entity” to identify triples. One branch extracts potential relations, then extracts entities associated with those relations. The other branch initially extracts the potential subjects and objects as well as subsequently extracts relations between pairs of entities consisting of subjects and objects. Moreover, the contextual information is enhanced with a self-attention mechanism by integrating the information of potential relations and potential entities to better exploit the semantic information of entities and relations. Extensive experimental results on various datasets show that ER-EBIF exhibits better performance than other baselines and effectiveness in addressing the issue of dependency on initial results in multi-step extraction.

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AAAI Conference 2025 Conference Paper

GLIC: General Format Learned Image Compression

MingSheng Zhou
MingMing Kong

Learned image lossy compression techniques have surpassed traditional methods in both subjective vision and quantitative evaluation. However, current models are only applicable to three-channel image formats, limiting their practical application due to the diversity and complexity of image formats. We propose a high-performance learned image compression model for general image formats. We first introduce a transfer method to unify any-channel image formats, enhancing the applicability of neural networks. This method's effectiveness is demonstrated through image information entropy and image homomorphism theory. Then, we introduce an adaptive attention residual block into the entropy model to give it better generalization ability. Meanwhile, we propose an evenly grouped cross-channel context module for progressive preview image decoding. Experimental results demonstrate that our method achieves state-of-the-art (SOTA) in the field of learned image compression in terms of PSNR and MS-SSIM. This work extends the applicability of learned image compression techniques to more practical production environments.

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