Published in Neurocomputing, 2026
In this paper, we propose a new prototype-based hierarchical alignment network (PHA-Net) to align individual/local/global level representations across modalities.
Recommended citation: Xiaolun Jing, Kezhao Yin, Xinxing Yang, Genke Yang, Jian Chu. PHA-Net: Prototype-based hierarchical alignment network for text-video retrieval[J]. Neurocomputing, 2026, 702:134693.
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Published in Information Fusion, 2025
In this paper, we propose a novel Text-Conditioned Multi-Grained Contrast (TC-MGC) framework to explore multi-grained contrasts between textual and semantic-relevant visual representations.
Recommended citation: Xiaolun Jing, Genke Yang, Jian Chu. TC-MGC: Text-conditioned multi-grained contrastive learning for text-video retrieval[J]. Information Fusion, 2025, 121:103151.
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Published in Neurocomputing, 2024
In this work, we rethink the inherent limitation of widely-used mean pooling operation in the frame features aggregation and investigate the adaptions of excitation and aggregation design for discriminative video representation generation.
Recommended citation: Xiaolun Jing, Genke Yang, Jian Chu. An empirical study of excitation and aggregation design adaptions in CLIP4Clip for video-text retrieval[J]. Neurocomputing, 2024, 596:127905.
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Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Published in China Automation Congress (CAC), 2025
In this paper, we propose a simple yet effective method called Global-Local Contrastive Consistency Learning (GLCCL) to achieve texts and videos semantics alignment.
Recommended citation: X. Jing, X. Yang and G. Yang, "Text-Video Retrieval With Global-Local Contrastive Consistency Learning," 2025 China Automation Congress (CAC), Harbin, China, 2025, pp. 1621-1626.
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