activity
20222025
most citedDual Preference Distribution Learning for Item Recommendation

5 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

TTOM: Test-Time Optimization and Memorization for Compositional Video Generation

Leigang Qu, Ziyang Wang, Na Zheng +3

Video Foundation Models (VFMs) exhibit remarkable visual generation performance, but struggle in compositional scenarios (e.g., motion, numeracy, and spatial relation). In this wor…

cs.AI2025★ 2 cited

Mitigating Hallucination Through Theory-Consistent Symmetric Multimodal Preference Optimization

Wenqi Liu, Xuemeng Song, Jiaxi Li +4

Direct Preference Optimization (DPO) has emerged as an effective approach for mitigating hallucination in Multimodal Large Language Models (MLLMs). Although existing methods have a…

cs.IR2025

Modality Reliability Guided Multimodal Recommendation

Xue Dong, Xuemeng Song, Na Zheng +2

Multimodal recommendation faces an issue of the performance degradation that the uni-modal recommendation sometimes achieves the better performance. A possible reason is that the u…

cs.CL2023

VK-G2T: Vision and Context Knowledge enhanced Gloss2Text

Liqiang Jing, Xuemeng Song, Xinxing Zu +3

Existing sign language translation methods follow a two-stage pipeline: first converting the sign language video to a gloss sequence (i.e. Sign2Gloss) and then translating the gene…

cs.IR2022★ 5 cited

Dual Preference Distribution Learning for Item Recommendation

Xue Dong, Xuemeng Song, Na Zheng +2

Recommender systems can automatically recommend users with items that they probably like. The goal of them is to model the user-item interaction by effectively representing the use…