most citedG2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory

3 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024

Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning

Xuxin Cheng, Wanshi Xu, Zhihong Zhu +2

Spoken language understanding (SLU) is a core task in task-oriented dialogue systems, which aims at understanding the user's current goal through constructing semantic frames. SLU…

cs.CL2023

ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding

Xuxin Cheng, Bowen Cao, Qichen Ye +3

Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue systems. However, the inevitable errors from automatic speech recognition (ASR) usually impa…

cs.CV2023★ 3 cited

G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory

Hongxiang Li, Meng Cao, Xuxin Cheng +3

The recent video grounding works attempt to introduce vanilla contrastive learning into video grounding. However, we claim that this naive solution is suboptimal. Contrastive learn…

cs.CV2023★ 3 cited

Unify, Align and Refine: Multi-Level Semantic Alignment for Radiology Report Generation

Yaowei Li, Bang Yang, Xuxin Cheng +3

Automatic radiology report generation has attracted enormous research interest due to its practical value in reducing the workload of radiologists. However, simultaneously establis…

cs.MM2023★ 1 cited

SSVMR: Saliency-based Self-training for Video-Music Retrieval

Xuxin Cheng, Zhihong Zhu, Hongxiang Li +2

With the rise of short videos, the demand for selecting appropriate background music (BGM) for a video has increased significantly, video-music retrieval (VMR) task gradually draws…

cs.CV2023★ 1 cited

Exploiting Auxiliary Caption for Video Grounding

Hongxiang Li, Meng Cao, Xuxin Cheng +3

Video grounding aims to locate a moment of interest matching the given query sentence from an untrimmed video. Previous works ignore the {sparsity dilemma} in video annotations, wh…