5 papers
MoAKE: Toward Unified All-in-One Action Quality Assessment via Mixture of Action Knowledge Experts
Huangbiao Xu, Huanqi Wu, Xiao Ke +3
Action Quality Assessment (AQA) aims to objectively evaluate performance quality from action videos. Most existing methods follow a ``one-by-one'' paradigm, training a separate mod…
LIMSSR: LLM-Driven Sequence-to-Score Reasoning under Training-Time Incomplete Multimodal Observations
Huangbiao Xu, Huanqi Wu, Xiao Ke +1
Real-world multimodal learning is often hindered by missing modalities. While Incomplete Multimodal Learning (IML) has gained traction, existing methods typically rely on the unrea…
: Towards Semantic Steganography via Large Language Models
Huanqi Wu, Huangbiao Xu, Runfeng Xie +3
Despite remarkable progress in steganography, embedding semantically rich, sentence-level information into carriers remains a challenging problem. In this work, we present a novel…
MCMoE: Completing Missing Modalities with Mixture of Experts for Incomplete Multimodal Action Quality Assessment
Huangbiao Xu, Huanqi Wu, Xiao Ke +3
Multimodal Action Quality Assessment (AQA) has recently emerged as a promising paradigm. By leveraging complementary information across shared contextual cues, it enhances the disc…
URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration
Rui Xu, Yuzhen Niu, Yuezhou Li +3
Existing low-light image enhancement (LLIE) and joint LLIE and deblurring (LLIE-deblur) models have made strides in addressing predefined degradations, yet they are often constrain…