5 citations · 10 across the 14 of their papers we have counts for
4 papers · 1 filter
MARS-Sep: Multimodal-Aligned Reinforced Sound Separation
Zihan Zhang, Xize Cheng, Zhennan Jiang +4
Universal sound separation faces a fundamental misalignment: models optimized for low-level signal metrics often produce semantically contaminated outputs, failing to suppress perc…
APO: Enhancing Reasoning Ability of MLLMs via Asymmetric Policy Optimization
Minjie Hong, Zirun Guo, Yan Xia +4
Multimodal Large Language Models (MLLMs) are powerful at integrating diverse data, but they often struggle with complex reasoning. While Reinforcement learning (RL) can boost reaso…
Unleashing the Power of Natural Audio Featuring Multiple Sound Sources
Xize Cheng, Slytherin Wang, Zehan Wang +3
Universal sound separation aims to extract clean audio tracks corresponding to distinct events from mixed audio, which is critical for artificial auditory perception. However, curr…
Towards Transformer-Based Aligned Generation with Self-Coherence Guidance
Shulei Wang, Wang Lin, Hai Huang +8
We introduce a novel, training-free approach for enhancing alignment in Transformer-based Text-Guided Diffusion Models (TGDMs). Existing TGDMs often struggle to generate semantical…