6 papers
Learning to Generate via Understanding: Understanding-Driven Intrinsic Rewarding for Unified Multimodal Models
Jiadong Pan, Liang Li, Yuxin Peng +6
Recently, unified multimodal models (UMMs) have made remarkable progress in integrating visual understanding and generation, demonstrating strong potential for complex text-to-imag…
Semantic Energy: Detecting LLM Hallucination Beyond Entropy
Huan Ma, Jiadong Pan, Jing Liu +7
Large Language Models (LLMs) are being increasingly deployed in real-world applications, but they remain susceptible to hallucinations, which produce fluent yet incorrect responses…
FlowDubber: Movie Dubbing with LLM-based Semantic-aware Learning and Flow Matching based Voice Enhancing
Gaoxiang Cong, Liang Li, Jiadong Pan +5
Movie Dubbing aims to convert scripts into speeches that align with the given movie clip in both temporal and emotional aspects while preserving the vocal timbre of a given brief r…
SafeCFG: Controlling Harmful Features with Dynamic Safe Guidance for Safe Generation
Jiadong Pan, Liang Li, Hongcheng Gao +3
Diffusion models (DMs) have demonstrated exceptional performance in text-to-image tasks, leading to their widespread use. With the introduction of classifier-free guidance (CFG), t…
Self-Reflective Reinforcement Learning for Diffusion-based Image Reasoning Generation
Jiadong Pan, Zhiyuan Ma, Kaiyan Zhang +2
Diffusion models have recently demonstrated exceptional performance in image generation task. However, existing image generation methods still significantly suffer from the dilemma…
EmoDubber: Towards High Quality and Emotion Controllable Movie Dubbing
Gaoxiang Cong, Jiadong Pan, Liang Li +5
Given a piece of text, a video clip, and a reference audio, the movie dubbing task aims to generate speech that aligns with the video while cloning the desired voice. The existing…