4 accepted papers
Model merging aims to integrate multiple independently fine-tuned expert models into a single model while preserving the knowledge of all experts. However, existing approaches mainly address parameter conflicts at the merging stage and overlook the role of the fine-tuning process, which often leads…
Continual learning with large pre-trained models offers significant potential for cross-task knowledge accumulation, but faces critical challenges such as catastrophic forgetting and parameter interference, especially when historical data is unavailable. Existing approaches typically rely on sequent…
Automatic Video Dubbing (AVD) aims to synthesize speech that matches a character’s speaking style and emotion in silent video clips. However, existing approaches rely on attention mechanisms to learn cross-modal prosodic alignment implicitly, making it challenging to capture subtle prosodic variatio…
In the Text-to-speech(TTS) task, the latent diffusion model has excellent fidelity and generalization, but its expensive resource consumption and slow inference speed have always been a challenging. To address this issue, this paper proposes the Discrete Diffusion Model with Contrastive Learning for…