6 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
AudioX: A Unified Framework for Anything-to-Audio Generation
Zeyue Tian, Zhaoyang Liu, Yizhu Jin +6
Audio and music generation based on flexible multimodal control signals is a widely applicable topic, with the following key challenges: 1) a unified multimodal modeling framework,…
Inference-time Scaling for Diffusion-based Audio Super-resolution
Yizhu Jin, Zhen Ye, Zeyue Tian +4
Diffusion models have demonstrated remarkable success in generative tasks, including audio super-resolution (SR). In many applications like movie post-production and album masterin…
Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis
Zhen Ye, Xinfa Zhu, Chi-Min Chan +17
Recent advances in text-based large language models (LLMs), particularly in the GPT series and the o1 model, have demonstrated the effectiveness of scaling both training-time and i…
I-MedSAM: Implicit Medical Image Segmentation with Segment Anything
Xiaobao Wei, Jiajun Cao, Yizhu Jin +3
With the development of Deep Neural Networks (DNNs), many efforts have been made to handle medical image segmentation. Traditional methods such as nnUNet train specific segmentatio…
A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models
Rui Ma, Qiang Zhou, Yizhu Jin +11
Copyright law confers upon creators the exclusive rights to reproduce, distribute, and monetize their creative works. However, recent progress in text-to-image generation has intro…