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In the field of video analysis, the persistence of objects is an important cue for humans to understand that objects still exist even when completely occluded. However, current object segmentation methods mainly focus on visible (modal) objects, lacking the capability to handle non-modal (visible + invisible) objects. To address this issue, researchers proposed a two-stage method based on diffusion priors, Diffusion-Vas, aimed at improving the effectiveness of non-modal segmentation and content completion in videos, enabling the tracking of specified targets within the video and using diffusion models to complete the occluded parts.

A research team composed of Renmin University of China, Beijing University of Posts and Telecommunications, and Shanghai AI Lab has developed a technology called Ref-AVS, aimed at solving the challenge of artificial intelligence understanding the complex physical world. This technology utilizes a unique multimodal fusion method that integrates Video Object Segmentation (VOS), Video Object Reference Segmentation (Ref-VOS), and audiovisual segmentation (AVS) information, enabling AI systems to accurately identify and locate specific objects in audiovisual scenes, regardless of whether those objects produce sound. To validate the effectiveness of the technology, the research team...