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AssigneeNVIDIA Corporation
InventorsMin-Hung Chen, Ci-Siang Lin, Chien-Yi Wang, Sifei Liu, Yu-Chiang Wang
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Referring Video Object Segmentation (RVOS) aims to segment an object referred to by a sentence query throughout an entire video. In contrast to Referring Image Segmentation (RIS), RVOS is particularly faced with dynamic visual challenges, such as position and size variation, pose deformation, object occlusion or exit, and scene variation. Moreover, the referring sentence may contain long-term motions or actions, which may not be easily recognized from a single frame. Existing works that address this challenging task generally require end-to-end training for vision-language models, which can be computationally expensive and time-consuming, while the requirement of dense mask annotations for training impedes the scalability of those approaches. The present disclosure uses grounded prompting to adapt image-based segmentation models to video object segmentation tasks, which can be achieved with relying only on weak supervision.
A method, comprising
A method, comprising:
A system, comprising:
A non-transitory computer-readable media storing computer instructions which when executed by one or more processors of a device cause the device to train a model to generate location information for a target object in a video based on a text prompt referring to the target object in the video by:
Dependencies are inferred from the claim text.