  Referring Video Object Segmentation (RVOS) aims to segment the object
referred to by the query sentence throughout the entire video. Most existing
methods require end-to-end training with dense mask annotations, which could be
computation-consuming and less scalable. In this work, we aim to efficiently
adapt foundation segmentation models for addressing RVOS from weak supervision
with the proposed Grounded Prompting (GroPrompt) framework. More specifically,
we propose Text-Aware Prompt Contrastive Learning (TAP-CL) to enhance the
association between the position prompts and the referring sentences with only
box supervisions, including Text-Contrastive Prompt Learning (TextCon) and
Modality-Contrastive Prompt Learning (ModalCon) at frame level and video level,
respectively. With the proposed TAP-CL, our GroPrompt framework can generate
temporal-consistent yet text-aware position prompts describing locations and
movements for the referred object from the video. The experimental results in
the standard RVOS benchmarks (Ref-YouTube-VOS, Ref-DAVIS17, A2D-Sentences, and
JHMDB-Sentences) demonstrate the competitive performance of our proposed
GroPrompt framework given only bounding box weak supervisions.
