Temporal Prompting Matters: Rethinking Referring Video Object
  Segmentation
Authors: name: Ci-Siang Lin, name: Min-Hung Chen, name: I-Jieh Liu, name: Chien-Yi Wang, name: Sifei Liu, name: Yu-Chiang Frank Wang

Referring Video Object Segmentation (RVOS) aims to segment the object
referred to by the query sentence in the 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 rethink the RVOS
problem and aim to investigate the key to this task. Based on existing
foundation segmentation models, we decompose the RVOS task into referring,
video, and segmentation factors, and propose a Temporal Prompt Generation and
Selection (Tenet) framework to address the referring and video factors while
leaving the segmentation problem to foundation models. To efficiently adapt
image-based foundation segmentation models to referring video object
segmentation, we leverage off-the-shelf object detectors and trackers to
produce temporal prompts associated with the referring sentence. While
high-quality temporal prompts could be produced, they can not be easily
identified from confidence scores. To tackle this issue, we propose Prompt
Preference Learning to evaluate the quality of the produced temporal prompts.
By taking such prompts to instruct image-based foundation segmentation models,
we would be able to produce high-quality masks for the referred object,
enabling efficient model adaptation to referring video object segmentation.
Experiments on RVOS benchmarks demonstrate the effectiveness of the Tenet
framework.