Reproduce “CaptionFormer: Unified Segmentation, Tracking, and Captioning for Spatio-Temporal Objects” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Gabriel Fiastre, Antoine Yang, Cordelia Schmid
Researchers introduce a method for generating synthetic, localized, object-level captions for video data, creating unified datasets LVISCap and LV-VISCap. They also present CaptionFormer, an end-to-end model that unifies segmentation, tracking, and captioning of spatio-temporal objects, achieving state-of-the-art results on several DVOC benchmarks while extending the task to include segmentation masks.
Key insights
- High-quality, localized, object-level captions can be effectively generated at scale using VLMs, addressing the long-standing data scarcity problem for complex spatio-temporal vision-language tasks.
- Unified supervision with object-level captioning data not only improves captioning performance but also enhances underlying video instance segmentation capabilities, suggesting a beneficial interplay between language and visual tasks.
- Temporal aggregation of object queries across multiple video clips significantly improves the accuracy and consistency of natural language descriptions for complete object trajectories.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.