Reproduce “SVAC: Scaling Is All You Need For Referring Video Object Segmentation” 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
Li Zhang, Haoxiang Gao, Zhihao Zhang, Luoxiao Huang, Tao Zhang
SVAC scales up input video frames and segmentation tokens within Multi-modal Large Language Models (MLLMs), achieving state-of-the-art performance in Referring Video Object Segmentation (RVOS) by efficiently processing more temporal context and adapting to dynamic object behaviors.
Key insights
- Scaling up the number of input video frames to MLLMs dramatically boosts RVOS performance, provided it can be done efficiently.
- Efficient spatio-temporal compression, as achieved by ASTC, is crucial for leveraging MLLMs on long videos without prohibitive computational overhead, outperforming simpler pooling or pruning methods.
- Allocating segmentation tokens at a clip-specific granularity (e.g., one token per 10 frames) significantly enhances the model's ability to track and segment objects with complex temporal dynamics.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.