Reproduce “DINTR: Tracking via Diffusion-based Interpolation” on OpenResearch
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About this paper
Pha Nguyen, Ngan Le, Jackson Cothren, Alper Yilmaz, Khoa Luu
Researchers at the University of Arkansas and Ohio State University developed DINTR, a new object tracking paradigm that employs diffusion-based interpolation directly within the visual latent space. This approach unifies various tracking tasks, including points, poses, bounding boxes, and segments, while enhancing temporal coherence and achieving competitive performance across multiple benchmarks.
Key insights
- Formulating object tracking as a diffusion-based interpolation directly in the visual latent space enables a unified handling of various object representations and input modalities within a single framework.
- The "offset learning" interpolation operator provides a more stable, efficient, and direct mechanism for modeling temporal correspondences compared to full next-frame reconstruction from noise.
- Object localization for tracking can be effectively derived from the internal self-attention and cross-attention maps of the diffusion U-Net, which implicitly capture spatial correlations related to the target object.
Summary by alphaXiv.
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