Match4Annotate: Propagating Sparse Video Annotations via Implicit Neural Feature Matching

• URL: http://arxiv.org/abs/2603.06471v1
• Date: Fri, 06 Mar 2026 16:56:46 GMT
• Title: Match4Annotate: Propagating Sparse Video Annotations via Implicit Neural Feature Matching
• Abstract summary: We present Match4Annotate, a framework for both intra-video and inter-video propagation of point and mask annotations.<n>Our method fits a SIREN-based implicit representation to DINOv3 features at test time, producing a continuous, high-temporal feature field.<n>We evaluate three challenging clinical ultrasound datasets.
• Score: 0.5459797813771498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Acquiring per-frame video annotations remains a primary bottleneck for deploying computer vision in specialized domains such as medical imaging, where expert labeling is slow and costly. Label propagation offers a natural solution, yet existing approaches face fundamental limitations. Video trackers and segmentation models can propagate labels within a single sequence but require per-video initialization and cannot generalize across videos. Classic correspondence pipelines operate on detector-chosen keypoints and struggle in low-texture scenes, while dense feature matching and one-shot segmentation methods enable cross-video propagation but lack spatiotemporal smoothness and unified support for both point and mask annotations. We present Match4Annotate, a lightweight framework for both intra-video and inter-video propagation of point and mask annotations. Our method fits a SIREN-based implicit neural representation to DINOv3 features at test time, producing a continuous, high-resolution spatiotemporal feature field, and learns a smooth implicit deformation field between frame pairs to guide correspondence matching. We evaluate on three challenging clinical ultrasound datasets. Match4Annotate achieves state-of-the-art inter-video propagation, outperforming feature matching and one-shot segmentation baselines, while remaining competitive with specialized trackers for intra-video propagation. Our results show that lightweight, test-time-optimized feature matching pipelines have the potential to offer an efficient and accessible solution for scalable annotation workflows.

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