Semi-Dense Matching Uncertainty Is Not Just Local Confidence
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2606. 18043v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets.
Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets. Despite their strong empirical performance in robotic manipulation, VLAs lack mechanisms to quantify confidence in their predictions and to detect when their actions may be unreliable.
Flow matching (FM) has become a popular action head paradigm for modern embodied models. However, as a conditional generative model, it does not explicitly expose its inherent uncertainty, producing faulty action chunks even when it misinterprets the scene or encounters out-of-distribution (OOD) inputs.
CALIPER is a model‑free RGB‑D framework that performs fine‑grained recognition of visually similar industrial parts by combining support‑based appearance matching with metric size evidence. Each class is onboarded from a single turntable RGB‑D video and a few labeled real images, enabling 3D reconstruction for appearance support and depth‑aligned size profiling. At inference, a YOLOv8n‑seg model localizes parts, a frozen DINOv2 backbone with an episodically trained embedding head matches support, and margin‑conditioned metric fusion selectively uses size evidence for ambiguous cases, achieving high accuracy on 18 parts and robust enrollment of unseen screws without retraining.
arXiv:2607. 27933v3 Announce Type: replace Abstract: Flow matching (FM) has become a popular action head paradigm for modern embodied models.
arXiv:2605. 00941v4 Announce Type: replace Abstract: Flow matching has become a leading framework for generative modeling, but quantifying the uncertainty of its samples remains an open problem.