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How Many Posterior Samples? Calibrated Stopping for Adaptive Sensing

Paper recorded by Signals 4 on 2026-09-18 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.CV · 计算机视觉 · first seen 2026-09-21

Abstract

In classification-oriented adaptive sensing, posterior samples characterize uncertainty at the current measurement state and can serve two roles: they may guide the next sensing direction, while their class labels provide votes for the candidate classes and determine whether sensing should continue. We focus on the stopping layer that turns these votes into a declaration, without modifying the pos

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#28 most recent of 270 cs.CV papers we have recorded · ↑ newer: Object Detection Benchmarks are Incomplete: The Role of Label Errors a · ↓ older: Classification-oriented adaptive sensing via posterior sampling
Cite this page: How Many Posterior Samples? Calibrated Stopping for Adaptive Sensing: the #28 most recent of 270 cs.CV papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/how-many-posterior-samples-calibrated-stopping-for-adaptive-sensing.html
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