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What Survives Label Downsampling? Exact Region Survival for Coarse Segmentation Targets

Paper recorded by Signals 4 on 2026-10-29 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.CV · 计算机视觉 · first seen 2026-10-30

Abstract

Segmentation networks that predict at an output stride are trained against label maps downsampled by strided sampling, nearest-neighbor resampling or majority pooling. These methods label each coarse grid cell on its own, so a region that wins no cell is absent from the target and receives no positive label. We present MatchDown, which builds the coarse target from a minimum-cost matching between

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#76 most recent of 710 cs.CV papers we have recorded · ↑ newer: TokenRepel: Generating Diverse Image Sets Without Compromising Quality · ↓ older: Parametric Gaussian Noise Mixer (PGNM): Unified Synthesis of Camera No
Cite this page: What Survives Label Downsampling? Exact Region Survival for Coarse Segmentation Targets: the #76 most recent of 710 cs.CV papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/what-survives-label-downsampling-exact-region-survival-for-coarse-segmentation-t.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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